diff --git a/.github/workflows/test-all-warehouses.yml b/.github/workflows/test-all-warehouses.yml
index 3246c679f..7efa247f0 100644
--- a/.github/workflows/test-all-warehouses.yml
+++ b/.github/workflows/test-all-warehouses.yml
@@ -96,6 +96,10 @@ jobs:
             databricks_catalog,
             athena,
             clickhouse,
+            duckdb,
+            trino,
+            dremio,
+            spark,
           ]
     uses: ./.github/workflows/test-warehouse.yml
     with:
diff --git a/.github/workflows/test-warehouse.yml b/.github/workflows/test-warehouse.yml
index 92b16fe04..5ed0ed99b 100644
--- a/.github/workflows/test-warehouse.yml
+++ b/.github/workflows/test-warehouse.yml
@@ -15,6 +15,9 @@ on:
           - spark
           - athena
           - clickhouse
+          - duckdb
+          - trino
+          - dremio
       elementary-ref:
         type: string
         required: false
@@ -83,6 +86,46 @@ jobs:
           path: dbt-data-reliability
           ref: ${{ inputs.dbt-data-reliability-ref }}
 
+      # ── Seed cache: compute key & restore volumes BEFORE starting services ──
+      # This ensures Docker volumes are populated before containers initialize.
+      - name: Compute seed cache key
+        id: seed-cache-key
+        if: inputs.warehouse-type == 'postgres' || inputs.warehouse-type == 'clickhouse' || inputs.warehouse-type == 'duckdb'
+        working-directory: ${{ env.E2E_DBT_PROJECT_DIR }}
+        run: |
+          # Cache key is a hash of seed-related files so that cache busts when
+          # the data generation script, dbt project config, or seed schemas change.
+          SEED_HASH=$(
+            {
+              cat generate_data.py \
+                  dbt_project.yml \
+                  docker-compose.yml \
+                  ${{ github.workspace }}/elementary/tests/profiles/profiles.yml.j2
+              echo "dbt_version=${{ inputs.dbt-version || '' }}"
+            } | sha256sum | head -c 16
+          )
+          echo "seed-hash=$SEED_HASH" >> "$GITHUB_OUTPUT"
+
+      - name: Restore seed cache
+        id: seed-cache
+        if: steps.seed-cache-key.outputs.seed-hash
+        uses: actions/cache@v4
+        with:
+          path: /tmp/seed-cache-${{ inputs.warehouse-type }}
+          key: seed-${{ inputs.warehouse-type }}-${{ steps.seed-cache-key.outputs.seed-hash }}
+
+      - name: Restore cached seed data into Docker volumes
+        if: steps.seed-cache.outputs.cache-hit == 'true' && inputs.warehouse-type != 'duckdb'
+        working-directory: ${{ env.E2E_DBT_PROJECT_DIR }}
+        run: bash ci/restore_seed_cache.sh "${{ inputs.warehouse-type }}"
+
+      - name: Restore cached DuckDB seed
+        if: steps.seed-cache.outputs.cache-hit == 'true' && inputs.warehouse-type == 'duckdb'
+        run: |
+          cp /tmp/seed-cache-duckdb/elementary_test.duckdb /tmp/elementary_test.duckdb
+          echo "DuckDB seed cache restored."
+
+      # ── Start warehouse services ──────────────────────────────────────────
       - name: Start Postgres
         if: inputs.warehouse-type == 'postgres'
         working-directory: ${{ env.E2E_DBT_PROJECT_DIR }}
@@ -93,6 +136,29 @@ jobs:
         working-directory: ${{ env.E2E_DBT_PROJECT_DIR }}
         run: docker compose up -d clickhouse
 
+      - name: Start Trino
+        if: inputs.warehouse-type == 'trino'
+        working-directory: ${{ env.E2E_DBT_PROJECT_DIR }}
+        run: |
+          docker compose up -d --wait trino
+
+      - name: Start Dremio
+        if: inputs.warehouse-type == 'dremio'
+        working-directory: ${{ env.E2E_DBT_PROJECT_DIR }}
+        run: |
+          # Start Dremio services in detached mode with healthchecks, then
+          # run the setup container separately.  Using --exit-code-from would
+          # imply --abort-on-container-exit, killing all services when the
+          # setup container finishes.
+          docker compose up -d --wait dremio dremio-minio nessie
+          docker compose run --rm dremio-setup
+
+      - name: Start Spark
+        if: inputs.warehouse-type == 'spark'
+        working-directory: ${{ env.E2E_DBT_PROJECT_DIR }}
+        run: |
+          docker compose up -d --build --wait spark-thrift
+
       - name: Setup Python
         uses: actions/setup-python@v5
         with:
@@ -100,13 +166,13 @@ jobs:
 
       - name: Install Spark requirements
         if: inputs.warehouse-type == 'spark'
-        run: sudo apt-get install python-dev libsasl2-dev gcc
+        run: sudo apt-get install -y python3-dev libsasl2-dev gcc
 
       - name: Install dbt
         run: >
           pip install
           "dbt-core${{ inputs.dbt-version && format('=={0}', inputs.dbt-version) }}"
-          "dbt-${{ (inputs.warehouse-type == 'databricks_catalog' && 'databricks') || (inputs.warehouse-type == 'athena' && 'athena-community') || inputs.warehouse-type }}${{ inputs.dbt-version && format('~={0}', inputs.dbt-version) }}"
+          "dbt-${{ (inputs.warehouse-type == 'databricks_catalog' && 'databricks') || (inputs.warehouse-type == 'athena' && 'athena-community') || (inputs.warehouse-type == 'dremio' && 'dremio') || inputs.warehouse-type }}${{ (inputs.warehouse-type == 'spark' && '[PyHive]') || '' }}${{ inputs.dbt-version && format('~={0}', inputs.dbt-version) }}"
 
       - name: Install Elementary
         run: |
@@ -117,21 +183,29 @@ jobs:
         env:
           CI_WAREHOUSE_SECRETS: ${{ secrets.CI_WAREHOUSE_SECRETS || '' }}
         run: |
-          # Schema name = py_<YYMMDD_HHMMSS>_<branch≤19>_<8-char hash>
-          # The hash prevents collisions across concurrent jobs; the branch
-          # keeps it human-readable; the timestamp helps with stale schema
-          # cleanup and ensures each CI run gets a unique schema.
-          #
-          # Budget (PostgreSQL 63-char limit):
-          #   py_(3) + timestamp(13) + _(1) + branch(≤19) + _(1) + hash(8) = 45
-          #   + _elementary(11) + _gw7(4) = 60
-          CONCURRENCY_GROUP="tests_${{ inputs.warehouse-type }}_dbt_${{ inputs.dbt-version }}_${BRANCH_NAME}"
-          SHORT_HASH=$(echo -n "$CONCURRENCY_GROUP" | sha256sum | head -c 8)
-          SAFE_BRANCH=$(echo "${BRANCH_NAME}" | awk '{print tolower($0)}' | sed "s/[^a-z0-9]/_/g; s/__*/_/g" | head -c 19)
-          DATE_STAMP=$(date -u +%y%m%d_%H%M%S)
-          SCHEMA_NAME="py_${DATE_STAMP}_${SAFE_BRANCH}_${SHORT_HASH}"
-
-          echo "Schema name: $SCHEMA_NAME (branch='${BRANCH_NAME}', timestamp=${DATE_STAMP}, hash of concurrency group)"
+          # Docker-based adapters use ephemeral containers, so a fixed schema
+          # name is safe (the concurrency group prevents parallel collisions).
+          # This enables caching the seeded database state between runs.
+          IS_DOCKER=false
+          case "${{ inputs.warehouse-type }}" in
+            postgres|clickhouse|trino|dremio|duckdb|spark) IS_DOCKER=true ;;
+          esac
+
+          if [ "$IS_DOCKER" = "true" ]; then
+            SCHEMA_NAME="elementary_tests"
+            echo "Schema name: $SCHEMA_NAME (fixed for Docker adapter '${{ inputs.warehouse-type }}')"
+          else
+            # Cloud adapters: unique schema per run to avoid collisions.
+            # Schema name = py_<YYMMDD_HHMMSS>_<branch≤19>_<8-char hash>
+            CONCURRENCY_GROUP="tests_${{ inputs.warehouse-type }}_dbt_${{ inputs.dbt-version }}_${BRANCH_NAME}"
+            SHORT_HASH=$(echo -n "$CONCURRENCY_GROUP" | sha256sum | head -c 8)
+            SAFE_BRANCH=$(echo "${BRANCH_NAME}" | awk '{print tolower($0)}' | sed "s/[^a-z0-9]/_/g; s/__*/_/g" | head -c 19)
+            DATE_STAMP=$(date -u +%y%m%d_%H%M%S)
+            SCHEMA_NAME="py_${DATE_STAMP}_${SAFE_BRANCH}_${SHORT_HASH}"
+            echo "Schema name: $SCHEMA_NAME (branch='${BRANCH_NAME}', timestamp=${DATE_STAMP}, hash of concurrency group)"
+          fi
+
+          echo "SCHEMA_NAME=$SCHEMA_NAME" >> "$GITHUB_ENV"
 
           python "${{ github.workspace }}/elementary/tests/profiles/generate_profiles.py" \
             --template "${{ github.workspace }}/elementary/tests/profiles/profiles.yml.j2" \
@@ -160,17 +234,42 @@ jobs:
         run: |
           dbt deps
 
+      - name: Generate seed data
+        working-directory: ${{ env.E2E_DBT_PROJECT_DIR }}
+        if: steps.seed-cache.outputs.cache-hit != 'true'
+        run: python generate_data.py
+
+      - name: Seed e2e dbt project (external)
+        working-directory: ${{ env.E2E_DBT_PROJECT_DIR }}
+        if: steps.seed-cache.outputs.cache-hit != 'true' && (inputs.warehouse-type == 'dremio' || inputs.warehouse-type == 'spark')
+        run: python load_seeds_external.py "${{ inputs.warehouse-type }}" "$SCHEMA_NAME" data
+
       - name: Seed e2e dbt project
         working-directory: ${{ env.E2E_DBT_PROJECT_DIR }}
-        if: inputs.warehouse-type == 'postgres' || inputs.warehouse-type == 'clickhouse' || inputs.generate-data
+        if: steps.seed-cache.outputs.cache-hit != 'true' && inputs.warehouse-type != 'dremio' && inputs.warehouse-type != 'spark'
+        run: dbt seed -f --target "${{ inputs.warehouse-type }}"
+
+      - name: Save seed cache from Docker volumes
+        if: steps.seed-cache.outputs.cache-hit != 'true' && (inputs.warehouse-type == 'postgres' || inputs.warehouse-type == 'clickhouse')
+        working-directory: ${{ env.E2E_DBT_PROJECT_DIR }}
+        run: bash ci/save_seed_cache.sh "${{ inputs.warehouse-type }}"
+
+      - name: Save DuckDB seed cache
+        if: steps.seed-cache.outputs.cache-hit != 'true' && inputs.warehouse-type == 'duckdb'
         run: |
-          python generate_data.py
-          dbt seed -f --target "${{ inputs.warehouse-type }}"
+          mkdir -p /tmp/seed-cache-duckdb
+          cp /tmp/elementary_test.duckdb /tmp/seed-cache-duckdb/elementary_test.duckdb
+          echo "DuckDB seed cache saved."
 
       - name: Run e2e dbt project
         working-directory: ${{ env.E2E_DBT_PROJECT_DIR }}
         run: |
-          dbt run --target "${{ inputs.warehouse-type }}" || true
+          # Dremio needs single-threaded execution to avoid Nessie catalog race conditions
+          EXTRA_ARGS=()
+          if [ "${{ inputs.warehouse-type }}" = "dremio" ]; then
+            EXTRA_ARGS+=(--threads 1)
+          fi
+          dbt run --target "${{ inputs.warehouse-type }}" "${EXTRA_ARGS[@]}" || true
 
           # Validate run_results.json: only error_model should be non-success
           jq -e '
@@ -192,7 +291,12 @@ jobs:
         working-directory: ${{ env.E2E_DBT_PROJECT_DIR }}
         continue-on-error: true
         run: |
-          dbt test --target "${{ inputs.warehouse-type }}"
+          # Dremio needs single-threaded execution to avoid Nessie catalog race conditions
+          EXTRA_ARGS=()
+          if [ "${{ inputs.warehouse-type }}" = "dremio" ]; then
+            EXTRA_ARGS+=(--threads 1 --exclude tag:ephemeral_model)
+          fi
+          dbt test --target "${{ inputs.warehouse-type }}" "${EXTRA_ARGS[@]}"
 
       - name: Run help
         run: edr --help
diff --git a/elementary/clients/dbt/transient_errors.py b/elementary/clients/dbt/transient_errors.py
index 7ea625b90..14882ab5e 100644
--- a/elementary/clients/dbt/transient_errors.py
+++ b/elementary/clients/dbt/transient_errors.py
@@ -100,6 +100,15 @@
         "connection timed out",
         "broken pipe",
     ),
+    "spark": (
+        "thrift transport is closed",
+        "could not connect to any thrift server",
+        "connection refused",
+    ),
+    "duckdb": (
+        # DuckDB runs in-process; transient errors are rare.
+        # Common patterns (connection reset, broken pipe) are in _COMMON.
+    ),
 }
 
 # Pre-computed union of all adapter-specific patterns for the fallback path
diff --git a/elementary/monitor/dbt_project/dbt_project.yml b/elementary/monitor/dbt_project/dbt_project.yml
index ce4e3dbe0..8df5fb9ba 100644
--- a/elementary/monitor/dbt_project/dbt_project.yml
+++ b/elementary/monitor/dbt_project/dbt_project.yml
@@ -28,9 +28,17 @@ clean-targets: # directories to be removed by `dbt clean`
 
 # Configuring models
 # Full documentation: https://docs.getdbt.com/docs/configuring-models
+dispatch:
+  - macro_namespace: elementary
+    search_order: ["elementary_cli", "elementary"]
+
 vars:
   edr_cli_run: true
 
+models:
+  elementary_cli:
+    +file_format: "{{ 'delta' if target.type == 'spark' else none }}"
+
 quoting:
   database: "{{ env_var('DATABASE_QUOTING', 'None') | as_native }}"
   schema: "{{ env_var('SCHEMA_QUOTING', 'None')  | as_native }}"
diff --git a/elementary/monitor/dbt_project/macros/alerts/population/model_alerts.sql b/elementary/monitor/dbt_project/macros/alerts/population/model_alerts.sql
index 882479710..b6a371382 100644
--- a/elementary/monitor/dbt_project/macros/alerts/population/model_alerts.sql
+++ b/elementary/monitor/dbt_project/macros/alerts/population/model_alerts.sql
@@ -52,7 +52,7 @@
         select * from {{ ref('elementary', 'dbt_models') }}
     ),
 
-    snapshots as (
+    snapshots_data as (
         select * from {{ ref('elementary', 'dbt_snapshots') }}
     ),
 
@@ -71,7 +71,7 @@
     artifacts_meta as (
         select unique_id, meta from models
         union all
-        select unique_id, meta from snapshots
+        select unique_id, meta from snapshots_data
         union all
         select unique_id, meta from seeds
     ),
diff --git a/elementary/monitor/dbt_project/macros/alerts/population/source_freshness_alerts.sql b/elementary/monitor/dbt_project/macros/alerts/population/source_freshness_alerts.sql
index 8016d146a..ac7601a6e 100644
--- a/elementary/monitor/dbt_project/macros/alerts/population/source_freshness_alerts.sql
+++ b/elementary/monitor/dbt_project/macros/alerts/population/source_freshness_alerts.sql
@@ -97,7 +97,7 @@
       {% if error_after_column_exists %}
         results.error_after,
         results.warn_after,
-        results.filter,
+        results.{{ elementary.escape_reserved_keywords('filter') }},
       {% endif %}
       results.error,
       sources.database_name,
diff --git a/elementary/monitor/dbt_project/macros/can_upload_source_freshness.sql b/elementary/monitor/dbt_project/macros/can_upload_source_freshness.sql
index d09937875..082ba0ec4 100644
--- a/elementary/monitor/dbt_project/macros/can_upload_source_freshness.sql
+++ b/elementary/monitor/dbt_project/macros/can_upload_source_freshness.sql
@@ -2,10 +2,10 @@
     {% set counter_query %}
         with invocations as (
             select invocation_id
-            from {{ ref("elementary", "dbt_source_freshness_results") }}
+            from {{ ref("dbt_source_freshness_results", package="elementary") }}
             where {{ elementary.edr_datediff(elementary.edr_cast_as_timestamp('generated_at'), elementary.edr_current_timestamp(), 'day') }} < {{ days_back }}
         )
-        select count(*) as count
+        select count(*) as {{ elementary.escape_reserved_keywords('count') }}
         from invocations
         where invocation_id = {{ elementary.edr_quote(invocation_id) }}
     {% endset %}
diff --git a/elementary/monitor/dbt_project/macros/get_adapter_type_and_unique_id.sql b/elementary/monitor/dbt_project/macros/get_adapter_type_and_unique_id.sql
index 0c954802d..2a021d3b5 100644
--- a/elementary/monitor/dbt_project/macros/get_adapter_type_and_unique_id.sql
+++ b/elementary/monitor/dbt_project/macros/get_adapter_type_and_unique_id.sql
@@ -17,3 +17,7 @@
 {% macro athena__get_adapter_unique_id() %}
     {{ return(target.s3_staging_dir) }}
 {% endmacro %}
+
+{% macro duckdb__get_adapter_unique_id() %}
+    {{ return(target.path) }}
+{% endmacro %}
diff --git a/elementary/monitor/dbt_project/macros/get_models_latest_invocation.sql b/elementary/monitor/dbt_project/macros/get_models_latest_invocation.sql
index 5dde01929..17829d663 100644
--- a/elementary/monitor/dbt_project/macros/get_models_latest_invocation.sql
+++ b/elementary/monitor/dbt_project/macros/get_models_latest_invocation.sql
@@ -2,16 +2,17 @@
   {% set query %}
     with ordered_run_results as (
       select
-        *,
-        row_number() over (partition by unique_id order by run_results.generated_at desc) as row_number
-      from {{ ref("elementary", "dbt_run_results") }} run_results
-      join {{ ref("elementary", "dbt_models") }} using (unique_id)
+        run_results.unique_id,
+        run_results.invocation_id,
+        row_number() over (partition by run_results.unique_id order by run_results.generated_at desc) as {{ elementary.escape_reserved_keywords('row_number') }}
+      from {{ ref("dbt_run_results", package="elementary") }} run_results
+      join {{ ref("dbt_models", package="elementary") }} models on run_results.unique_id = models.unique_id
     ),
 
     latest_run_results as (
-      select *
+      select unique_id, invocation_id
       from ordered_run_results
-      where row_number = 1
+      where {{ elementary.escape_reserved_keywords('row_number') }} = 1
     )
 
     select unique_id, invocation_id from latest_run_results
diff --git a/elementary/monitor/dbt_project/macros/get_models_latest_invocations_data.sql b/elementary/monitor/dbt_project/macros/get_models_latest_invocations_data.sql
index 374128b12..783df3293 100644
--- a/elementary/monitor/dbt_project/macros/get_models_latest_invocations_data.sql
+++ b/elementary/monitor/dbt_project/macros/get_models_latest_invocations_data.sql
@@ -1,35 +1,35 @@
 {% macro get_models_latest_invocations_data() %}
-  {% set invocations_relation = ref("elementary", "dbt_invocations") %}
+  {% set invocations_relation = ref("dbt_invocations", package="elementary") %}
   {% set column_exists = elementary.column_exists_in_relation(invocations_relation, 'job_url') %}
 
   {% set query %}
     with ordered_run_results as (
       select
-        *,
-        row_number() over (partition by unique_id order by run_results.generated_at desc) as row_number
-      from {{ ref("elementary", "dbt_run_results") }} run_results
-      join {{ ref("elementary", "dbt_models") }} using (unique_id)
+        run_results.invocation_id,
+        row_number() over (partition by run_results.unique_id order by run_results.generated_at desc) as {{ elementary.escape_reserved_keywords('row_number') }}
+      from {{ ref("dbt_run_results", package="elementary") }} run_results
+      join {{ ref("dbt_models", package="elementary") }} models on run_results.unique_id = models.unique_id
     ),
 
     latest_models_invocations as (
       select distinct invocation_id
       from ordered_run_results
-      where row_number = 1
+      where {{ elementary.escape_reserved_keywords('row_number') }} = 1
     )
 
     select
-      invocation_id,
-      command,
-      selected,
-      full_refresh,
+      invocations.invocation_id,
+      invocations.command,
+      invocations.selected,
+      invocations.full_refresh,
       {% if column_exists %}
-        job_url,
+        invocations.job_url,
       {% endif %}
-      job_name,
-      job_id,
-      orchestrator
+      invocations.job_name,
+      invocations.job_id,
+      invocations.orchestrator
     from {{ invocations_relation }} invocations
-    join latest_models_invocations using (invocation_id)
+    join latest_models_invocations on invocations.invocation_id = latest_models_invocations.invocation_id
   {% endset %}
   {% set result = elementary.run_query(query) %}
   {% do return(elementary.agate_to_dicts(result)) %}
diff --git a/elementary/monitor/dbt_project/macros/get_result_rows_agate.sql b/elementary/monitor/dbt_project/macros/get_result_rows_agate.sql
index f8a4f1257..b56b42126 100644
--- a/elementary/monitor/dbt_project/macros/get_result_rows_agate.sql
+++ b/elementary/monitor/dbt_project/macros/get_result_rows_agate.sql
@@ -7,7 +7,7 @@
   select
     elementary_test_results_id,
     result_row
-  from {{ ref("elementary", "test_result_rows") }}
+  from {{ ref("test_result_rows", package="elementary") }}
   where {{ elementary.edr_datediff(elementary.edr_cast_as_timestamp('detected_at'), elementary.edr_current_timestamp(), 'day') }} < {{ days_back }}
   {% if valid_ids_query %}
     and elementary_test_results_id in ({{ valid_ids_query }})
@@ -25,7 +25,7 @@
   select
     elementary_test_results_id,
     result_row
-  from {{ ref("elementary", "test_result_rows") }}
+  from {{ ref("test_result_rows", package="elementary") }}
   where detected_at > {{ elementary.edr_timeadd('day', -1 * days_back, elementary.edr_current_timestamp()) }}
   {% if valid_ids_query %}
     and elementary_test_results_id in ({{ valid_ids_query }})
diff --git a/elementary/monitor/dbt_project/macros/get_source_freshness_results.sql b/elementary/monitor/dbt_project/macros/get_source_freshness_results.sql
index e6224f984..8160e6f26 100644
--- a/elementary/monitor/dbt_project/macros/get_source_freshness_results.sql
+++ b/elementary/monitor/dbt_project/macros/get_source_freshness_results.sql
@@ -33,7 +33,7 @@
             {% if error_after_column_exists %}
                 freshness.error_after,
                 freshness.warn_after,
-                freshness.filter,
+                freshness.{{ elementary.escape_reserved_keywords('filter') }},
             {% endif %}
             freshness.max_loaded_at_time_ago_in_s,
             freshness.snapshotted_at,
diff --git a/elementary/monitor/dbt_project/models/alerts/alerts_v2.sql b/elementary/monitor/dbt_project/models/alerts/alerts_v2.sql
index 860a1d332..03ba0acb0 100644
--- a/elementary/monitor/dbt_project/models/alerts/alerts_v2.sql
+++ b/elementary/monitor/dbt_project/models/alerts/alerts_v2.sql
@@ -12,20 +12,20 @@
 
 -- depends_on: {{ ref('dbt_tests') }}
 -- depends_on: {{ ref('elementary_test_results') }}
--- depends_on: {{ ref('elementary', 'test_result_rows') }}
+-- depends_on: {{ ref('test_result_rows', package='elementary') }}
 
 -- depends_on: {{ ref('dbt_models') }}
--- depends_on: {{ ref('elementary', 'dbt_snapshots') }}
+-- depends_on: {{ ref('dbt_snapshots', package='elementary') }}
 -- depends_on: {{ ref('model_run_results') }}
 -- depends_on: {{ ref('snapshot_run_results') }}
 
 -- depends_on: {{ ref('dbt_sources') }}
--- depends_on: {{ ref('elementary', 'dbt_source_freshness_results') }}
+-- depends_on: {{ ref('dbt_source_freshness_results', package='elementary') }}
 
 -- depends_on: {{ ref('dbt_seeds') }}
 
--- depends_on: {{ ref('elementary', 'dbt_invocations') }}
--- depends_on: {{ ref('elementary', 'dbt_run_results') }}
+-- depends_on: {{ ref('dbt_invocations', package='elementary') }}
+-- depends_on: {{ ref('dbt_run_results', package='elementary') }}
 
 -- backwards compatibility
 -- depends_on: {{ ref('elementary_cli', 'alerts') }}
diff --git a/elementary/monitor/dbt_project/models/alerts/deprecated/alerts.sql b/elementary/monitor/dbt_project/models/alerts/deprecated/alerts.sql
index 400203669..2e3167ab5 100644
--- a/elementary/monitor/dbt_project/models/alerts/deprecated/alerts.sql
+++ b/elementary/monitor/dbt_project/models/alerts/deprecated/alerts.sql
@@ -15,7 +15,7 @@
 {% set schema_changes_relation = adapter.get_relation(this.database, this.schema, 'alerts_schema_changes') %}
 
 with failed_tests as (
-    select * from {{ ref('elementary', 'alerts_dbt_tests') }}
+    select * from {{ ref('alerts_dbt_tests', package='elementary') }}
 
     {% if schema_changes_relation %}
         union all
@@ -32,7 +32,7 @@ with failed_tests as (
 )
 
 select
-    {{ dbt_utils.star(from=ref('elementary', 'alerts_dbt_tests'), except=["detected_at"]) }},
+    {{ dbt_utils.star(from=ref('alerts_dbt_tests', package='elementary'), except=["detected_at"]) }},
     {{ elementary_cli.get_alerts_model_detected_at_expr(this) }} as detected_at,
     false as alert_sent,  {# backwards compatibility #}
     'pending' as suppression_status,
diff --git a/pyproject.toml b/pyproject.toml
index ff7b1fc91..6043c7b10 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -54,6 +54,8 @@ dbt-spark = {version = ">=0.20,<2.0.0", optional = true}
 dbt-athena-community = {version = ">=1.6.3,<2.0.0", optional = true}
 dbt-trino = {version = ">=1.5.0,<2.0.0", optional = true}
 dbt-clickhouse = {version = ">=0.20,<2.0.0", optional = true}
+dbt-duckdb = {version = ">=1.5.0,<2.0.0", optional = true}
+dbt-dremio = {version = ">=1.5.0,<2.0.0", optional = true}
 [tool.poetry.extras]
 snowflake = ["dbt-snowflake"]
 bigquery = ["dbt-bigquery"]
@@ -64,7 +66,9 @@ spark = ["dbt-spark"]
 athena = ["dbt-athena-community"]
 clickhouse = ["dbt-clickhouse"]
 trino = ["dbt-trino"]
-all = ["dbt-snowflake", "dbt-bigquery", "dbt-redshift", "dbt-postgres", "dbt-databricks", "dbt-spark", "dbt-clickhouse", "dbt-athena-community", "dbt-trino"]
+duckdb = ["dbt-duckdb"]
+dremio = ["dbt-dremio"]
+all = ["dbt-snowflake", "dbt-bigquery", "dbt-redshift", "dbt-postgres", "dbt-databricks", "dbt-spark", "dbt-clickhouse", "dbt-athena-community", "dbt-trino", "dbt-duckdb", "dbt-dremio"]
 
 [build-system]
 requires = ["poetry-core>=1.0.0"]
diff --git a/tests/e2e/report/fixtures/elementary_output.json b/tests/e2e/report/fixtures/elementary_output.json
index fb15959af..a2b525fef 100644
--- a/tests/e2e/report/fixtures/elementary_output.json
+++ b/tests/e2e/report/fixtures/elementary_output.json
@@ -3551,7 +3551,7 @@
       "database_name": "postgres",
       "schema_name": "edr",
       "table_name": null,
-      "column_name": "max",
+      "column_name": "max_val",
       "name": "elementary_column_anomalies_numeric_column_anomalies_drop__average__max",
       "display_name": "Elementary Column Anomalies Numeric Column Anomalies Drop  Average  Max",
       "original_path": "models/schema.yml",
@@ -3561,7 +3561,7 @@
       "test_params": {
         "column_anomalies": ["average"],
         "anomaly_direction": "drop",
-        "column_name": "max",
+        "column_name": "max_val",
         "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"
       },
       "description": "Column-level anomaly monitors (null_count, null_percent, zero_count, string_length, variance, etc.) on the column according to its data type.",
@@ -3585,7 +3585,7 @@
       "database_name": "postgres",
       "schema_name": "edr",
       "table_name": null,
-      "column_name": "max",
+      "column_name": "max_val",
       "name": "elementary_column_anomalies_numeric_column_anomalies_spike__average__max",
       "display_name": "Elementary Column Anomalies Numeric Column Anomalies Spike  Average  Max",
       "original_path": "models/schema.yml",
@@ -3595,7 +3595,7 @@
       "test_params": {
         "column_anomalies": ["average"],
         "anomaly_direction": "spike",
-        "column_name": "max",
+        "column_name": "max_val",
         "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"
       },
       "description": "Column-level anomaly monitors (null_count, null_percent, zero_count, string_length, variance, etc.) on the column according to its data type.",
@@ -3814,7 +3814,7 @@
       "database_name": "postgres",
       "schema_name": "edr",
       "table_name": null,
-      "column_name": "min",
+      "column_name": "min_val",
       "name": "elementary_column_anomalies_numeric_column_anomalies_min__min",
       "display_name": "Elementary Column Anomalies Numeric Column Anomalies Min  Min",
       "original_path": "models/schema.yml",
@@ -3823,7 +3823,7 @@
       "test_sub_type": "min",
       "test_params": {
         "column_anomalies": ["min"],
-        "column_name": "min",
+        "column_name": "min_val",
         "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"
       },
       "description": "Column-level anomaly monitors (null_count, null_percent, zero_count, string_length, variance, etc.) on the column according to its data type.",
@@ -3924,7 +3924,7 @@
       "database_name": "postgres",
       "schema_name": "edr",
       "table_name": null,
-      "column_name": "max",
+      "column_name": "max_val",
       "name": "elementary_column_anomalies_numeric_column_anomalies_average__max",
       "display_name": "Elementary Column Anomalies Numeric Column Anomalies Average  Max",
       "original_path": "models/schema.yml",
@@ -3933,7 +3933,7 @@
       "test_sub_type": "average",
       "test_params": {
         "column_anomalies": ["average"],
-        "column_name": "max",
+        "column_name": "max_val",
         "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"
       },
       "description": "Column-level anomaly monitors (null_count, null_percent, zero_count, string_length, variance, etc.) on the column according to its data type.",
@@ -4034,7 +4034,7 @@
       "database_name": "postgres",
       "schema_name": "edr",
       "table_name": null,
-      "column_name": "sum",
+      "column_name": "sum_val",
       "name": "elementary_column_anomalies_numeric_column_anomalies_sum__sum",
       "display_name": "Elementary Column Anomalies Numeric Column Anomalies Sum  Sum",
       "original_path": "models/schema.yml",
@@ -4043,7 +4043,7 @@
       "test_sub_type": "sum",
       "test_params": {
         "column_anomalies": ["sum"],
-        "column_name": "sum",
+        "column_name": "sum_val",
         "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"
       },
       "description": "Column-level anomaly monitors (null_count, null_percent, zero_count, string_length, variance, etc.) on the column according to its data type.",
@@ -4071,7 +4071,7 @@
       "database_name": "postgres",
       "schema_name": "edr",
       "table_name": null,
-      "column_name": "max",
+      "column_name": "max_val",
       "name": "elementary_column_anomalies_numeric_column_anomalies_min__max",
       "display_name": "Elementary Column Anomalies Numeric Column Anomalies Min  Max",
       "original_path": "models/schema.yml",
@@ -4080,7 +4080,7 @@
       "test_sub_type": "min",
       "test_params": {
         "column_anomalies": ["min"],
-        "column_name": "max",
+        "column_name": "max_val",
         "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"
       },
       "description": "Column-level anomaly monitors (null_count, null_percent, zero_count, string_length, variance, etc.) on the column according to its data type.",
@@ -4182,7 +4182,7 @@
       "database_name": "postgres",
       "schema_name": "edr",
       "table_name": null,
-      "column_name": "min",
+      "column_name": "min_val",
       "name": "elementary_column_anomalies_numeric_column_anomalies_average__min",
       "display_name": "Elementary Column Anomalies Numeric Column Anomalies Average  Min",
       "original_path": "models/schema.yml",
@@ -4191,7 +4191,7 @@
       "test_sub_type": "average",
       "test_params": {
         "column_anomalies": ["average"],
-        "column_name": "min",
+        "column_name": "min_val",
         "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"
       },
       "description": "Column-level anomaly monitors (null_count, null_percent, zero_count, string_length, variance, etc.) on the column according to its data type.",
@@ -4441,7 +4441,7 @@
       "database_name": "postgres",
       "schema_name": "edr",
       "table_name": null,
-      "column_name": "min",
+      "column_name": "min_val",
       "name": "elementary_column_anomalies_numeric_column_anomalies_max__min",
       "display_name": "Elementary Column Anomalies Numeric Column Anomalies Max  Min",
       "original_path": "models/schema.yml",
@@ -4450,7 +4450,7 @@
       "test_sub_type": "max",
       "test_params": {
         "column_anomalies": ["max"],
-        "column_name": "min",
+        "column_name": "min_val",
         "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"
       },
       "description": "Column-level anomaly monitors (null_count, null_percent, zero_count, string_length, variance, etc.) on the column according to its data type.",
@@ -4514,7 +4514,7 @@
       "database_name": "postgres",
       "schema_name": "edr",
       "table_name": null,
-      "column_name": "max",
+      "column_name": "max_val",
       "name": "elementary_column_anomalies_numeric_column_anomalies_max__max",
       "display_name": "Elementary Column Anomalies Numeric Column Anomalies Max  Max",
       "original_path": "models/schema.yml",
@@ -4523,7 +4523,7 @@
       "test_sub_type": "max",
       "test_params": {
         "column_anomalies": ["max"],
-        "column_name": "max",
+        "column_name": "max_val",
         "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"
       },
       "description": "Column-level anomaly monitors (null_count, null_percent, zero_count, string_length, variance, etc.) on the column according to its data type.",
@@ -5755,7 +5755,7 @@
       "database_name": "postgres",
       "schema_name": "edr",
       "table_name": null,
-      "column_name": "one",
+      "column_name": "col1",
       "name": "accepted_values_one_one__2__3",
       "display_name": "Accepted Values One One  2  3",
       "original_path": "models/schema.yml",
@@ -5764,7 +5764,7 @@
       "test_sub_type": null,
       "test_params": {
         "values": [2, 3],
-        "column_name": "one",
+        "column_name": "col1",
         "model": "{{ get_where_subquery(ref('one')) }}"
       },
       "description": "This test validates that all of the values in a column are present in a supplied list of `values`. If any values other than those provided in the list are present, then the test will fail.",
@@ -5834,13 +5834,13 @@
           "table_unique_id": "postgres.test_seeds.numeric_column_anomalies_training",
           "test_type": "dbt_test",
           "test_sub_type": "singular",
-          "test_query": "select min from \"postgres\".\"test_seeds\".\"numeric_column_anomalies_training\" where min < 105",
+          "test_query": "select min_val from \"postgres\".\"test_seeds\".\"numeric_column_anomalies_training\" where min_val < 105",
           "test_params": {},
           "test_created_at": null,
           "description": null,
           "result": {
             "result_description": "Got 279 results, configured to fail if != 0",
-            "result_query": "select min from \"postgres\".\"test_seeds\".\"numeric_column_anomalies_training\" where min < 105"
+            "result_query": "select min_val from \"postgres\".\"test_seeds\".\"numeric_column_anomalies_training\" where min_val < 105"
           },
           "configuration": {
             "test_name": "singular_test_with_source_ref",
@@ -217733,13 +217733,13 @@
           "table_unique_id": "postgres.test_seeds.numeric_column_anomalies_training",
           "test_type": "dbt_test",
           "test_sub_type": "singular",
-          "test_query": "select min from \"postgres\".\"test_seeds\".\"numeric_column_anomalies_training\" where min < 105",
+          "test_query": "select min_val from \"postgres\".\"test_seeds\".\"numeric_column_anomalies_training\" where min_val < 105",
           "test_params": {},
           "test_created_at": null,
           "description": null,
           "result": {
             "result_description": "Got 279 results, configured to fail if != 0",
-            "result_query": "select min from \"postgres\".\"test_seeds\".\"numeric_column_anomalies_training\" where min < 105"
+            "result_query": "select min_val from \"postgres\".\"test_seeds\".\"numeric_column_anomalies_training\" where min_val < 105"
           },
           "configuration": {
             "test_name": "singular_test_with_source_ref",
@@ -217781,13 +217781,13 @@
           "table_unique_id": "postgres.edr.numeric_column_anomalies",
           "test_type": "dbt_test",
           "test_sub_type": "singular",
-          "test_query": "select min from \"postgres\".\"edr\".\"numeric_column_anomalies\" where min < 100",
+          "test_query": "select min_val from \"postgres\".\"edr\".\"numeric_column_anomalies\" where min_val < 100",
           "test_params": {},
           "test_created_at": null,
           "description": null,
           "result": {
             "result_description": "Got 90 results, configured to fail if != 0",
-            "result_query": "select min from \"postgres\".\"edr\".\"numeric_column_anomalies\" where min < 100"
+            "result_query": "select min_val from \"postgres\".\"edr\".\"numeric_column_anomalies\" where min_val < 100"
           },
           "configuration": {
             "test_name": "singular_test_with_one_ref",
@@ -267842,13 +267842,13 @@
           "table_unique_id": "",
           "test_type": "dbt_test",
           "test_sub_type": "singular",
-          "test_query": "select min from \"postgres\".\"edr\".\"numeric_column_anomalies\" where min < 100",
+          "test_query": "select min_val from \"postgres\".\"edr\".\"numeric_column_anomalies\" where min_val < 100",
           "test_params": {},
           "test_created_at": null,
           "description": null,
           "result": {
             "result_description": "Got 90 results, configured to fail if != 0",
-            "result_query": "select min from \"postgres\".\"edr\".\"numeric_column_anomalies\" where min < 100"
+            "result_query": "select min_val from \"postgres\".\"edr\".\"numeric_column_anomalies\" where min_val < 100"
           },
           "configuration": {
             "test_name": "singular_test_with_no_ref",
@@ -269564,7 +269564,7 @@
           "database_name": "postgres",
           "schema_name": "edr",
           "table_name": "numeric_column_anomalies",
-          "column_name": "max",
+          "column_name": "max_val",
           "test_name": "column_anomalies",
           "test_display_name": "Column Anomalies",
           "original_path": "models/schema.yml",
@@ -269578,7 +269578,7 @@
           "test_query": "select * from (None) results\n      where\n        anomaly_score is not null and\n        upper(full_table_name) = upper(cast('POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES' as varchar(4096))) and\n        metric_name = cast('average' as varchar(4096))\n          and upper(column_name) = upper(cast('max' as varchar(4096)))",
           "test_params": {
             "column_anomalies": ["average"],
-            "column_name": "max",
+            "column_name": "max_val",
             "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}",
             "timestamp_column": "updated_at",
             "where_expression": null,
@@ -269635,7 +269635,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb",
               "detected_at": "2024-09-17T08:37:18.203263",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": -0.7071067811865475,
               "anomaly_score_threshold": 3,
@@ -269670,7 +269670,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb",
               "detected_at": "2024-09-17T08:37:18.203263",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.8887540382763068,
               "anomaly_score_threshold": 3,
@@ -269705,7 +269705,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb",
               "detected_at": "2024-09-17T08:37:18.203263",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.9281437914045992,
               "anomaly_score_threshold": 3,
@@ -269740,7 +269740,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb",
               "detected_at": "2024-09-17T08:37:18.203263",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": -0.09599461851321081,
               "anomaly_score_threshold": 3,
@@ -269775,7 +269775,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb",
               "detected_at": "2024-09-17T08:37:18.203263",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": -0.6608047556580349,
               "anomaly_score_threshold": 3,
@@ -269810,7 +269810,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb",
               "detected_at": "2024-09-17T08:37:18.203263",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.6512559888531815,
               "anomaly_score_threshold": 3,
@@ -269845,7 +269845,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb",
               "detected_at": "2024-09-17T08:37:18.203263",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.6308040368337668,
               "anomaly_score_threshold": 3,
@@ -269880,7 +269880,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb",
               "detected_at": "2024-09-17T08:37:18.203263",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": -0.7755718777927628,
               "anomaly_score_threshold": 3,
@@ -269915,7 +269915,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb",
               "detected_at": "2024-09-17T08:37:18.203263",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 1.1030267814383428,
               "anomaly_score_threshold": 3,
@@ -269950,7 +269950,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb",
               "detected_at": "2024-09-17T08:37:18.203263",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.1679559075120216,
               "anomaly_score_threshold": 3,
@@ -269985,7 +269985,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb",
               "detected_at": "2024-09-17T08:37:18.203263",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": -0.3063228011378462,
               "anomaly_score_threshold": 3,
@@ -270020,7 +270020,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb",
               "detected_at": "2024-09-17T08:37:18.203263",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.21461211646590303,
               "anomaly_score_threshold": 3,
@@ -270055,7 +270055,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb",
               "detected_at": "2024-09-17T08:37:18.203263",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.31131709492105425,
               "anomaly_score_threshold": 3,
@@ -270090,7 +270090,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb",
               "detected_at": "2024-09-17T08:37:18.203263",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.251500992139858,
               "anomaly_score_threshold": 3,
@@ -270125,7 +270125,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb",
               "detected_at": "2024-09-17T08:37:18.203263",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.42210816153333885,
               "anomaly_score_threshold": 3,
@@ -270160,7 +270160,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb",
               "detected_at": "2024-09-17T08:37:18.203263",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": -0.605017605043944,
               "anomaly_score_threshold": 3,
@@ -270195,7 +270195,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb",
               "detected_at": "2024-09-17T08:37:18.203263",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 2.5827267063790265,
               "anomaly_score_threshold": 3,
@@ -270230,7 +270230,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb",
               "detected_at": "2024-09-17T08:37:18.203263",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": -0.13998014272261033,
               "anomaly_score_threshold": 3,
@@ -270265,7 +270265,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb",
               "detected_at": "2024-09-17T08:37:18.203263",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.1724305275360632,
               "anomaly_score_threshold": 3,
@@ -270300,7 +270300,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb",
               "detected_at": "2024-09-17T08:37:18.203263",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.5621533983430184,
               "anomaly_score_threshold": 3,
@@ -270335,7 +270335,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb",
               "detected_at": "2024-09-17T08:37:18.203263",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.29561366363042746,
               "anomaly_score_threshold": 3,
@@ -270370,7 +270370,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb",
               "detected_at": "2024-09-17T08:37:18.203263",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": -0.9616626282755779,
               "anomaly_score_threshold": 3,
@@ -270405,7 +270405,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb",
               "detected_at": "2024-09-17T08:37:18.203263",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": -0.4939082623918486,
               "anomaly_score_threshold": 3,
@@ -270440,7 +270440,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb",
               "detected_at": "2024-09-17T08:37:18.203263",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.35113935626367215,
               "anomaly_score_threshold": 3,
@@ -270475,7 +270475,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb",
               "detected_at": "2024-09-17T08:37:18.203263",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": -0.2663241287481187,
               "anomaly_score_threshold": 3,
@@ -270510,7 +270510,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb",
               "detected_at": "2024-09-17T08:37:18.203263",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.49306229683715785,
               "anomaly_score_threshold": 3,
@@ -270545,7 +270545,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb",
               "detected_at": "2024-09-17T08:37:18.203263",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.011033955248365522,
               "anomaly_score_threshold": 3,
@@ -270580,7 +270580,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb",
               "detected_at": "2024-09-17T08:37:18.203263",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 1.7856436025656197,
               "anomaly_score_threshold": 3,
@@ -270615,7 +270615,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb",
               "detected_at": "2024-09-17T08:37:18.203263",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 5.186062127192063,
               "anomaly_score_threshold": 3,
@@ -270648,7 +270648,7 @@
           "database_name": "postgres",
           "schema_name": "edr",
           "table_name": "numeric_column_anomalies",
-          "column_name": "min",
+          "column_name": "min_val",
           "test_name": "column_anomalies",
           "test_display_name": "Column Anomalies",
           "original_path": "models/schema.yml",
@@ -270662,7 +270662,7 @@
           "test_query": "select * from (None) results\n      where\n        anomaly_score is not null and\n        upper(full_table_name) = upper(cast('POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES' as varchar(4096))) and\n        metric_name = cast('average' as varchar(4096))\n          and upper(column_name) = upper(cast('min' as varchar(4096)))",
           "test_params": {
             "column_anomalies": ["average"],
-            "column_name": "min",
+            "column_name": "min_val",
             "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}",
             "timestamp_column": "updated_at",
             "where_expression": null,
@@ -270719,7 +270719,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7",
               "detected_at": "2024-09-17T08:37:18.202160",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "average",
               "anomaly_score": 0.7071067811865476,
               "anomaly_score_threshold": 3,
@@ -270754,7 +270754,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7",
               "detected_at": "2024-09-17T08:37:18.202160",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "average",
               "anomaly_score": -0.4970825218595467,
               "anomaly_score_threshold": 3,
@@ -270789,7 +270789,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7",
               "detected_at": "2024-09-17T08:37:18.202160",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "average",
               "anomaly_score": 1.4795365699201515,
               "anomaly_score_threshold": 3,
@@ -270824,7 +270824,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7",
               "detected_at": "2024-09-17T08:37:18.202160",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "average",
               "anomaly_score": -1.7149742719371412,
               "anomaly_score_threshold": 3,
@@ -270859,7 +270859,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7",
               "detected_at": "2024-09-17T08:37:18.202160",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "average",
               "anomaly_score": -1.6888450869988973,
               "anomaly_score_threshold": 3,
@@ -270894,7 +270894,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7",
               "detected_at": "2024-09-17T08:37:18.202160",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "average",
               "anomaly_score": -0.655614492100346,
               "anomaly_score_threshold": 3,
@@ -270929,7 +270929,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7",
               "detected_at": "2024-09-17T08:37:18.202160",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "average",
               "anomaly_score": 1.5372126430212485,
               "anomaly_score_threshold": 3,
@@ -270964,7 +270964,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7",
               "detected_at": "2024-09-17T08:37:18.202160",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "average",
               "anomaly_score": 0.4415108589087184,
               "anomaly_score_threshold": 3,
@@ -270999,7 +270999,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7",
               "detected_at": "2024-09-17T08:37:18.202160",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "average",
               "anomaly_score": -2.058523345979261,
               "anomaly_score_threshold": 3,
@@ -271034,7 +271034,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7",
               "detected_at": "2024-09-17T08:37:18.202160",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "average",
               "anomaly_score": 1.7766662001457223,
               "anomaly_score_threshold": 3,
@@ -271069,7 +271069,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7",
               "detected_at": "2024-09-17T08:37:18.202160",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "average",
               "anomaly_score": -0.901752848051644,
               "anomaly_score_threshold": 3,
@@ -271104,7 +271104,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7",
               "detected_at": "2024-09-17T08:37:18.202160",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "average",
               "anomaly_score": -0.5176043596075687,
               "anomaly_score_threshold": 3,
@@ -271139,7 +271139,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7",
               "detected_at": "2024-09-17T08:37:18.202160",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "average",
               "anomaly_score": -0.7211088472839159,
               "anomaly_score_threshold": 3,
@@ -271174,7 +271174,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7",
               "detected_at": "2024-09-17T08:37:18.202160",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "average",
               "anomaly_score": 0.1163465794522468,
               "anomaly_score_threshold": 3,
@@ -271209,7 +271209,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7",
               "detected_at": "2024-09-17T08:37:18.202160",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "average",
               "anomaly_score": -0.9011428984951104,
               "anomaly_score_threshold": 3,
@@ -271244,7 +271244,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7",
               "detected_at": "2024-09-17T08:37:18.202160",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "average",
               "anomaly_score": -0.32633345425425203,
               "anomaly_score_threshold": 3,
@@ -271279,7 +271279,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7",
               "detected_at": "2024-09-17T08:37:18.202160",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "average",
               "anomaly_score": 0.912269759518618,
               "anomaly_score_threshold": 3,
@@ -271314,7 +271314,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7",
               "detected_at": "2024-09-17T08:37:18.202160",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "average",
               "anomaly_score": -0.32205016710438233,
               "anomaly_score_threshold": 3,
@@ -271349,7 +271349,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7",
               "detected_at": "2024-09-17T08:37:18.202160",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "average",
               "anomaly_score": -0.2790176392188665,
               "anomaly_score_threshold": 3,
@@ -271384,7 +271384,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7",
               "detected_at": "2024-09-17T08:37:18.202160",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "average",
               "anomaly_score": 0.87795352897897,
               "anomaly_score_threshold": 3,
@@ -271419,7 +271419,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7",
               "detected_at": "2024-09-17T08:37:18.202160",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "average",
               "anomaly_score": 0.6913164990469673,
               "anomaly_score_threshold": 3,
@@ -271454,7 +271454,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7",
               "detected_at": "2024-09-17T08:37:18.202160",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "average",
               "anomaly_score": 1.2999346936346796,
               "anomaly_score_threshold": 3,
@@ -271489,7 +271489,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7",
               "detected_at": "2024-09-17T08:37:18.202160",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "average",
               "anomaly_score": -1.1273276144566928,
               "anomaly_score_threshold": 3,
@@ -271524,7 +271524,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7",
               "detected_at": "2024-09-17T08:37:18.202160",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "average",
               "anomaly_score": -0.45662645907856336,
               "anomaly_score_threshold": 3,
@@ -271559,7 +271559,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7",
               "detected_at": "2024-09-17T08:37:18.202160",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "average",
               "anomaly_score": 0.4604993171426426,
               "anomaly_score_threshold": 3,
@@ -271594,7 +271594,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7",
               "detected_at": "2024-09-17T08:37:18.202160",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "average",
               "anomaly_score": -0.4716260297167855,
               "anomaly_score_threshold": 3,
@@ -271629,7 +271629,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7",
               "detected_at": "2024-09-17T08:37:18.202160",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "average",
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               "anomaly_score_threshold": 3,
@@ -271664,7 +271664,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7",
               "detected_at": "2024-09-17T08:37:18.202160",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "average",
               "anomaly_score": -0.6829828265317237,
               "anomaly_score_threshold": 3,
@@ -271699,7 +271699,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7",
               "detected_at": "2024-09-17T08:37:18.202160",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "average",
               "anomaly_score": -5.1472448304991865,
               "anomaly_score_threshold": 3,
@@ -271732,7 +271732,7 @@
           "database_name": "postgres",
           "schema_name": "edr",
           "table_name": "numeric_column_anomalies",
-          "column_name": "max",
+          "column_name": "max_val",
           "test_name": "column_anomalies",
           "test_display_name": "Column Anomalies",
           "original_path": "models/schema.yml",
@@ -271747,7 +271747,7 @@
           "test_params": {
             "column_anomalies": ["average"],
             "anomaly_direction": "drop",
-            "column_name": "max",
+            "column_name": "max_val",
             "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}",
             "timestamp_column": "updated_at",
             "where_expression": null,
@@ -271799,7 +271799,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_drop__average__max.e87fc4578f",
               "detected_at": "2024-09-17T08:36:56.271663",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": -0.7071067811865475,
               "anomaly_score_threshold": 3,
@@ -271834,7 +271834,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_drop__average__max.e87fc4578f",
               "detected_at": "2024-09-17T08:36:56.271663",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.8887540382763068,
               "anomaly_score_threshold": 3,
@@ -271869,7 +271869,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_drop__average__max.e87fc4578f",
               "detected_at": "2024-09-17T08:36:56.271663",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.9281437914045992,
               "anomaly_score_threshold": 3,
@@ -271904,7 +271904,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_drop__average__max.e87fc4578f",
               "detected_at": "2024-09-17T08:36:56.271663",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": -0.09599461851321081,
               "anomaly_score_threshold": 3,
@@ -271939,7 +271939,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_drop__average__max.e87fc4578f",
               "detected_at": "2024-09-17T08:36:56.271663",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": -0.6608047556580349,
               "anomaly_score_threshold": 3,
@@ -271974,7 +271974,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_drop__average__max.e87fc4578f",
               "detected_at": "2024-09-17T08:36:56.271663",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.6512559888531815,
               "anomaly_score_threshold": 3,
@@ -272009,7 +272009,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_drop__average__max.e87fc4578f",
               "detected_at": "2024-09-17T08:36:56.271663",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.6308040368337668,
               "anomaly_score_threshold": 3,
@@ -272044,7 +272044,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_drop__average__max.e87fc4578f",
               "detected_at": "2024-09-17T08:36:56.271663",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": -0.7755718777927628,
               "anomaly_score_threshold": 3,
@@ -272079,7 +272079,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_drop__average__max.e87fc4578f",
               "detected_at": "2024-09-17T08:36:56.271663",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 1.1030267814383428,
               "anomaly_score_threshold": 3,
@@ -272114,7 +272114,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_drop__average__max.e87fc4578f",
               "detected_at": "2024-09-17T08:36:56.271663",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.1679559075120216,
               "anomaly_score_threshold": 3,
@@ -272149,7 +272149,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_drop__average__max.e87fc4578f",
               "detected_at": "2024-09-17T08:36:56.271663",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": -0.3063228011378462,
               "anomaly_score_threshold": 3,
@@ -272184,7 +272184,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_drop__average__max.e87fc4578f",
               "detected_at": "2024-09-17T08:36:56.271663",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.21461211646590303,
               "anomaly_score_threshold": 3,
@@ -272219,7 +272219,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_drop__average__max.e87fc4578f",
               "detected_at": "2024-09-17T08:36:56.271663",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.31131709492105425,
               "anomaly_score_threshold": 3,
@@ -272254,7 +272254,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_drop__average__max.e87fc4578f",
               "detected_at": "2024-09-17T08:36:56.271663",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.251500992139858,
               "anomaly_score_threshold": 3,
@@ -272289,7 +272289,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_drop__average__max.e87fc4578f",
               "detected_at": "2024-09-17T08:36:56.271663",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.42210816153333885,
               "anomaly_score_threshold": 3,
@@ -272324,7 +272324,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_drop__average__max.e87fc4578f",
               "detected_at": "2024-09-17T08:36:56.271663",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": -0.605017605043944,
               "anomaly_score_threshold": 3,
@@ -272359,7 +272359,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_drop__average__max.e87fc4578f",
               "detected_at": "2024-09-17T08:36:56.271663",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 2.5827267063790265,
               "anomaly_score_threshold": 3,
@@ -272394,7 +272394,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_drop__average__max.e87fc4578f",
               "detected_at": "2024-09-17T08:36:56.271663",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": -0.13998014272261033,
               "anomaly_score_threshold": 3,
@@ -272429,7 +272429,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_drop__average__max.e87fc4578f",
               "detected_at": "2024-09-17T08:36:56.271663",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.1724305275360632,
               "anomaly_score_threshold": 3,
@@ -272464,7 +272464,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_drop__average__max.e87fc4578f",
               "detected_at": "2024-09-17T08:36:56.271663",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.5621533983430184,
               "anomaly_score_threshold": 3,
@@ -272499,7 +272499,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_drop__average__max.e87fc4578f",
               "detected_at": "2024-09-17T08:36:56.271663",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.29561366363042746,
               "anomaly_score_threshold": 3,
@@ -272534,7 +272534,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_drop__average__max.e87fc4578f",
               "detected_at": "2024-09-17T08:36:56.271663",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": -0.9616626282755779,
               "anomaly_score_threshold": 3,
@@ -272569,7 +272569,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_drop__average__max.e87fc4578f",
               "detected_at": "2024-09-17T08:36:56.271663",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": -0.4939082623918486,
               "anomaly_score_threshold": 3,
@@ -272604,7 +272604,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_drop__average__max.e87fc4578f",
               "detected_at": "2024-09-17T08:36:56.271663",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.35113935626367215,
               "anomaly_score_threshold": 3,
@@ -272639,7 +272639,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_drop__average__max.e87fc4578f",
               "detected_at": "2024-09-17T08:36:56.271663",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": -0.2663241287481187,
               "anomaly_score_threshold": 3,
@@ -272674,7 +272674,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_drop__average__max.e87fc4578f",
               "detected_at": "2024-09-17T08:36:56.271663",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.49306229683715785,
               "anomaly_score_threshold": 3,
@@ -272709,7 +272709,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_drop__average__max.e87fc4578f",
               "detected_at": "2024-09-17T08:36:56.271663",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.011033955248365522,
               "anomaly_score_threshold": 3,
@@ -272744,7 +272744,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_drop__average__max.e87fc4578f",
               "detected_at": "2024-09-17T08:36:56.271663",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 1.7856436025656197,
               "anomaly_score_threshold": 3,
@@ -272779,7 +272779,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_drop__average__max.e87fc4578f",
               "detected_at": "2024-09-17T08:36:56.271663",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 5.186062127192063,
               "anomaly_score_threshold": 3,
@@ -273896,7 +273896,7 @@
           "database_name": "postgres",
           "schema_name": "edr",
           "table_name": "numeric_column_anomalies",
-          "column_name": "max",
+          "column_name": "max_val",
           "test_name": "column_anomalies",
           "test_display_name": "Column Anomalies",
           "original_path": "models/schema.yml",
@@ -273910,7 +273910,7 @@
           "test_query": "select * from (None) results\n      where\n        anomaly_score is not null and\n        upper(full_table_name) = upper(cast('POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES' as varchar(4096))) and\n        metric_name = cast('max' as varchar(4096))\n          and upper(column_name) = upper(cast('max' as varchar(4096)))",
           "test_params": {
             "column_anomalies": ["max"],
-            "column_name": "max",
+            "column_name": "max_val",
             "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}",
             "timestamp_column": "updated_at",
             "where_expression": null,
@@ -273967,7 +273967,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__max.21a73d9fec",
               "detected_at": "2024-09-17T08:37:18.219590",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "max",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -274002,7 +274002,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__max.21a73d9fec",
               "detected_at": "2024-09-17T08:37:18.219590",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "max",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -274037,7 +274037,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__max.21a73d9fec",
               "detected_at": "2024-09-17T08:37:18.219590",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "max",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -274072,7 +274072,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__max.21a73d9fec",
               "detected_at": "2024-09-17T08:37:18.219590",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "max",
               "anomaly_score": -1.7888543819998572,
               "anomaly_score_threshold": 3,
@@ -274107,7 +274107,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__max.21a73d9fec",
               "detected_at": "2024-09-17T08:37:18.219590",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "max",
               "anomaly_score": 0.4082482904638398,
               "anomaly_score_threshold": 3,
@@ -274142,7 +274142,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__max.21a73d9fec",
               "detected_at": "2024-09-17T08:37:18.219590",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "max",
               "anomaly_score": 0.3779644730092165,
               "anomaly_score_threshold": 3,
@@ -274177,7 +274177,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__max.21a73d9fec",
               "detected_at": "2024-09-17T08:37:18.219590",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "max",
               "anomaly_score": 0.35355339059327373,
               "anomaly_score_threshold": 3,
@@ -274212,7 +274212,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__max.21a73d9fec",
               "detected_at": "2024-09-17T08:37:18.219590",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "max",
               "anomaly_score": -1.763834207376379,
               "anomaly_score_threshold": 3,
@@ -274247,7 +274247,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__max.21a73d9fec",
               "detected_at": "2024-09-17T08:37:18.219590",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "max",
               "anomaly_score": 0.4743416490252299,
               "anomaly_score_threshold": 3,
@@ -274282,7 +274282,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__max.21a73d9fec",
               "detected_at": "2024-09-17T08:37:18.219590",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "max",
               "anomaly_score": 0.44946657497550746,
               "anomaly_score_threshold": 3,
@@ -274317,7 +274317,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__max.21a73d9fec",
               "detected_at": "2024-09-17T08:37:18.219590",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "max",
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               "anomaly_score_threshold": 3,
@@ -274352,7 +274352,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__max.21a73d9fec",
               "detected_at": "2024-09-17T08:37:18.219590",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "max",
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               "anomaly_score_threshold": 3,
@@ -274387,7 +274387,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__max.21a73d9fec",
               "detected_at": "2024-09-17T08:37:18.219590",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "max",
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               "anomaly_score_threshold": 3,
@@ -274422,7 +274422,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__max.21a73d9fec",
               "detected_at": "2024-09-17T08:37:18.219590",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "max",
               "anomaly_score": 0.3789323733725152,
               "anomaly_score_threshold": 3,
@@ -274457,7 +274457,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__max.21a73d9fec",
               "detected_at": "2024-09-17T08:37:18.219590",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "max",
               "anomaly_score": 0.36596252735569995,
               "anomaly_score_threshold": 3,
@@ -274492,7 +274492,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__max.21a73d9fec",
               "detected_at": "2024-09-17T08:37:18.219590",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "max",
               "anomaly_score": -2.0957473279823096,
               "anomaly_score_threshold": 3,
@@ -274527,7 +274527,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__max.21a73d9fec",
               "detected_at": "2024-09-17T08:37:18.219590",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "max",
               "anomaly_score": 0.4346134936801519,
               "anomaly_score_threshold": 3,
@@ -274562,7 +274562,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__max.21a73d9fec",
               "detected_at": "2024-09-17T08:37:18.219590",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "max",
               "anomaly_score": -1.8848425873126156,
               "anomaly_score_threshold": 3,
@@ -274597,7 +274597,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__max.21a73d9fec",
               "detected_at": "2024-09-17T08:37:18.219590",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "max",
               "anomaly_score": 0.48733971724042047,
               "anomaly_score_threshold": 3,
@@ -274632,7 +274632,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__max.21a73d9fec",
               "detected_at": "2024-09-17T08:37:18.219590",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "max",
               "anomaly_score": 0.4733810683311845,
               "anomaly_score_threshold": 3,
@@ -274667,7 +274667,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__max.21a73d9fec",
               "detected_at": "2024-09-17T08:37:18.219590",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "max",
               "anomaly_score": -1.8015145143613498,
               "anomaly_score_threshold": 3,
@@ -274702,7 +274702,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__max.21a73d9fec",
               "detected_at": "2024-09-17T08:37:18.219590",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "max",
               "anomaly_score": 0.5154614273558762,
               "anomaly_score_threshold": 3,
@@ -274737,7 +274737,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__max.21a73d9fec",
               "detected_at": "2024-09-17T08:37:18.219590",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "max",
               "anomaly_score": 0.5021881942620349,
               "anomaly_score_threshold": 3,
@@ -274772,7 +274772,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__max.21a73d9fec",
               "detected_at": "2024-09-17T08:37:18.219590",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "max",
               "anomaly_score": 0.4898979485566078,
               "anomaly_score_threshold": 3,
@@ -274807,7 +274807,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__max.21a73d9fec",
               "detected_at": "2024-09-17T08:37:18.219590",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "max",
               "anomaly_score": 0.4784743764693129,
               "anomaly_score_threshold": 3,
@@ -274842,7 +274842,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__max.21a73d9fec",
               "detected_at": "2024-09-17T08:37:18.219590",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "max",
               "anomaly_score": 0.4678196435115046,
               "anomaly_score_threshold": 3,
@@ -274877,7 +274877,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__max.21a73d9fec",
               "detected_at": "2024-09-17T08:37:18.219590",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "max",
               "anomaly_score": 0.4578507717504886,
               "anomaly_score_threshold": 3,
@@ -274912,7 +274912,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__max.21a73d9fec",
               "detected_at": "2024-09-17T08:37:18.219590",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "max",
               "anomaly_score": 0.44849685092248026,
               "anomaly_score_threshold": 3,
@@ -274947,7 +274947,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__max.21a73d9fec",
               "detected_at": "2024-09-17T08:37:18.219590",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "max",
               "anomaly_score": 5.293475976723012,
               "anomaly_score_threshold": 3,
@@ -274980,7 +274980,7 @@
           "database_name": "postgres",
           "schema_name": "edr",
           "table_name": "numeric_column_anomalies",
-          "column_name": "min",
+          "column_name": "min_val",
           "test_name": "column_anomalies",
           "test_display_name": "Column Anomalies",
           "original_path": "models/schema.yml",
@@ -274994,7 +274994,7 @@
           "test_query": "select * from (None) results\n      where\n        anomaly_score is not null and\n        upper(full_table_name) = upper(cast('POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES' as varchar(4096))) and\n        metric_name = cast('max' as varchar(4096))\n          and upper(column_name) = upper(cast('min' as varchar(4096)))",
           "test_params": {
             "column_anomalies": ["max"],
-            "column_name": "min",
+            "column_name": "min_val",
             "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}",
             "timestamp_column": "updated_at",
             "where_expression": null,
@@ -275051,7 +275051,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__min.6758a0b107",
               "detected_at": "2024-09-17T08:37:18.219616",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "max",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -275086,7 +275086,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__min.6758a0b107",
               "detected_at": "2024-09-17T08:37:18.219616",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "max",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -275121,7 +275121,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__min.6758a0b107",
               "detected_at": "2024-09-17T08:37:18.219616",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "max",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -275156,7 +275156,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__min.6758a0b107",
               "detected_at": "2024-09-17T08:37:18.219616",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "max",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -275191,7 +275191,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__min.6758a0b107",
               "detected_at": "2024-09-17T08:37:18.219616",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "max",
               "anomaly_score": -2.0412414523193037,
               "anomaly_score_threshold": 3,
@@ -275226,7 +275226,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__min.6758a0b107",
               "detected_at": "2024-09-17T08:37:18.219616",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "max",
               "anomaly_score": 0.3779644730092165,
               "anomaly_score_threshold": 3,
@@ -275261,7 +275261,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__min.6758a0b107",
               "detected_at": "2024-09-17T08:37:18.219616",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "max",
               "anomaly_score": -0.8400268812903088,
               "anomaly_score_threshold": 3,
@@ -275296,7 +275296,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__min.6758a0b107",
               "detected_at": "2024-09-17T08:37:18.219616",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "max",
               "anomaly_score": 0.47140452079104506,
               "anomaly_score_threshold": 3,
@@ -275331,7 +275331,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__min.6758a0b107",
               "detected_at": "2024-09-17T08:37:18.219616",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "max",
               "anomaly_score": 0.4444783184923322,
               "anomaly_score_threshold": 3,
@@ -275366,7 +275366,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__min.6758a0b107",
               "detected_at": "2024-09-17T08:37:18.219616",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "max",
               "anomaly_score": 0.42174116783666193,
               "anomaly_score_threshold": 3,
@@ -275401,7 +275401,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__min.6758a0b107",
               "detected_at": "2024-09-17T08:37:18.219616",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "max",
               "anomaly_score": 0.40219983326992187,
               "anomaly_score_threshold": 3,
@@ -275436,7 +275436,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__min.6758a0b107",
               "detected_at": "2024-09-17T08:37:18.219616",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "max",
               "anomaly_score": 0.3851644432598143,
               "anomaly_score_threshold": 3,
@@ -275471,7 +275471,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__min.6758a0b107",
               "detected_at": "2024-09-17T08:37:18.219616",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "max",
               "anomaly_score": 0.3701382740549606,
               "anomaly_score_threshold": 3,
@@ -275506,7 +275506,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__min.6758a0b107",
               "detected_at": "2024-09-17T08:37:18.219616",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "max",
               "anomaly_score": 0.35675303400631764,
               "anomaly_score_threshold": 3,
@@ -275541,7 +275541,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__min.6758a0b107",
               "detected_at": "2024-09-17T08:37:18.219616",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "max",
               "anomaly_score": 0.3447289198326913,
               "anomaly_score_threshold": 3,
@@ -275576,7 +275576,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__min.6758a0b107",
               "detected_at": "2024-09-17T08:37:18.219616",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "max",
               "anomaly_score": -3.0716544651334403,
               "anomaly_score_threshold": 3,
@@ -275611,7 +275611,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__min.6758a0b107",
               "detected_at": "2024-09-17T08:37:18.219616",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "max",
               "anomaly_score": 0.3967460238079474,
               "anomaly_score_threshold": 3,
@@ -275646,7 +275646,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__min.6758a0b107",
               "detected_at": "2024-09-17T08:37:18.219616",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "max",
               "anomaly_score": 0.38507629485651323,
               "anomaly_score_threshold": 3,
@@ -275681,7 +275681,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__min.6758a0b107",
               "detected_at": "2024-09-17T08:37:18.219616",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "max",
               "anomaly_score": 0.37438474117709464,
               "anomaly_score_threshold": 3,
@@ -275716,7 +275716,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__min.6758a0b107",
               "detected_at": "2024-09-17T08:37:18.219616",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "max",
               "anomaly_score": 0.3645413082882342,
               "anomaly_score_threshold": 3,
@@ -275751,7 +275751,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__min.6758a0b107",
               "detected_at": "2024-09-17T08:37:18.219616",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "max",
               "anomaly_score": 0.3554390220498153,
               "anomaly_score_threshold": 3,
@@ -275786,7 +275786,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__min.6758a0b107",
               "detected_at": "2024-09-17T08:37:18.219616",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "max",
               "anomaly_score": 0.34698895917976225,
               "anomaly_score_threshold": 3,
@@ -275821,7 +275821,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__min.6758a0b107",
               "detected_at": "2024-09-17T08:37:18.219616",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "max",
               "anomaly_score": 0.3391164991562634,
               "anomaly_score_threshold": 3,
@@ -275856,7 +275856,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__min.6758a0b107",
               "detected_at": "2024-09-17T08:37:18.219616",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "max",
               "anomaly_score": -0.9767857643556492,
               "anomaly_score_threshold": 3,
@@ -275891,7 +275891,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__min.6758a0b107",
               "detected_at": "2024-09-17T08:37:18.219616",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "max",
               "anomaly_score": -0.9407722612579593,
               "anomaly_score_threshold": 3,
@@ -275926,7 +275926,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__min.6758a0b107",
               "detected_at": "2024-09-17T08:37:18.219616",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "max",
               "anomaly_score": 0.40923320936324586,
               "anomaly_score_threshold": 3,
@@ -275961,7 +275961,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__min.6758a0b107",
               "detected_at": "2024-09-17T08:37:18.219616",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "max",
               "anomaly_score": 0.4008918628686251,
               "anomaly_score_threshold": 3,
@@ -275996,7 +275996,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__min.6758a0b107",
               "detected_at": "2024-09-17T08:37:18.219616",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "max",
               "anomaly_score": 0.39304294755242236,
               "anomaly_score_threshold": 3,
@@ -276031,7 +276031,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__min.6758a0b107",
               "detected_at": "2024-09-17T08:37:18.219616",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "max",
               "anomaly_score": 0.38563966175429043,
               "anomaly_score_threshold": 3,
@@ -277148,7 +277148,7 @@
           "database_name": "postgres",
           "schema_name": "edr",
           "table_name": "numeric_column_anomalies",
-          "column_name": "max",
+          "column_name": "max_val",
           "test_name": "column_anomalies",
           "test_display_name": "Column Anomalies",
           "original_path": "models/schema.yml",
@@ -277162,7 +277162,7 @@
           "test_query": "select * from (None) results\n      where\n        anomaly_score is not null and\n        upper(full_table_name) = upper(cast('POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES' as varchar(4096))) and\n        metric_name = cast('min' as varchar(4096))\n          and upper(column_name) = upper(cast('max' as varchar(4096)))",
           "test_params": {
             "column_anomalies": ["min"],
-            "column_name": "max",
+            "column_name": "max_val",
             "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}",
             "timestamp_column": "updated_at",
             "where_expression": null,
@@ -277219,7 +277219,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__max.9841e551cc",
               "detected_at": "2024-09-17T08:37:18.209995",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "min",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -277254,7 +277254,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__max.9841e551cc",
               "detected_at": "2024-09-17T08:37:18.209995",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "min",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -277289,7 +277289,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__max.9841e551cc",
               "detected_at": "2024-09-17T08:37:18.209995",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "min",
               "anomaly_score": 1.5,
               "anomaly_score_threshold": 3,
@@ -277324,7 +277324,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__max.9841e551cc",
               "detected_at": "2024-09-17T08:37:18.209995",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "min",
               "anomaly_score": -0.4472135954999643,
               "anomaly_score_threshold": 3,
@@ -277359,7 +277359,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__max.9841e551cc",
               "detected_at": "2024-09-17T08:37:18.209995",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "min",
               "anomaly_score": -0.4082482904638572,
               "anomaly_score_threshold": 3,
@@ -277394,7 +277394,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__max.9841e551cc",
               "detected_at": "2024-09-17T08:37:18.209995",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "min",
               "anomaly_score": -0.37796447300923525,
               "anomaly_score_threshold": 3,
@@ -277429,7 +277429,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__max.9841e551cc",
               "detected_at": "2024-09-17T08:37:18.209995",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "min",
               "anomaly_score": -0.35355339059327373,
               "anomaly_score_threshold": 3,
@@ -277464,7 +277464,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__max.9841e551cc",
               "detected_at": "2024-09-17T08:37:18.209995",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "min",
               "anomaly_score": -0.33333333333334275,
               "anomaly_score_threshold": 3,
@@ -277499,7 +277499,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__max.9841e551cc",
               "detected_at": "2024-09-17T08:37:18.209995",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "min",
               "anomaly_score": -0.31622776601684244,
               "anomaly_score_threshold": 3,
@@ -277534,7 +277534,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__max.9841e551cc",
               "detected_at": "2024-09-17T08:37:18.209995",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "min",
               "anomaly_score": -0.3015113445777722,
               "anomaly_score_threshold": 3,
@@ -277569,7 +277569,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__max.9841e551cc",
               "detected_at": "2024-09-17T08:37:18.209995",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "min",
               "anomaly_score": 1.2065994998097656,
               "anomaly_score_threshold": 3,
@@ -277604,7 +277604,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__max.9841e551cc",
               "detected_at": "2024-09-17T08:37:18.209995",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "min",
               "anomaly_score": -0.3851644432598143,
               "anomaly_score_threshold": 3,
@@ -277639,7 +277639,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__max.9841e551cc",
               "detected_at": "2024-09-17T08:37:18.209995",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "min",
               "anomaly_score": -0.37013827405493605,
               "anomaly_score_threshold": 3,
@@ -277674,7 +277674,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__max.9841e551cc",
               "detected_at": "2024-09-17T08:37:18.209995",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "min",
               "anomaly_score": -0.356753034006343,
               "anomaly_score_threshold": 3,
@@ -277709,7 +277709,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__max.9841e551cc",
               "detected_at": "2024-09-17T08:37:18.209995",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "min",
               "anomaly_score": -0.3447289198326913,
               "anomaly_score_threshold": 3,
@@ -277744,7 +277744,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__max.9841e551cc",
               "detected_at": "2024-09-17T08:37:18.209995",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "min",
               "anomaly_score": -0.3338489304447865,
               "anomaly_score_threshold": 3,
@@ -277779,7 +277779,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__max.9841e551cc",
               "detected_at": "2024-09-17T08:37:18.209995",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "min",
               "anomaly_score": -0.32394177193585927,
               "anomaly_score_threshold": 3,
@@ -277814,7 +277814,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__max.9841e551cc",
               "detected_at": "2024-09-17T08:37:18.209995",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "min",
               "anomaly_score": -0.3148701428436028,
               "anomaly_score_threshold": 3,
@@ -277849,7 +277849,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__max.9841e551cc",
               "detected_at": "2024-09-17T08:37:18.209995",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "min",
               "anomaly_score": -0.3065225015493171,
               "anomaly_score_threshold": 3,
@@ -277884,7 +277884,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__max.9841e551cc",
               "detected_at": "2024-09-17T08:37:18.209995",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "min",
               "anomaly_score": 1.5818231576774018,
               "anomaly_score_threshold": 3,
@@ -277919,7 +277919,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__max.9841e551cc",
               "detected_at": "2024-09-17T08:37:18.209995",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "min",
               "anomaly_score": -0.3628518182485477,
               "anomaly_score_threshold": 3,
@@ -277954,7 +277954,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__max.9841e551cc",
               "detected_at": "2024-09-17T08:37:18.209995",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "min",
               "anomaly_score": -0.35418282564845743,
               "anomaly_score_threshold": 3,
@@ -277989,7 +277989,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__max.9841e551cc",
               "detected_at": "2024-09-17T08:37:18.209995",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "min",
               "anomaly_score": -0.34610932762159635,
               "anomaly_score_threshold": 3,
@@ -278024,7 +278024,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__max.9841e551cc",
               "detected_at": "2024-09-17T08:37:18.209995",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "min",
               "anomaly_score": -0.33856589561211997,
               "anomaly_score_threshold": 3,
@@ -278059,7 +278059,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__max.9841e551cc",
               "detected_at": "2024-09-17T08:37:18.209995",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "min",
               "anomaly_score": -0.3314967720659121,
               "anomaly_score_threshold": 3,
@@ -278094,7 +278094,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__max.9841e551cc",
               "detected_at": "2024-09-17T08:37:18.209995",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "min",
               "anomaly_score": -0.3248541022089371,
               "anomaly_score_threshold": 3,
@@ -278129,7 +278129,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__max.9841e551cc",
               "detected_at": "2024-09-17T08:37:18.209995",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "min",
               "anomaly_score": -0.3185965464321459,
               "anomaly_score_threshold": 3,
@@ -278164,7 +278164,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__max.9841e551cc",
               "detected_at": "2024-09-17T08:37:18.209995",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "min",
               "anomaly_score": -0.3126881799192291,
               "anomaly_score_threshold": 3,
@@ -278199,7 +278199,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__max.9841e551cc",
               "detected_at": "2024-09-17T08:37:18.209995",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "min",
               "anomaly_score": -0.3070976116635235,
               "anomaly_score_threshold": 3,
@@ -278232,7 +278232,7 @@
           "database_name": "postgres",
           "schema_name": "edr",
           "table_name": "numeric_column_anomalies",
-          "column_name": "min",
+          "column_name": "min_val",
           "test_name": "column_anomalies",
           "test_display_name": "Column Anomalies",
           "original_path": "models/schema.yml",
@@ -278246,7 +278246,7 @@
           "test_query": "select * from (None) results\n      where\n        anomaly_score is not null and\n        upper(full_table_name) = upper(cast('POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES' as varchar(4096))) and\n        metric_name = cast('min' as varchar(4096))\n          and upper(column_name) = upper(cast('min' as varchar(4096)))",
           "test_params": {
             "column_anomalies": ["min"],
-            "column_name": "min",
+            "column_name": "min_val",
             "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}",
             "timestamp_column": "updated_at",
             "where_expression": null,
@@ -278303,7 +278303,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__min.72357fb8ab",
               "detected_at": "2024-09-17T08:37:18.063164",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "min",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -278338,7 +278338,7 @@
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               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.251500992139858,
               "anomaly_score_threshold": 3,
@@ -279873,7 +279873,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_spike__average__max.d74f23e2ef",
               "detected_at": "2024-09-17T08:36:56.271685",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.42210816153333885,
               "anomaly_score_threshold": 3,
@@ -279908,7 +279908,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_spike__average__max.d74f23e2ef",
               "detected_at": "2024-09-17T08:36:56.271685",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": -0.605017605043944,
               "anomaly_score_threshold": 3,
@@ -279943,7 +279943,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_spike__average__max.d74f23e2ef",
               "detected_at": "2024-09-17T08:36:56.271685",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 2.5827267063790265,
               "anomaly_score_threshold": 3,
@@ -279978,7 +279978,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_spike__average__max.d74f23e2ef",
               "detected_at": "2024-09-17T08:36:56.271685",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": -0.13998014272261033,
               "anomaly_score_threshold": 3,
@@ -280013,7 +280013,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_spike__average__max.d74f23e2ef",
               "detected_at": "2024-09-17T08:36:56.271685",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.1724305275360632,
               "anomaly_score_threshold": 3,
@@ -280048,7 +280048,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_spike__average__max.d74f23e2ef",
               "detected_at": "2024-09-17T08:36:56.271685",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.5621533983430184,
               "anomaly_score_threshold": 3,
@@ -280083,7 +280083,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_spike__average__max.d74f23e2ef",
               "detected_at": "2024-09-17T08:36:56.271685",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.29561366363042746,
               "anomaly_score_threshold": 3,
@@ -280118,7 +280118,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_spike__average__max.d74f23e2ef",
               "detected_at": "2024-09-17T08:36:56.271685",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": -0.9616626282755779,
               "anomaly_score_threshold": 3,
@@ -280153,7 +280153,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_spike__average__max.d74f23e2ef",
               "detected_at": "2024-09-17T08:36:56.271685",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": -0.4939082623918486,
               "anomaly_score_threshold": 3,
@@ -280188,7 +280188,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_spike__average__max.d74f23e2ef",
               "detected_at": "2024-09-17T08:36:56.271685",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.35113935626367215,
               "anomaly_score_threshold": 3,
@@ -280223,7 +280223,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_spike__average__max.d74f23e2ef",
               "detected_at": "2024-09-17T08:36:56.271685",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": -0.2663241287481187,
               "anomaly_score_threshold": 3,
@@ -280258,7 +280258,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_spike__average__max.d74f23e2ef",
               "detected_at": "2024-09-17T08:36:56.271685",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.49306229683715785,
               "anomaly_score_threshold": 3,
@@ -280293,7 +280293,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_spike__average__max.d74f23e2ef",
               "detected_at": "2024-09-17T08:36:56.271685",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 0.011033955248365522,
               "anomaly_score_threshold": 3,
@@ -280328,7 +280328,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_spike__average__max.d74f23e2ef",
               "detected_at": "2024-09-17T08:36:56.271685",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 1.7856436025656197,
               "anomaly_score_threshold": 3,
@@ -280363,7 +280363,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_spike__average__max.d74f23e2ef",
               "detected_at": "2024-09-17T08:36:56.271685",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "average",
               "anomaly_score": 5.186062127192063,
               "anomaly_score_threshold": 3,
@@ -290143,7 +290143,7 @@
           "database_name": "postgres",
           "schema_name": "edr",
           "table_name": "numeric_column_anomalies",
-          "column_name": "sum",
+          "column_name": "sum_val",
           "test_name": "column_anomalies",
           "test_display_name": "Column Anomalies",
           "original_path": "models/schema.yml",
@@ -290157,7 +290157,7 @@
           "test_query": "select * from (None) results\n      where\n        anomaly_score is not null and\n        upper(full_table_name) = upper(cast('POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES' as varchar(4096))) and\n        metric_name = cast('sum' as varchar(4096))\n          and upper(column_name) = upper(cast('sum' as varchar(4096)))",
           "test_params": {
             "column_anomalies": ["sum"],
-            "column_name": "sum",
+            "column_name": "sum_val",
             "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}",
             "timestamp_column": "updated_at",
             "where_expression": null,
@@ -290214,7 +290214,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_sum__sum.6ede4629eb",
               "detected_at": "2024-09-17T08:37:18.203099",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "sum",
               "anomaly_score": 0.7071067811865475,
               "anomaly_score_threshold": 3,
@@ -290249,7 +290249,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_sum__sum.6ede4629eb",
               "detected_at": "2024-09-17T08:37:18.203099",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "sum",
               "anomaly_score": -1.1546990451435037,
               "anomaly_score_threshold": 3,
@@ -290284,7 +290284,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_sum__sum.6ede4629eb",
               "detected_at": "2024-09-17T08:37:18.203099",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "sum",
               "anomaly_score": -1.1103830704935824,
               "anomaly_score_threshold": 3,
@@ -290319,7 +290319,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_sum__sum.6ede4629eb",
               "detected_at": "2024-09-17T08:37:18.203099",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "sum",
               "anomaly_score": -1.3630349951060639,
               "anomaly_score_threshold": 3,
@@ -290354,7 +290354,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_sum__sum.6ede4629eb",
               "detected_at": "2024-09-17T08:37:18.203099",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "sum",
               "anomaly_score": 1.2750224544848048,
               "anomaly_score_threshold": 3,
@@ -290389,7 +290389,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_sum__sum.6ede4629eb",
               "detected_at": "2024-09-17T08:37:18.203099",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "sum",
               "anomaly_score": -0.9515907112546935,
               "anomaly_score_threshold": 3,
@@ -290424,7 +290424,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_sum__sum.6ede4629eb",
               "detected_at": "2024-09-17T08:37:18.203099",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "sum",
               "anomaly_score": -0.778154130589765,
               "anomaly_score_threshold": 3,
@@ -290459,7 +290459,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_sum__sum.6ede4629eb",
               "detected_at": "2024-09-17T08:37:18.203099",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "sum",
               "anomaly_score": 0.6615591094720813,
               "anomaly_score_threshold": 3,
@@ -290494,7 +290494,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_sum__sum.6ede4629eb",
               "detected_at": "2024-09-17T08:37:18.203099",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "sum",
               "anomaly_score": 0.331214035238084,
               "anomaly_score_threshold": 3,
@@ -290529,7 +290529,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_sum__sum.6ede4629eb",
               "detected_at": "2024-09-17T08:37:18.203099",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "sum",
               "anomaly_score": 0.17883915117187324,
               "anomaly_score_threshold": 3,
@@ -290564,7 +290564,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_sum__sum.6ede4629eb",
               "detected_at": "2024-09-17T08:37:18.203099",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "sum",
               "anomaly_score": -0.6743112821127709,
               "anomaly_score_threshold": 3,
@@ -290599,7 +290599,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_sum__sum.6ede4629eb",
               "detected_at": "2024-09-17T08:37:18.203099",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "sum",
               "anomaly_score": 0.28871936944039867,
               "anomaly_score_threshold": 3,
@@ -290634,7 +290634,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_sum__sum.6ede4629eb",
               "detected_at": "2024-09-17T08:37:18.203099",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "sum",
               "anomaly_score": 0.611454351604304,
               "anomaly_score_threshold": 3,
@@ -290669,7 +290669,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_sum__sum.6ede4629eb",
               "detected_at": "2024-09-17T08:37:18.203099",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "sum",
               "anomaly_score": -0.8973701967903115,
               "anomaly_score_threshold": 3,
@@ -290704,7 +290704,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_sum__sum.6ede4629eb",
               "detected_at": "2024-09-17T08:37:18.203099",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "sum",
               "anomaly_score": 0.323101443632085,
               "anomaly_score_threshold": 3,
@@ -290739,7 +290739,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_sum__sum.6ede4629eb",
               "detected_at": "2024-09-17T08:37:18.203099",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "sum",
               "anomaly_score": -1.7006873383112162,
               "anomaly_score_threshold": 3,
@@ -290774,7 +290774,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_sum__sum.6ede4629eb",
               "detected_at": "2024-09-17T08:37:18.203099",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "sum",
               "anomaly_score": -1.083341706071991,
               "anomaly_score_threshold": 3,
@@ -290809,7 +290809,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_sum__sum.6ede4629eb",
               "detected_at": "2024-09-17T08:37:18.203099",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "sum",
               "anomaly_score": -0.5548178315083552,
               "anomaly_score_threshold": 3,
@@ -290844,7 +290844,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_sum__sum.6ede4629eb",
               "detected_at": "2024-09-17T08:37:18.203099",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "sum",
               "anomaly_score": 1.45428287662859,
               "anomaly_score_threshold": 3,
@@ -290879,7 +290879,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_sum__sum.6ede4629eb",
               "detected_at": "2024-09-17T08:37:18.203099",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "sum",
               "anomaly_score": 0.11077298262761338,
               "anomaly_score_threshold": 3,
@@ -290914,7 +290914,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_sum__sum.6ede4629eb",
               "detected_at": "2024-09-17T08:37:18.203099",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "sum",
               "anomaly_score": -1.5220842485837642,
               "anomaly_score_threshold": 3,
@@ -290949,7 +290949,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_sum__sum.6ede4629eb",
               "detected_at": "2024-09-17T08:37:18.203099",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "sum",
               "anomaly_score": 0.4865260429868005,
               "anomaly_score_threshold": 3,
@@ -290984,7 +290984,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_sum__sum.6ede4629eb",
               "detected_at": "2024-09-17T08:37:18.203099",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "sum",
               "anomaly_score": -1.0169372276557251,
               "anomaly_score_threshold": 3,
@@ -291019,7 +291019,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_sum__sum.6ede4629eb",
               "detected_at": "2024-09-17T08:37:18.203099",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "sum",
               "anomaly_score": 0.16945868887058793,
               "anomaly_score_threshold": 3,
@@ -291054,7 +291054,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_sum__sum.6ede4629eb",
               "detected_at": "2024-09-17T08:37:18.203099",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "sum",
               "anomaly_score": -0.3098968850009721,
               "anomaly_score_threshold": 3,
@@ -291089,7 +291089,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_sum__sum.6ede4629eb",
               "detected_at": "2024-09-17T08:37:18.203099",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "sum",
               "anomaly_score": -0.11884501374695568,
               "anomaly_score_threshold": 3,
@@ -291124,7 +291124,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_sum__sum.6ede4629eb",
               "detected_at": "2024-09-17T08:37:18.203099",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "sum",
               "anomaly_score": -0.49284731245929847,
               "anomaly_score_threshold": 3,
@@ -291159,7 +291159,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_sum__sum.6ede4629eb",
               "detected_at": "2024-09-17T08:37:18.203099",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "sum",
               "anomaly_score": 0.5864371345708931,
               "anomaly_score_threshold": 3,
@@ -291194,7 +291194,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_sum__sum.6ede4629eb",
               "detected_at": "2024-09-17T08:37:18.203099",
               "full_table_name": "POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "sum",
               "anomaly_score": 5.285336644533107,
               "anomaly_score_threshold": 3,
@@ -328079,13 +328079,13 @@
           "table_unique_id": "postgres.edr.numeric_column_anomalies",
           "test_type": "dbt_test",
           "test_sub_type": "singular",
-          "test_query": "select min from \"postgres\".\"edr\".\"numeric_column_anomalies\" where min < 100",
+          "test_query": "select min_val from \"postgres\".\"edr\".\"numeric_column_anomalies\" where min_val < 100",
           "test_params": {},
           "test_created_at": null,
           "description": null,
           "result": {
             "result_description": "Got 90 results, configured to fail if != 0",
-            "result_query": "select min from \"postgres\".\"edr\".\"numeric_column_anomalies\" where min < 100"
+            "result_query": "select min_val from \"postgres\".\"edr\".\"numeric_column_anomalies\" where min_val < 100"
           },
           "configuration": {
             "test_name": "singular_test_with_one_ref",
@@ -329199,7 +329199,7 @@
           "database_name": "postgres",
           "schema_name": "edr",
           "table_name": "copy_numeric_column_anomalies",
-          "column_name": "max",
+          "column_name": "max_val",
           "test_name": "all_columns_anomalies",
           "test_display_name": "All Columns Anomalies",
           "original_path": "models/schema.yml",
@@ -329269,7 +329269,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -329304,7 +329304,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -329339,7 +329339,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -329374,7 +329374,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -329409,7 +329409,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -329444,7 +329444,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -329479,7 +329479,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -329514,7 +329514,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -329549,7 +329549,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -329584,7 +329584,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -329619,7 +329619,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -329654,7 +329654,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -329689,7 +329689,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -329724,7 +329724,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -329759,7 +329759,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -329794,7 +329794,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -329829,7 +329829,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -329864,7 +329864,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -329899,7 +329899,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -329934,7 +329934,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -329969,7 +329969,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -330004,7 +330004,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -330039,7 +330039,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -330074,7 +330074,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -330109,7 +330109,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -330144,7 +330144,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -330179,7 +330179,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -330214,7 +330214,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -330249,7 +330249,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "max",
+              "column_name": "max_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -330282,7 +330282,7 @@
           "database_name": "postgres",
           "schema_name": "edr",
           "table_name": "copy_numeric_column_anomalies",
-          "column_name": "min",
+          "column_name": "min_val",
           "test_name": "all_columns_anomalies",
           "test_display_name": "All Columns Anomalies",
           "original_path": "models/schema.yml",
@@ -330352,7 +330352,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -330387,7 +330387,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -330422,7 +330422,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -330457,7 +330457,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -330492,7 +330492,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -330527,7 +330527,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -330562,7 +330562,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "min",
+              "column_name": "min_val",
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               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -333218,7 +333218,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -333253,7 +333253,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -333288,7 +333288,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -333323,7 +333323,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -333358,7 +333358,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -333393,7 +333393,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -333428,7 +333428,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -333463,7 +333463,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -333498,7 +333498,7 @@
               "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148",
               "detected_at": "2024-09-17T08:37:17.823483",
               "full_table_name": "POSTGRES.EDR.COPY_NUMERIC_COLUMN_ANOMALIES",
-              "column_name": "sum",
+              "column_name": "sum_val",
               "metric_name": "zero_count",
               "anomaly_score": 0.0,
               "anomaly_score_threshold": 3,
@@ -616164,13 +616164,13 @@
           "table_unique_id": "",
           "test_type": "dbt_test",
           "test_sub_type": "singular",
-          "test_query": "with min_len_issues as (\n    select null_count_int as min_issue from \"postgres\".\"edr\".\"any_type_column_anomalies\" where null_count_int < 100\n),\n\nmin_issues as (\n    select min as min_issue from \"postgres\".\"edr\".\"numeric_column_anomalies\" where min < 100\n),\n\nall_issues as (\n        select * from min_len_issues\n        union all\n        select * from min_issues\n)\n\nselect * from all_issues",
+          "test_query": "with min_len_issues as (\n    select null_count_int as min_issue from \"postgres\".\"edr\".\"any_type_column_anomalies\" where null_count_int < 100\n),\n\nmin_issues as (\n    select min as min_issue from \"postgres\".\"edr\".\"numeric_column_anomalies\" where min_val < 100\n),\n\nall_issues as (\n        select * from min_len_issues\n        union all\n        select * from min_issues\n)\n\nselect * from all_issues",
           "test_params": {},
           "test_created_at": null,
           "description": null,
           "result": {
             "result_description": "Got 90 results, configured to fail if != 0",
-            "result_query": "with min_len_issues as (\n    select null_count_int as min_issue from \"postgres\".\"edr\".\"any_type_column_anomalies\" where null_count_int < 100\n),\n\nmin_issues as (\n    select min as min_issue from \"postgres\".\"edr\".\"numeric_column_anomalies\" where min < 100\n),\n\nall_issues as (\n        select * from min_len_issues\n        union all\n        select * from min_issues\n)\n\nselect * from all_issues"
+            "result_query": "with min_len_issues as (\n    select null_count_int as min_issue from \"postgres\".\"edr\".\"any_type_column_anomalies\" where null_count_int < 100\n),\n\nmin_issues as (\n    select min as min_issue from \"postgres\".\"edr\".\"numeric_column_anomalies\" where min_val < 100\n),\n\nall_issues as (\n        select * from min_len_issues\n        union all\n        select * from min_issues\n)\n\nselect * from all_issues"
           },
           "configuration": {
             "test_name": "singular_test_with_two_refs",
@@ -619585,13 +619585,13 @@
           "table_unique_id": "postgres.test_seeds.numeric_column_anomalies_training",
           "test_type": "dbt_test",
           "test_sub_type": "singular",
-          "test_query": "select min from \"postgres\".\"test_seeds\".\"numeric_column_anomalies_training\" where min < 105",
+          "test_query": "select min_val from \"postgres\".\"test_seeds\".\"numeric_column_anomalies_training\" where min_val < 105",
           "test_params": {},
           "test_created_at": null,
           "description": null,
           "result": {
             "result_description": "Got 279 results, configured to fail if != 0",
-            "result_query": "select min from \"postgres\".\"test_seeds\".\"numeric_column_anomalies_training\" where min < 105"
+            "result_query": "select min_val from \"postgres\".\"test_seeds\".\"numeric_column_anomalies_training\" where min_val < 105"
           },
           "configuration": {
             "test_name": "singular_test_with_source_ref",
@@ -634294,13 +634294,13 @@
           "table_unique_id": "postgres.test_seeds.numeric_column_anomalies_training",
           "test_type": "dbt_test",
           "test_sub_type": "singular",
-          "test_query": "select min from \"postgres\".\"test_seeds\".\"numeric_column_anomalies_training\" where min < 105",
+          "test_query": "select min_val from \"postgres\".\"test_seeds\".\"numeric_column_anomalies_training\" where min_val < 105",
           "test_params": {},
           "test_created_at": null,
           "description": null,
           "result": {
             "result_description": "Got 279 results, configured to fail if != 0",
-            "result_query": "select min from \"postgres\".\"test_seeds\".\"numeric_column_anomalies_training\" where min < 105"
+            "result_query": "select min_val from \"postgres\".\"test_seeds\".\"numeric_column_anomalies_training\" where min_val < 105"
           },
           "configuration": {
             "test_name": "singular_test_with_source_ref",
@@ -634346,13 +634346,13 @@
           "table_unique_id": "postgres.edr.numeric_column_anomalies",
           "test_type": "dbt_test",
           "test_sub_type": "singular",
-          "test_query": "select min from \"postgres\".\"edr\".\"numeric_column_anomalies\" where min < 100",
+          "test_query": "select min_val from \"postgres\".\"edr\".\"numeric_column_anomalies\" where min_val < 100",
           "test_params": {},
           "test_created_at": null,
           "description": null,
           "result": {
             "result_description": "Got 90 results, configured to fail if != 0",
-            "result_query": "select min from \"postgres\".\"edr\".\"numeric_column_anomalies\" where min < 100"
+            "result_query": "select min_val from \"postgres\".\"edr\".\"numeric_column_anomalies\" where min_val < 100"
           },
           "configuration": {
             "test_name": "singular_test_with_one_ref",
@@ -635064,13 +635064,13 @@
           "table_unique_id": "",
           "test_type": "dbt_test",
           "test_sub_type": "singular",
-          "test_query": "select min from \"postgres\".\"edr\".\"numeric_column_anomalies\" where min < 100",
+          "test_query": "select min_val from \"postgres\".\"edr\".\"numeric_column_anomalies\" where min_val < 100",
           "test_params": {},
           "test_created_at": null,
           "description": null,
           "result": {
             "result_description": "Got 90 results, configured to fail if != 0",
-            "result_query": "select min from \"postgres\".\"edr\".\"numeric_column_anomalies\" where min < 100"
+            "result_query": "select min_val from \"postgres\".\"edr\".\"numeric_column_anomalies\" where min_val < 100"
           },
           "configuration": {
             "test_name": "singular_test_with_no_ref",
@@ -635695,7 +635695,7 @@
           "database_name": "postgres",
           "schema_name": "edr",
           "table_name": "numeric_column_anomalies",
-          "column_name": "max",
+          "column_name": "max_val",
           "test_name": "column_anomalies",
           "test_display_name": "Column Anomalies",
           "original_path": "models/schema.yml",
@@ -635709,7 +635709,7 @@
           "test_query": "select * from (None) results\n      where\n        anomaly_score is not null and\n        upper(full_table_name) = upper(cast('POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES' as varchar(4096))) and\n        metric_name = cast('average' as varchar(4096))\n          and upper(column_name) = upper(cast('max' as varchar(4096)))",
           "test_params": {
             "column_anomalies": ["average"],
-            "column_name": "max",
+            "column_name": "max_val",
             "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}",
             "timestamp_column": "updated_at",
             "where_expression": null,
@@ -635774,7 +635774,7 @@
           "database_name": "postgres",
           "schema_name": "edr",
           "table_name": "numeric_column_anomalies",
-          "column_name": "min",
+          "column_name": "min_val",
           "test_name": "column_anomalies",
           "test_display_name": "Column Anomalies",
           "original_path": "models/schema.yml",
@@ -635788,7 +635788,7 @@
           "test_query": "select * from (None) results\n      where\n        anomaly_score is not null and\n        upper(full_table_name) = upper(cast('POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES' as varchar(4096))) and\n        metric_name = cast('average' as varchar(4096))\n          and upper(column_name) = upper(cast('min' as varchar(4096)))",
           "test_params": {
             "column_anomalies": ["average"],
-            "column_name": "min",
+            "column_name": "min_val",
             "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}",
             "timestamp_column": "updated_at",
             "where_expression": null,
@@ -635853,7 +635853,7 @@
           "database_name": "postgres",
           "schema_name": "edr",
           "table_name": "numeric_column_anomalies",
-          "column_name": "max",
+          "column_name": "max_val",
           "test_name": "column_anomalies",
           "test_display_name": "Column Anomalies",
           "original_path": "models/schema.yml",
@@ -635868,7 +635868,7 @@
           "test_params": {
             "column_anomalies": ["average"],
             "anomaly_direction": "drop",
-            "column_name": "max",
+            "column_name": "max_val",
             "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}",
             "timestamp_column": "updated_at",
             "where_expression": null,
@@ -636007,7 +636007,7 @@
           "database_name": "postgres",
           "schema_name": "edr",
           "table_name": "numeric_column_anomalies",
-          "column_name": "max",
+          "column_name": "max_val",
           "test_name": "column_anomalies",
           "test_display_name": "Column Anomalies",
           "original_path": "models/schema.yml",
@@ -636021,7 +636021,7 @@
           "test_query": "select * from (None) results\n      where\n        anomaly_score is not null and\n        upper(full_table_name) = upper(cast('POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES' as varchar(4096))) and\n        metric_name = cast('max' as varchar(4096))\n          and upper(column_name) = upper(cast('max' as varchar(4096)))",
           "test_params": {
             "column_anomalies": ["max"],
-            "column_name": "max",
+            "column_name": "max_val",
             "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}",
             "timestamp_column": "updated_at",
             "where_expression": null,
@@ -636086,7 +636086,7 @@
           "database_name": "postgres",
           "schema_name": "edr",
           "table_name": "numeric_column_anomalies",
-          "column_name": "min",
+          "column_name": "min_val",
           "test_name": "column_anomalies",
           "test_display_name": "Column Anomalies",
           "original_path": "models/schema.yml",
@@ -636100,7 +636100,7 @@
           "test_query": "select * from (None) results\n      where\n        anomaly_score is not null and\n        upper(full_table_name) = upper(cast('POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES' as varchar(4096))) and\n        metric_name = cast('max' as varchar(4096))\n          and upper(column_name) = upper(cast('min' as varchar(4096)))",
           "test_params": {
             "column_anomalies": ["max"],
-            "column_name": "min",
+            "column_name": "min_val",
             "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}",
             "timestamp_column": "updated_at",
             "where_expression": null,
@@ -636244,7 +636244,7 @@
           "database_name": "postgres",
           "schema_name": "edr",
           "table_name": "numeric_column_anomalies",
-          "column_name": "max",
+          "column_name": "max_val",
           "test_name": "column_anomalies",
           "test_display_name": "Column Anomalies",
           "original_path": "models/schema.yml",
@@ -636258,7 +636258,7 @@
           "test_query": "select * from (None) results\n      where\n        anomaly_score is not null and\n        upper(full_table_name) = upper(cast('POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES' as varchar(4096))) and\n        metric_name = cast('min' as varchar(4096))\n          and upper(column_name) = upper(cast('max' as varchar(4096)))",
           "test_params": {
             "column_anomalies": ["min"],
-            "column_name": "max",
+            "column_name": "max_val",
             "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}",
             "timestamp_column": "updated_at",
             "where_expression": null,
@@ -636323,7 +636323,7 @@
           "database_name": "postgres",
           "schema_name": "edr",
           "table_name": "numeric_column_anomalies",
-          "column_name": "min",
+          "column_name": "min_val",
           "test_name": "column_anomalies",
           "test_display_name": "Column Anomalies",
           "original_path": "models/schema.yml",
@@ -636337,7 +636337,7 @@
           "test_query": "select * from (None) results\n      where\n        anomaly_score is not null and\n        upper(full_table_name) = upper(cast('POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES' as varchar(4096))) and\n        metric_name = cast('min' as varchar(4096))\n          and upper(column_name) = upper(cast('min' as varchar(4096)))",
           "test_params": {
             "column_anomalies": ["min"],
-            "column_name": "min",
+            "column_name": "min_val",
             "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}",
             "timestamp_column": "updated_at",
             "where_expression": null,
@@ -636402,7 +636402,7 @@
           "database_name": "postgres",
           "schema_name": "edr",
           "table_name": "numeric_column_anomalies",
-          "column_name": "max",
+          "column_name": "max_val",
           "test_name": "column_anomalies",
           "test_display_name": "Column Anomalies",
           "original_path": "models/schema.yml",
@@ -636417,7 +636417,7 @@
           "test_params": {
             "column_anomalies": ["average"],
             "anomaly_direction": "spike",
-            "column_name": "max",
+            "column_name": "max_val",
             "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}",
             "timestamp_column": "updated_at",
             "where_expression": null,
@@ -637179,7 +637179,7 @@
           "database_name": "postgres",
           "schema_name": "edr",
           "table_name": "numeric_column_anomalies",
-          "column_name": "sum",
+          "column_name": "sum_val",
           "test_name": "column_anomalies",
           "test_display_name": "Column Anomalies",
           "original_path": "models/schema.yml",
@@ -637193,7 +637193,7 @@
           "test_query": "select * from (None) results\n      where\n        anomaly_score is not null and\n        upper(full_table_name) = upper(cast('POSTGRES.EDR.NUMERIC_COLUMN_ANOMALIES' as varchar(4096))) and\n        metric_name = cast('sum' as varchar(4096))\n          and upper(column_name) = upper(cast('sum' as varchar(4096)))",
           "test_params": {
             "column_anomalies": ["sum"],
-            "column_name": "sum",
+            "column_name": "sum_val",
             "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}",
             "timestamp_column": "updated_at",
             "where_expression": null,
@@ -639986,13 +639986,13 @@
           "table_unique_id": "postgres.edr.numeric_column_anomalies",
           "test_type": "dbt_test",
           "test_sub_type": "singular",
-          "test_query": "select min from \"postgres\".\"edr\".\"numeric_column_anomalies\" where min < 100",
+          "test_query": "select min_val from \"postgres\".\"edr\".\"numeric_column_anomalies\" where min_val < 100",
           "test_params": {},
           "test_created_at": null,
           "description": null,
           "result": {
             "result_description": "Got 90 results, configured to fail if != 0",
-            "result_query": "select min from \"postgres\".\"edr\".\"numeric_column_anomalies\" where min < 100"
+            "result_query": "select min_val from \"postgres\".\"edr\".\"numeric_column_anomalies\" where min_val < 100"
           },
           "configuration": {
             "test_name": "singular_test_with_one_ref",
@@ -640105,7 +640105,7 @@
           "database_name": "postgres",
           "schema_name": "edr",
           "table_name": "copy_numeric_column_anomalies",
-          "column_name": "max",
+          "column_name": "max_val",
           "test_name": "all_columns_anomalies",
           "test_display_name": "All Columns Anomalies",
           "original_path": "models/schema.yml",
@@ -640183,7 +640183,7 @@
           "database_name": "postgres",
           "schema_name": "edr",
           "table_name": "copy_numeric_column_anomalies",
-          "column_name": "min",
+          "column_name": "min_val",
           "test_name": "all_columns_anomalies",
           "test_display_name": "All Columns Anomalies",
           "original_path": "models/schema.yml",
@@ -640339,7 +640339,7 @@
           "database_name": "postgres",
           "schema_name": "edr",
           "table_name": "copy_numeric_column_anomalies",
-          "column_name": "sum",
+          "column_name": "sum_val",
           "test_name": "all_columns_anomalies",
           "test_display_name": "All Columns Anomalies",
           "original_path": "models/schema.yml",
@@ -647178,13 +647178,13 @@
           "table_unique_id": "",
           "test_type": "dbt_test",
           "test_sub_type": "singular",
-          "test_query": "with min_len_issues as (\n    select null_count_int as min_issue from \"postgres\".\"edr\".\"any_type_column_anomalies\" where null_count_int < 100\n),\n\nmin_issues as (\n    select min as min_issue from \"postgres\".\"edr\".\"numeric_column_anomalies\" where min < 100\n),\n\nall_issues as (\n        select * from min_len_issues\n        union all\n        select * from min_issues\n)\n\nselect * from all_issues",
+          "test_query": "with min_len_issues as (\n    select null_count_int as min_issue from \"postgres\".\"edr\".\"any_type_column_anomalies\" where null_count_int < 100\n),\n\nmin_issues as (\n    select min as min_issue from \"postgres\".\"edr\".\"numeric_column_anomalies\" where min_val < 100\n),\n\nall_issues as (\n        select * from min_len_issues\n        union all\n        select * from min_issues\n)\n\nselect * from all_issues",
           "test_params": {},
           "test_created_at": null,
           "description": null,
           "result": {
             "result_description": "Got 90 results, configured to fail if != 0",
-            "result_query": "with min_len_issues as (\n    select null_count_int as min_issue from \"postgres\".\"edr\".\"any_type_column_anomalies\" where null_count_int < 100\n),\n\nmin_issues as (\n    select min as min_issue from \"postgres\".\"edr\".\"numeric_column_anomalies\" where min < 100\n),\n\nall_issues as (\n        select * from min_len_issues\n        union all\n        select * from min_issues\n)\n\nselect * from all_issues"
+            "result_query": "with min_len_issues as (\n    select null_count_int as min_issue from \"postgres\".\"edr\".\"any_type_column_anomalies\" where null_count_int < 100\n),\n\nmin_issues as (\n    select min as min_issue from \"postgres\".\"edr\".\"numeric_column_anomalies\" where min_val < 100\n),\n\nall_issues as (\n        select * from min_len_issues\n        union all\n        select * from min_issues\n)\n\nselect * from all_issues"
           },
           "configuration": {
             "test_name": "singular_test_with_two_refs",
@@ -647994,7 +647994,7 @@
       "status": "success",
       "last_exec_time": 0.1,
       "median_exec_time": 0.05,
-      "compiled_code": "select 1 as one",
+      "compiled_code": "select 1 as col1",
       "last_generated_at": "2024-09-17T08:38:20+00:00",
       "exec_time_change_rate": 100.0,
       "totals": { "errors": 0, "success": 6 },
@@ -648236,7 +648236,7 @@
       "status": "success",
       "last_exec_time": 0.9,
       "median_exec_time": 1.0,
-      "compiled_code": "select 1 as one",
+      "compiled_code": "select 1 as col1",
       "last_generated_at": "2024-09-17T08:36:52+00:00",
       "exec_time_change_rate": -9.999999999999998,
       "totals": { "errors": 0, "success": 2 },
diff --git a/tests/e2e_dbt_project/ci/restore_seed_cache.sh b/tests/e2e_dbt_project/ci/restore_seed_cache.sh
new file mode 100755
index 000000000..c468866b9
--- /dev/null
+++ b/tests/e2e_dbt_project/ci/restore_seed_cache.sh
@@ -0,0 +1,21 @@
+#!/bin/bash
+# Restore Docker volume data from a seed cache directory.
+# Usage: restore_seed_cache.sh <warehouse-type>
+set -euo pipefail
+
+WAREHOUSE_TYPE="${1:?Usage: restore_seed_cache.sh <warehouse-type>}"
+CACHE_DIR="/tmp/seed-cache-${WAREHOUSE_TYPE}"
+
+echo "Restoring seed cache for ${WAREHOUSE_TYPE}..."
+
+# Restore each Docker volume from the cached tarballs.
+# This runs BEFORE services start so containers initialise with cached data.
+for archive in "$CACHE_DIR"/*.tar.gz; do
+  [ -f "$archive" ] || continue
+  VOLUME_NAME=$(basename "$archive" .tar.gz)
+  echo "Restoring volume $VOLUME_NAME from $archive..."
+  docker volume create "$VOLUME_NAME" 2>/dev/null || true
+  docker run --rm -v "$VOLUME_NAME:/data" -v "$CACHE_DIR:/cache:ro" \
+    alpine sh -c "cd /data && tar xzf /cache/${VOLUME_NAME}.tar.gz"
+done
+echo "Seed cache restored."
diff --git a/tests/e2e_dbt_project/ci/save_seed_cache.sh b/tests/e2e_dbt_project/ci/save_seed_cache.sh
new file mode 100755
index 000000000..cb69e4857
--- /dev/null
+++ b/tests/e2e_dbt_project/ci/save_seed_cache.sh
@@ -0,0 +1,57 @@
+#!/bin/bash
+# Save Docker volume data to a cache directory for seed caching.
+# Usage: save_seed_cache.sh <warehouse-type>
+set -euo pipefail
+
+WAREHOUSE_TYPE="${1:?Usage: save_seed_cache.sh <warehouse-type>}"
+CACHE_DIR="/tmp/seed-cache-${WAREHOUSE_TYPE}"
+mkdir -p "$CACHE_DIR"
+
+# Get the Docker Compose project name and list volumes
+COMPOSE_VOLUMES=$(docker compose config --volumes 2>/dev/null || true)
+PROJECT=$(docker compose config 2>/dev/null | grep '^name:' | awk '{print $2}' || echo "e2e_dbt_project")
+
+# Stop running containers so no files change while archiving
+docker compose stop || true
+
+for vol in $COMPOSE_VOLUMES; do
+  FULL_VOL="${PROJECT}_${vol}"
+  if docker volume inspect "$FULL_VOL" >/dev/null 2>&1; then
+    echo "Saving volume $FULL_VOL..."
+    docker run --rm -v "$FULL_VOL:/data:ro" -v "$CACHE_DIR:/cache" \
+      alpine sh -c "cd /data && tar czf /cache/${FULL_VOL}.tar.gz ."
+  fi
+done
+
+# Restart containers for the rest of the pipeline
+docker compose start || true
+
+# Wait for services to be ready after restart
+ready=0
+case "$WAREHOUSE_TYPE" in
+  clickhouse)
+    for i in $(seq 1 30); do
+      if curl -sf http://localhost:8123/ping > /dev/null; then
+        ready=1; break
+      fi
+      echo "Waiting for ClickHouse after restart... ($i/30)"; sleep 2
+    done
+    ;;
+  postgres)
+    for i in $(seq 1 30); do
+      if pg_isready -h localhost -p 5432 > /dev/null 2>&1; then
+        ready=1; break
+      fi
+      echo "Waiting for Postgres after restart... ($i/30)"; sleep 2
+    done
+    ;;
+  *)
+    ready=1
+    ;;
+esac
+
+if [ "$ready" -ne 1 ]; then
+  echo "ERROR: $WAREHOUSE_TYPE did not become ready after restart" >&2
+  exit 1
+fi
+echo "Seed cache saved."
diff --git a/tests/e2e_dbt_project/data/training/numeric_column_anomalies_training.csv b/tests/e2e_dbt_project/data/training/numeric_column_anomalies_training.csv
index e69de29bb..7933ded6e 100644
--- a/tests/e2e_dbt_project/data/training/numeric_column_anomalies_training.csv
+++ b/tests/e2e_dbt_project/data/training/numeric_column_anomalies_training.csv
@@ -0,0 +1,6001 @@
+updated_at,occurred_at,min_val,max_val,zero_count,zero_percent,average,standard_deviation,variance,sum_val
+1969-12-30 00:00:00,1969-12-29 23:00:00,123,144,0,150,99,101,100,122
+1969-12-30 00:00:00,1969-12-29 23:00:00,186,126,0,100,101,100,99,100
+1969-12-30 00:00:00,1969-12-29 23:00:00,144,110,0,122,100,99,101,162
+1969-12-30 00:00:00,1969-12-29 23:00:00,123,141,0,187,100,100,100,119
+1969-12-30 00:00:00,1969-12-29 23:00:00,121,129,0,0,101,101,100,200
+1969-12-30 00:00:00,1969-12-29 23:00:00,114,142,0,200,101,99,100,106
+1969-12-30 00:00:00,1969-12-29 23:00:00,160,111,124,125,101,100,101,187
+1969-12-30 00:00:00,1969-12-29 23:00:00,159,140,116,146,100,101,100,147
+1969-12-30 00:00:00,1969-12-29 23:00:00,174,126,110,177,99,101,100,106
+1969-12-30 00:00:00,1969-12-29 23:00:00,159,160,145,159,101,100,99,179
+1969-12-30 00:00:00,1969-12-29 23:00:00,128,149,190,0,99,100,99,104
+1969-12-30 00:00:00,1969-12-29 23:00:00,109,111,175,186,99,99,100,196
+1969-12-30 00:00:00,1969-12-29 23:00:00,149,118,127,139,99,100,101,126
+1969-12-30 00:00:00,1969-12-29 23:00:00,165,152,126,101,100,100,100,160
+1969-12-30 00:00:00,1969-12-29 23:00:00,105,198,156,160,99,99,99,105
+1969-12-30 00:00:00,1969-12-29 23:00:00,105,189,142,0,99,99,101,121
+1969-12-30 00:00:00,1969-12-29 23:00:00,162,192,158,198,99,99,101,151
+1969-12-30 00:00:00,1969-12-29 23:00:00,199,158,118,128,101,101,101,194
+1969-12-30 00:00:00,1969-12-29 23:00:00,125,187,185,170,100,100,100,168
+1969-12-30 00:00:00,1969-12-29 23:00:00,190,108,116,134,101,101,100,191
+1969-12-30 00:00:00,1969-12-29 23:00:00,142,104,141,131,100,101,99,100
+1969-12-30 00:00:00,1969-12-29 23:00:00,188,152,156,156,99,101,100,109
+1969-12-30 00:00:00,1969-12-29 23:00:00,109,103,181,125,101,100,99,175
+1969-12-30 00:00:00,1969-12-29 23:00:00,135,200,148,136,99,101,99,162
+1969-12-30 00:00:00,1969-12-29 23:00:00,114,129,172,169,100,99,99,198
+1969-12-30 00:00:00,1969-12-29 23:00:00,164,186,146,117,101,100,101,118
+1969-12-30 00:00:00,1969-12-29 23:00:00,179,166,118,115,99,99,101,108
+1969-12-30 00:00:00,1969-12-29 23:00:00,154,194,114,168,99,101,99,126
+1969-12-30 00:00:00,1969-12-29 23:00:00,199,121,172,184,99,101,100,149
+1969-12-30 00:00:00,1969-12-29 23:00:00,100,189,100,170,101,100,99,166
+1969-12-30 00:00:00,1969-12-29 23:00:00,196,197,113,191,100,101,100,146
+1969-12-30 00:00:00,1969-12-29 23:00:00,104,134,111,0,101,99,100,100
+1969-12-30 00:00:00,1969-12-29 23:00:00,163,166,127,0,99,100,101,121
+1969-12-30 00:00:00,1969-12-29 23:00:00,117,192,125,154,100,101,100,185
+1969-12-30 00:00:00,1969-12-29 23:00:00,116,174,126,131,99,101,100,165
+1969-12-30 00:00:00,1969-12-29 23:00:00,155,169,107,184,101,101,101,171
+1969-12-30 00:00:00,1969-12-29 23:00:00,116,183,127,107,101,99,100,118
+1969-12-30 00:00:00,1969-12-29 23:00:00,120,130,165,120,100,99,101,120
+1969-12-30 00:00:00,1969-12-29 23:00:00,181,147,197,124,100,100,100,151
+1969-12-30 00:00:00,1969-12-29 23:00:00,118,155,153,0,101,100,101,105
+1969-12-30 00:00:00,1969-12-29 23:00:00,195,181,163,106,101,101,100,185
+1969-12-30 00:00:00,1969-12-29 23:00:00,124,158,108,0,100,99,100,132
+1969-12-30 00:00:00,1969-12-29 23:00:00,115,131,146,182,99,100,99,102
+1969-12-30 00:00:00,1969-12-29 23:00:00,167,196,160,101,99,99,100,100
+1969-12-30 00:00:00,1969-12-29 23:00:00,187,199,174,113,100,99,101,130
+1969-12-30 00:00:00,1969-12-29 23:00:00,135,163,143,168,99,99,99,178
+1969-12-30 00:00:00,1969-12-29 23:00:00,111,134,155,121,100,99,101,171
+1969-12-30 00:00:00,1969-12-29 23:00:00,153,107,109,103,100,99,99,126
+1969-12-30 00:00:00,1969-12-29 23:00:00,165,198,198,157,99,101,101,193
+1969-12-30 00:00:00,1969-12-29 23:00:00,200,143,118,105,101,101,101,190
+1969-12-30 00:00:00,1969-12-29 23:00:00,165,113,195,0,101,100,101,106
+1969-12-30 00:00:00,1969-12-29 23:00:00,182,105,111,170,100,99,99,177
+1969-12-30 00:00:00,1969-12-29 23:00:00,114,123,181,193,101,100,100,109
+1969-12-30 00:00:00,1969-12-29 23:00:00,169,114,171,167,101,101,99,185
+1969-12-30 00:00:00,1969-12-29 23:00:00,111,101,188,112,101,99,101,194
+1969-12-30 00:00:00,1969-12-29 23:00:00,171,180,183,0,99,101,99,141
+1969-12-30 00:00:00,1969-12-29 23:00:00,161,139,114,127,99,100,101,163
+1969-12-30 00:00:00,1969-12-29 23:00:00,157,100,162,129,99,99,101,179
+1969-12-30 00:00:00,1969-12-29 23:00:00,123,170,162,182,99,101,99,131
+1969-12-30 00:00:00,1969-12-29 23:00:00,114,193,105,111,100,99,99,180
+1969-12-30 00:00:00,1969-12-29 23:00:00,176,169,136,106,101,100,99,105
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+1969-12-30 00:00:00,1969-12-29 23:00:00,140,171,196,143,100,101,99,135
+1969-12-30 00:00:00,1969-12-29 23:00:00,133,104,156,168,100,101,100,185
+1969-12-30 00:00:00,1969-12-29 23:00:00,113,189,190,176,99,99,101,119
+1969-12-30 00:00:00,1969-12-29 23:00:00,174,125,110,188,101,99,100,183
+1969-12-30 00:00:00,1969-12-29 23:00:00,126,105,124,176,101,100,101,100
+1969-12-30 00:00:00,1969-12-29 23:00:00,187,113,119,155,99,100,100,104
+1969-12-30 00:00:00,1969-12-29 23:00:00,126,128,131,157,101,101,101,101
+1969-12-30 00:00:00,1969-12-29 23:00:00,198,189,140,167,101,100,99,143
+1969-12-30 00:00:00,1969-12-29 23:00:00,153,190,190,125,101,99,99,126
+1969-12-30 00:00:00,1969-12-29 23:00:00,137,109,142,161,100,101,99,114
+1969-12-30 00:00:00,1969-12-29 23:00:00,132,186,188,189,100,99,100,172
+1969-12-30 00:00:00,1969-12-29 23:00:00,188,172,128,0,100,100,101,110
+1969-12-30 00:00:00,1969-12-29 23:00:00,121,125,122,0,99,100,100,180
+1969-12-30 00:00:00,1969-12-29 23:00:00,104,173,197,117,100,100,101,163
+1969-12-30 00:00:00,1969-12-29 23:00:00,182,187,185,161,99,99,100,160
+1969-12-30 00:00:00,1969-12-29 23:00:00,182,105,182,0,100,100,101,104
+1969-12-30 00:00:00,1969-12-29 23:00:00,106,187,141,197,100,100,101,112
+1969-12-30 00:00:00,1969-12-29 23:00:00,129,104,194,171,99,100,100,162
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+1969-12-30 00:00:00,1969-12-29 23:00:00,149,134,173,187,100,99,100,159
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+1969-12-30 00:00:00,1969-12-29 23:00:00,127,100,150,176,100,100,100,127
+1969-12-30 00:00:00,1969-12-29 23:00:00,125,108,149,109,101,101,101,197
+1969-12-30 00:00:00,1969-12-29 23:00:00,169,139,150,114,99,99,101,173
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+1969-12-30 00:00:00,1969-12-29 23:00:00,169,158,162,186,99,101,99,142
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+1969-12-30 00:00:00,1969-12-29 23:00:00,154,100,112,121,100,101,101,134
+1969-12-30 00:00:00,1969-12-29 23:00:00,129,113,110,135,101,99,100,200
+1969-12-30 00:00:00,1969-12-29 23:00:00,105,134,148,179,99,99,99,152
+1969-12-30 00:00:00,1969-12-29 23:00:00,160,102,147,153,101,101,99,188
+1969-12-30 00:00:00,1969-12-29 23:00:00,102,142,114,180,100,99,101,125
+1969-12-30 00:00:00,1969-12-29 23:00:00,133,178,107,191,99,101,99,145
+1969-12-30 00:00:00,1969-12-29 23:00:00,142,186,110,149,101,101,99,193
+1969-12-30 00:00:00,1969-12-29 23:00:00,152,111,142,198,99,99,101,179
+1969-12-30 00:00:00,1969-12-29 23:00:00,179,150,114,129,99,100,99,142
+1969-12-30 00:00:00,1969-12-29 23:00:00,125,126,178,181,99,101,99,119
+1969-12-30 00:00:00,1969-12-29 23:00:00,189,117,183,125,99,100,101,133
+1969-12-30 00:00:00,1969-12-29 23:00:00,182,155,102,0,100,99,99,100
+1969-12-30 00:00:00,1969-12-29 23:00:00,200,101,168,128,101,100,100,175
+1969-12-30 00:00:00,1969-12-29 23:00:00,181,178,174,121,100,101,99,133
+1969-12-30 00:00:00,1969-12-29 23:00:00,116,182,167,177,101,101,101,113
+1969-12-30 00:00:00,1969-12-29 23:00:00,164,147,198,149,99,101,99,187
+1969-12-30 00:00:00,1969-12-29 23:00:00,169,177,134,122,99,100,101,117
+1969-12-30 00:00:00,1969-12-29 23:00:00,181,193,146,146,99,101,99,174
+1969-12-30 00:00:00,1969-12-29 23:00:00,125,100,147,148,100,101,100,113
+1969-12-30 00:00:00,1969-12-29 23:00:00,133,191,158,197,100,101,100,169
+1969-12-30 00:00:00,1969-12-29 23:00:00,191,172,107,132,99,99,99,179
+1969-12-30 00:00:00,1969-12-29 23:00:00,156,198,150,171,100,101,99,174
+1969-12-30 00:00:00,1969-12-29 23:00:00,189,102,124,108,100,101,99,166
+1969-12-30 00:00:00,1969-12-29 23:00:00,193,105,124,146,101,99,100,133
+1969-12-30 00:00:00,1969-12-29 23:00:00,146,132,150,200,99,99,101,186
+1969-12-30 00:00:00,1969-12-29 23:00:00,182,183,175,0,100,99,99,159
+1969-12-30 00:00:00,1969-12-29 23:00:00,182,188,126,113,99,99,100,184
+1969-12-30 00:00:00,1969-12-29 23:00:00,115,110,188,199,99,100,99,160
+1969-12-30 00:00:00,1969-12-29 23:00:00,163,161,190,0,99,100,101,116
+1969-12-30 00:00:00,1969-12-29 23:00:00,104,191,165,140,100,100,100,178
+1969-12-30 00:00:00,1969-12-29 23:00:00,146,101,137,0,101,99,99,184
+1969-12-30 00:00:00,1969-12-29 23:00:00,185,162,147,131,101,101,100,188
+1969-12-30 00:00:00,1969-12-29 23:00:00,101,174,164,125,100,101,99,119
+1969-12-30 00:00:00,1969-12-29 23:00:00,111,173,169,0,99,101,99,156
+1969-12-30 00:00:00,1969-12-29 23:00:00,128,165,185,107,99,99,101,184
+1969-12-30 00:00:00,1969-12-29 23:00:00,129,160,115,123,100,101,99,126
+1969-12-30 00:00:00,1969-12-29 23:00:00,189,187,129,121,100,101,99,180
+1969-12-30 00:00:00,1969-12-29 23:00:00,105,138,177,0,99,99,101,177
+1969-12-30 00:00:00,1969-12-29 23:00:00,114,136,185,146,101,101,99,125
+1969-12-30 00:00:00,1969-12-29 23:00:00,103,185,185,149,99,100,100,195
+1969-12-30 00:00:00,1969-12-29 23:00:00,188,119,157,118,100,101,100,162
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+1969-12-30 00:00:00,1969-12-29 23:00:00,180,118,172,144,101,101,100,182
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+1969-12-30 00:00:00,1969-12-29 23:00:00,190,197,150,114,101,101,100,164
+1969-12-30 00:00:00,1969-12-29 23:00:00,141,166,107,136,101,101,101,114
+1969-12-30 00:00:00,1969-12-29 23:00:00,160,118,165,134,100,100,99,165
+1969-12-30 00:00:00,1969-12-29 23:00:00,166,108,136,126,101,99,99,130
+1969-12-30 00:00:00,1969-12-29 23:00:00,120,198,170,0,101,99,101,193
+1969-12-30 00:00:00,1969-12-29 23:00:00,180,109,180,109,99,100,99,107
+1969-12-30 00:00:00,1969-12-29 23:00:00,196,140,143,147,100,99,101,131
+1969-12-30 00:00:00,1969-12-29 23:00:00,174,171,110,112,100,100,100,121
+1969-12-30 00:00:00,1969-12-29 23:00:00,111,191,124,158,100,99,100,144
+1969-12-30 00:00:00,1969-12-29 23:00:00,164,168,186,118,99,100,101,186
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diff --git a/tests/e2e_dbt_project/data/validation/numeric_column_anomalies_validation.csv b/tests/e2e_dbt_project/data/validation/numeric_column_anomalies_validation.csv
index e69de29bb..2eb2e3978 100644
--- a/tests/e2e_dbt_project/data/validation/numeric_column_anomalies_validation.csv
+++ b/tests/e2e_dbt_project/data/validation/numeric_column_anomalies_validation.csv
@@ -0,0 +1,201 @@
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+1969-12-31 00:00:00,1969-12-30 17:00:00,188,300,0,198,106,102,111,306
+1969-12-31 00:00:00,1969-12-30 17:00:00,13,272,0,0,103,92,101,330
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diff --git a/tests/e2e_dbt_project/dbt_project.yml b/tests/e2e_dbt_project/dbt_project.yml
index a3a2d135d..4562c2c20 100644
--- a/tests/e2e_dbt_project/dbt_project.yml
+++ b/tests/e2e_dbt_project/dbt_project.yml
@@ -27,5 +27,9 @@ seeds:
   +schema: test_seeds
 
 models:
+  elementary_integration_tests:
+    +file_format: "{{ 'delta' if target.type == 'spark' else none }}"
+
   elementary:
     +schema: elementary
+    +file_format: "{{ 'delta' if target.type == 'spark' else none }}"
diff --git a/tests/e2e_dbt_project/docker-compose.yml b/tests/e2e_dbt_project/docker-compose.yml
index 97a3152ac..47b4c4bb7 100644
--- a/tests/e2e_dbt_project/docker-compose.yml
+++ b/tests/e2e_dbt_project/docker-compose.yml
@@ -7,27 +7,296 @@ services:
       - "127.0.0.1:5432:5432"
     command: postgres -c max_connections=500
     environment:
-      POSTGRES_USER: admin
-      POSTGRES_PASSWORD: admin
+      POSTGRES_USER: ${POSTGRES_USER:-admin}
+      POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:-admin}
     volumes:
       - postgres:/var/lib/postgresql/data
+    healthcheck:
+      test: ["CMD-SHELL", "pg_isready -U ${POSTGRES_USER:-admin}"]
+      interval: 5s
+      timeout: 5s
+      retries: 10
 
   clickhouse:
-    image: clickhouse/clickhouse-server:latest
+    image: clickhouse/clickhouse-server:24.3
     container_name: clickhouse
     ports:
       - "8123:8123"
       - "9000:9000"
     volumes:
-      - ./clickhouse-data:/var/lib/clickhouse
+      - clickhouse-data:/var/lib/clickhouse
     environment:
       CLICKHOUSE_DB: default
       CLICKHOUSE_USER: default
-      CLICKHOUSE_PASSWORD: "default"
+      CLICKHOUSE_PASSWORD: ${CLICKHOUSE_PASSWORD:-default}
     ulimits:
       nofile:
         soft: 262144
         hard: 262144
+    healthcheck:
+      test: ["CMD-SHELL", "curl -sf http://localhost:8123/ping"]
+      interval: 5s
+      timeout: 5s
+      retries: 10
+
+  # ── Trino (with Iceberg + Hive Metastore + MinIO) ──────────────────
+  trino:
+    image: "trinodb/trino:439"
+    ports:
+      - "8086:8080"
+    volumes:
+      - ./docker/trino/etc:/usr/lib/trino/etc:ro
+      - ./docker/trino/catalog:/etc/trino/catalog
+    depends_on:
+      hive-metastore:
+        condition: service_healthy
+      trino-mc-job:
+        condition: service_completed_successfully
+    healthcheck:
+      test:
+        [
+          "CMD-SHELL",
+          'curl -sf http://localhost:8080/v1/info | grep -q ''"starting":false''',
+        ]
+      interval: 5s
+      timeout: 5s
+      retries: 60
+      start_period: 10s
+
+  trino-metastore-db:
+    image: postgres:11
+    hostname: metastore_db
+    environment:
+      POSTGRES_USER: ${HIVE_METASTORE_USER:-hive}
+      POSTGRES_PASSWORD: ${HIVE_METASTORE_PASSWORD:-hive}
+      POSTGRES_DB: metastore
+    healthcheck:
+      test: ["CMD-SHELL", "pg_isready -U ${HIVE_METASTORE_USER:-hive}"]
+      interval: 5s
+      timeout: 5s
+      retries: 10
+
+  hive-metastore:
+    hostname: hive-metastore
+    image: "starburstdata/hive:3.1.2-e.18"
+    ports:
+      - "9083:9083"
+    environment:
+      HIVE_METASTORE_DRIVER: org.postgresql.Driver
+      HIVE_METASTORE_JDBC_URL: jdbc:postgresql://metastore_db:5432/metastore
+      HIVE_METASTORE_USER: ${HIVE_METASTORE_USER:-hive}
+      HIVE_METASTORE_PASSWORD: ${HIVE_METASTORE_PASSWORD:-hive}
+      HIVE_METASTORE_WAREHOUSE_DIR: s3://datalake/
+      S3_ENDPOINT: http://trino-minio:9000
+      S3_ACCESS_KEY: ${TRINO_MINIO_ACCESS_KEY:-minio}
+      S3_SECRET_KEY: ${TRINO_MINIO_SECRET_KEY:-minio123}
+      S3_PATH_STYLE_ACCESS: "true"
+      REGION: ""
+      GOOGLE_CLOUD_KEY_FILE_PATH: ""
+      AZURE_ADL_CLIENT_ID: ""
+      AZURE_ADL_CREDENTIAL: ""
+      AZURE_ADL_REFRESH_URL: ""
+      AZURE_ABFS_STORAGE_ACCOUNT: ""
+      AZURE_ABFS_ACCESS_KEY: ""
+      AZURE_WASB_STORAGE_ACCOUNT: ""
+      AZURE_ABFS_OAUTH: ""
+      AZURE_ABFS_OAUTH_TOKEN_PROVIDER: ""
+      AZURE_ABFS_OAUTH_CLIENT_ID: ""
+      AZURE_ABFS_OAUTH_SECRET: ""
+      AZURE_ABFS_OAUTH_ENDPOINT: ""
+      AZURE_WASB_ACCESS_KEY: ""
+      HIVE_METASTORE_USERS_IN_ADMIN_ROLE: "admin"
+    depends_on:
+      trino-metastore-db:
+        condition: service_healthy
+      trino-minio:
+        condition: service_healthy
+    healthcheck:
+      test: ["CMD-SHELL", "bash -c 'echo > /dev/tcp/localhost/9083'"]
+      interval: 10s
+      timeout: 5s
+      retries: 60
+      start_period: 60s
+
+  trino-minio:
+    hostname: minio
+    image: "minio/minio:RELEASE.2022-05-26T05-48-41Z"
+    ports:
+      - "9002:9000"
+      - "9003:9001"
+    environment:
+      MINIO_ACCESS_KEY: ${TRINO_MINIO_ACCESS_KEY:-minio}
+      MINIO_SECRET_KEY: ${TRINO_MINIO_SECRET_KEY:-minio123}
+    command: server /data --console-address ":9001"
+    healthcheck:
+      test: ["CMD-SHELL", "curl -sf http://localhost:9000/minio/health/live"]
+      interval: 5s
+      timeout: 5s
+      retries: 10
+
+  trino-mc-job:
+    image: "minio/mc:RELEASE.2022-05-09T04-08-26Z"
+    entrypoint: |
+      /bin/bash -c "
+      until /usr/bin/mc config --quiet host add myminio http://minio:9000 $${TRINO_MINIO_ACCESS_KEY:-minio} $${TRINO_MINIO_SECRET_KEY:-minio123}; do
+        sleep 2;
+      done;
+      /usr/bin/mc mb --quiet --ignore-existing myminio/datalake
+      "
+    depends_on:
+      trino-minio:
+        condition: service_healthy
+
+  # ── Dremio (with Nessie + MinIO) ───────────────────────────────────
+  nessie:
+    image: ghcr.io/projectnessie/nessie:0.80.0
+    container_name: catalog
+    networks:
+      - dremio-lakehouse
+    ports:
+      - "19120:19120"
+    healthcheck:
+      test:
+        ["CMD-SHELL", "curl -sf http://localhost:19120/api/v2/config || exit 1"]
+      interval: 5s
+      timeout: 5s
+      retries: 10
+
+  dremio-minio:
+    image: minio/minio:RELEASE.2024-01-18T22-51-28Z
+    container_name: dremio-storage
+    environment:
+      - MINIO_ROOT_USER=${DREMIO_MINIO_ROOT_USER:-admin}
+      - MINIO_ROOT_PASSWORD=${DREMIO_MINIO_ROOT_PASSWORD:-password}
+      - MINIO_DOMAIN=storage
+      - MINIO_REGION_NAME=us-east-1
+      - MINIO_REGION=us-east-1
+    networks:
+      - dremio-lakehouse
+    ports:
+      - "9004:9000"
+      - "9005:9001"
+    command: ["server", "/data", "--console-address", ":9001"]
+    volumes:
+      - dremio-minio-data:/data
+    healthcheck:
+      test: ["CMD-SHELL", "bash -c 'echo > /dev/tcp/localhost/9000'"]
+      interval: 5s
+      timeout: 5s
+      retries: 10
+
+  dremio-minio-setup:
+    image: minio/mc:RELEASE.2024-01-18T07-03-39Z
+    container_name: dremio-minio-setup
+    depends_on:
+      dremio-minio:
+        condition: service_healthy
+    entrypoint: >
+      /bin/sh -c "
+      mc alias set myminio http://dremio-storage:9000 $${DREMIO_MINIO_ROOT_USER:-admin} $${DREMIO_MINIO_ROOT_PASSWORD:-password};
+      mc mb --ignore-existing myminio/datalake;
+      mc ls myminio;
+      "
+    networks:
+      - dremio-lakehouse
+
+  dremio:
+    image: dremio/dremio-oss:25.1.0
+    platform: linux/amd64
+    ports:
+      - "9047:9047"
+      - "31010:31010"
+      - "32010:32010"
+      - "45678:45678"
+    container_name: dremio
+    environment:
+      - DREMIO_JAVA_SERVER_EXTRA_OPTS=-Dpaths.dist=file:///opt/dremio/data/dist -Ddebug.addDefaultUser=true
+      - SERVICES_COORDINATOR_ENABLED=true
+      - SERVICES_EXECUTOR_ENABLED=true
+    networks:
+      - dremio-lakehouse
+    volumes:
+      - dremio-data:/opt/dremio/data:rw
+    user: "0"
+    healthcheck:
+      test: ["CMD-SHELL", "curl -sf http://localhost:9047 || exit 1"]
+      interval: 5s
+      timeout: 5s
+      retries: 60
+      start_period: 15s
+
+  dremio-setup:
+    image: alpine:3.19
+    container_name: dremio-setup
+    depends_on:
+      dremio:
+        condition: service_healthy
+      nessie:
+        condition: service_healthy
+      dremio-minio-setup:
+        condition: service_completed_successfully
+    environment:
+      DREMIO_USER: ${DREMIO_USER:-dremio}
+      DREMIO_PASS: ${DREMIO_PASS:-dremio123}
+      MINIO_ROOT_USER: ${DREMIO_MINIO_ROOT_USER:-admin}
+      MINIO_ROOT_PASSWORD: ${DREMIO_MINIO_ROOT_PASSWORD:-password}
+    volumes:
+      - ./docker/dremio/dremio-setup.sh:/dremio-setup.sh
+    command: sh /dremio-setup.sh
+    networks:
+      - dremio-lakehouse
+
+  # ── Spark Thrift Server (with Delta Lake + Hive Metastore) ─────────
+  spark-thrift:
+    container_name: spark-thrift
+    build:
+      context: ./docker/spark
+      dockerfile: Dockerfile
+    ports:
+      - "10000:10000"
+      - "4040:4040"
+    depends_on:
+      spark-hive-metastore:
+        condition: service_healthy
+    command: >
+      --class org.apache.spark.sql.hive.thriftserver.HiveThriftServer2
+      --name Thrift JDBC/ODBC Server
+    healthcheck:
+      test: ["CMD-SHELL", "nc -z localhost 10000"]
+      interval: 10s
+      timeout: 5s
+      retries: 20
+      start_period: 30s
+    user: "0"
+    volumes:
+      - spark-warehouse:/spark-warehouse/
+      - ./docker/spark/hive-site.xml:/usr/spark/conf/hive-site.xml
+      - ./docker/spark/spark-defaults.conf:/usr/spark/conf/spark-defaults.conf
+      - ./data:/seed-data:ro
+    environment:
+      - WAIT_FOR=spark-hive-metastore:5432
+
+  spark-hive-metastore:
+    image: postgres:15-alpine
+    volumes:
+      - spark-hive-metastore:/var/lib/postgresql/data
+    environment:
+      - POSTGRES_USER=${SPARK_METASTORE_USER:-dbt}
+      - POSTGRES_PASSWORD=${SPARK_METASTORE_PASSWORD:-dbt}
+      - POSTGRES_DB=metastore
+    healthcheck:
+      test: ["CMD-SHELL", "pg_isready -U ${SPARK_METASTORE_USER:-dbt}"]
+      interval: 5s
+      timeout: 5s
+      retries: 10
+
+networks:
+  dremio-lakehouse:
 
 volumes:
   postgres:
+  clickhouse-data:
+  dremio-data:
+  dremio-minio-data:
+  spark-warehouse:
+  spark-hive-metastore:
diff --git a/tests/e2e_dbt_project/docker/dremio/dremio-setup.sh b/tests/e2e_dbt_project/docker/dremio/dremio-setup.sh
new file mode 100644
index 000000000..eb77d9210
--- /dev/null
+++ b/tests/e2e_dbt_project/docker/dremio/dremio-setup.sh
@@ -0,0 +1,51 @@
+#!/bin/sh
+set -e
+
+# Install required tools
+apk add --no-cache curl jq
+
+# Wait for Dremio to be ready (bounded to avoid CI hangs)
+max_attempts=60
+attempt=1
+until curl --silent --fail --max-time 3 http://dremio:9047 >/dev/null; do
+  if [ "$attempt" -ge "$max_attempts" ]; then
+    echo "Dremio did not become ready after $max_attempts attempts" >&2
+    exit 1
+  fi
+  echo "Waiting for Dremio... ($attempt/$max_attempts)"
+  sleep 5
+  attempt=$((attempt + 1))
+done
+
+echo "Dremio is up. Proceeding with configuration..."
+
+# Log in to Dremio to get the auth token
+# Credentials are passed via environment variables from docker-compose.yml
+DREMIO_USER="${DREMIO_USER:-dremio}"
+DREMIO_PASS="${DREMIO_PASS:-dremio123}"
+AUTH_TOKEN=$(curl -s -X POST "http://dremio:9047/apiv2/login" \
+  -H "Content-Type: application/json" \
+  --data "{\"userName\":\"$DREMIO_USER\", \"password\":\"$DREMIO_PASS\"}" | jq -r '.token // empty')
+
+# Check if AUTH_TOKEN is not empty or null
+if [ -z "$AUTH_TOKEN" ] || [ "$AUTH_TOKEN" = "null" ]; then
+  echo "Failed to obtain Dremio auth token"
+  exit 1
+fi
+
+echo "Obtained Dremio auth token"
+
+# Create a Nessie catalog source in Dremio (used as "database" for views)
+HTTP_CODE=$(curl -s -o /tmp/dremio_source_response.json -w "%{http_code}" -X PUT "http://dremio:9047/apiv2/source/NessieSource" \
+  -H "Content-Type: application/json" \
+  -H "Authorization: _dremio$AUTH_TOKEN" \
+  --data "{\"name\":\"NessieSource\",\"config\":{\"nessieEndpoint\":\"http://catalog:19120/api/v2\",\"nessieAuthType\":\"NONE\",\"credentialType\":\"ACCESS_KEY\",\"awsAccessKey\":\"${MINIO_ROOT_USER:-admin}\",\"awsAccessSecret\":\"${MINIO_ROOT_PASSWORD:-password}\",\"awsRootPath\":\"datalake\",\"secure\":false,\"propertyList\":[{\"name\":\"fs.s3a.path.style.access\",\"value\":\"true\"},{\"name\":\"fs.s3a.endpoint\",\"value\":\"dremio-storage:9000\"},{\"name\":\"dremio.s3.compat\",\"value\":\"true\"}]},\"type\":\"NESSIE\",\"metadataPolicy\":{\"deleteUnavailableDatasets\":true,\"autoPromoteDatasets\":false,\"namesRefreshMillis\":3600000,\"datasetDefinitionRefreshAfterMillis\":3600000,\"datasetDefinitionExpireAfterMillis\":10800000,\"authTTLMillis\":86400000,\"updateMode\":\"PREFETCH_QUERIED\"}}")
+
+
+if [ "$HTTP_CODE" -lt 200 ] || [ "$HTTP_CODE" -ge 300 ]; then
+  echo "Failed to create Nessie Source in Dremio (HTTP $HTTP_CODE)"
+  cat /tmp/dremio_source_response.json
+  exit 1
+fi
+
+echo "Nessie Source created in Dremio"
diff --git a/tests/e2e_dbt_project/docker/spark/Dockerfile b/tests/e2e_dbt_project/docker/spark/Dockerfile
new file mode 100644
index 000000000..aab2ab394
--- /dev/null
+++ b/tests/e2e_dbt_project/docker/spark/Dockerfile
@@ -0,0 +1,33 @@
+ARG OPENJDK_VERSION=8
+FROM eclipse-temurin:${OPENJDK_VERSION}-jre
+
+ARG SPARK_VERSION=3.3.2
+ARG HADOOP_VERSION=3
+ARG DELTA_VERSION=2.2.0
+
+ENV SPARK_HOME /usr/spark
+ENV PATH="/usr/spark/bin:/usr/spark/sbin:${PATH}"
+
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends wget netcat-openbsd procps libpostgresql-jdbc-java && \
+    wget -q "https://archive.apache.org/dist/spark/spark-${SPARK_VERSION}/spark-${SPARK_VERSION}-bin-hadoop${HADOOP_VERSION}.tgz" && \
+    tar xzf "spark-${SPARK_VERSION}-bin-hadoop${HADOOP_VERSION}.tgz" && \
+    rm "spark-${SPARK_VERSION}-bin-hadoop${HADOOP_VERSION}.tgz" && \
+    mv "spark-${SPARK_VERSION}-bin-hadoop${HADOOP_VERSION}" /usr/spark && \
+    ln -s /usr/share/java/postgresql-jdbc4.jar /usr/spark/jars/postgresql-jdbc4.jar && \
+    wget -q "https://repo1.maven.org/maven2/io/delta/delta-core_2.12/${DELTA_VERSION}/delta-core_2.12-${DELTA_VERSION}.jar" \
+         -P /usr/spark/jars/ && \
+    wget -q "https://repo1.maven.org/maven2/io/delta/delta-storage/${DELTA_VERSION}/delta-storage-${DELTA_VERSION}.jar" \
+         -P /usr/spark/jars/ && \
+    apt-get remove -y wget && \
+    apt-get autoremove -y && \
+    apt-get clean
+
+COPY entrypoint.sh /scripts/
+RUN chmod +x /scripts/entrypoint.sh && \
+    groupadd -r spark && useradd -r -g spark -m spark && \
+    chown -R spark:spark /usr/spark /scripts
+
+USER spark
+ENTRYPOINT ["/scripts/entrypoint.sh"]
+CMD ["--help"]
diff --git a/tests/e2e_dbt_project/docker/spark/entrypoint.sh b/tests/e2e_dbt_project/docker/spark/entrypoint.sh
new file mode 100644
index 000000000..36b8174c9
--- /dev/null
+++ b/tests/e2e_dbt_project/docker/spark/entrypoint.sh
@@ -0,0 +1,23 @@
+#!/bin/bash
+
+if [ -n "$WAIT_FOR" ]; then
+    WAIT_FOR_TIMEOUT_SECONDS="${WAIT_FOR_TIMEOUT_SECONDS:-180}"
+    IFS=';' read -a HOSTPORT_ARRAY <<< "$WAIT_FOR"
+    for HOSTPORT in "${HOSTPORT_ARRAY[@]}"
+    do
+        WAIT_FOR_HOST=${HOSTPORT%:*}
+        WAIT_FOR_PORT=${HOSTPORT#*:}
+        START_TS=$(date +%s)
+
+        echo Waiting for $WAIT_FOR_HOST to listen on $WAIT_FOR_PORT...
+        while ! nc -z "$WAIT_FOR_HOST" "$WAIT_FOR_PORT"; do
+            if [ $(( $(date +%s) - START_TS )) -ge "$WAIT_FOR_TIMEOUT_SECONDS" ]; then
+                echo "Timed out waiting for $WAIT_FOR_HOST:$WAIT_FOR_PORT after ${WAIT_FOR_TIMEOUT_SECONDS}s"
+                exit 1
+            fi
+            sleep 2
+        done
+    done
+fi
+
+exec spark-submit "$@"
diff --git a/tests/e2e_dbt_project/docker/spark/hive-site.xml b/tests/e2e_dbt_project/docker/spark/hive-site.xml
new file mode 100644
index 000000000..de4e50685
--- /dev/null
+++ b/tests/e2e_dbt_project/docker/spark/hive-site.xml
@@ -0,0 +1,26 @@
+<configuration>
+    <property>
+        <name>javax.jdo.option.ConnectionURL</name>
+        <value>jdbc:postgresql://spark-hive-metastore/metastore</value>
+    </property>
+
+    <property>
+        <name>javax.jdo.option.ConnectionDriverName</name>
+        <value>org.postgresql.Driver</value>
+    </property>
+
+    <property>
+        <name>javax.jdo.option.ConnectionUserName</name>
+        <value>dbt</value>
+    </property>
+
+    <property>
+        <name>javax.jdo.option.ConnectionPassword</name>
+        <value>dbt</value>
+    </property>
+
+    <property>
+        <name>hive.metastore.schema.verification</name>
+        <value>false</value>
+    </property>
+</configuration>
diff --git a/tests/e2e_dbt_project/docker/spark/spark-defaults.conf b/tests/e2e_dbt_project/docker/spark/spark-defaults.conf
new file mode 100644
index 000000000..80caa2162
--- /dev/null
+++ b/tests/e2e_dbt_project/docker/spark/spark-defaults.conf
@@ -0,0 +1,14 @@
+spark.driver.memory                    2g
+spark.executor.memory                  2g
+spark.executor.cores                   1
+spark.hadoop.datanucleus.autoCreateTables       true
+spark.hadoop.datanucleus.schema.autoCreateTables true
+spark.hadoop.datanucleus.fixedDatastore          false
+spark.serializer                       org.apache.spark.serializer.KryoSerializer
+spark.driver.userClassPathFirst        true
+spark.sql.extensions                   io.delta.sql.DeltaSparkSessionExtension
+spark.sql.catalog.spark_catalog        org.apache.spark.sql.delta.catalog.DeltaCatalog
+spark.sql.shuffle.partitions           2
+spark.default.parallelism              2
+spark.ui.enabled                       false
+spark.sql.adaptive.enabled             true
diff --git a/tests/e2e_dbt_project/docker/trino/catalog/iceberg.properties b/tests/e2e_dbt_project/docker/trino/catalog/iceberg.properties
new file mode 100644
index 000000000..c43f07e7f
--- /dev/null
+++ b/tests/e2e_dbt_project/docker/trino/catalog/iceberg.properties
@@ -0,0 +1,9 @@
+connector.name=iceberg
+hive.metastore.uri=thrift://hive-metastore:9083
+hive.s3.endpoint=http://minio:9000
+hive.s3.path-style-access=true
+hive.s3.aws-access-key=minio
+hive.s3.aws-secret-key=minio123
+hive.metastore-cache-ttl=0s
+hive.metastore-refresh-interval=5s
+hive.metastore-timeout=60s
diff --git a/tests/e2e_dbt_project/docker/trino/catalog/memory.properties b/tests/e2e_dbt_project/docker/trino/catalog/memory.properties
new file mode 100644
index 000000000..56b3b5e6a
--- /dev/null
+++ b/tests/e2e_dbt_project/docker/trino/catalog/memory.properties
@@ -0,0 +1,2 @@
+connector.name=memory
+memory.max-data-per-node=128MB
diff --git a/tests/e2e_dbt_project/docker/trino/etc/config.properties b/tests/e2e_dbt_project/docker/trino/etc/config.properties
new file mode 100644
index 000000000..0b4b617ce
--- /dev/null
+++ b/tests/e2e_dbt_project/docker/trino/etc/config.properties
@@ -0,0 +1,4 @@
+coordinator=true
+node-scheduler.include-coordinator=true
+http-server.http.port=8080
+discovery.uri=http://localhost:8080
diff --git a/tests/e2e_dbt_project/docker/trino/etc/jvm.config b/tests/e2e_dbt_project/docker/trino/etc/jvm.config
new file mode 100644
index 000000000..087fd09e0
--- /dev/null
+++ b/tests/e2e_dbt_project/docker/trino/etc/jvm.config
@@ -0,0 +1,13 @@
+-server
+-XX:InitialRAMPercentage=80
+-XX:MaxRAMPercentage=80
+-XX:G1HeapRegionSize=32M
+-XX:+ExplicitGCInvokesConcurrent
+-XX:+HeapDumpOnOutOfMemoryError
+-XX:+ExitOnOutOfMemoryError
+-XX:-OmitStackTraceInFastThrow
+-XX:ReservedCodeCacheSize=256M
+-XX:PerMethodRecompilationCutoff=10000
+-XX:PerBytecodeRecompilationCutoff=10000
+-Djdk.attach.allowAttachSelf=true
+-Djdk.nio.maxCachedBufferSize=2000000
diff --git a/tests/e2e_dbt_project/docker/trino/etc/node.properties b/tests/e2e_dbt_project/docker/trino/etc/node.properties
new file mode 100644
index 000000000..5b02ff7f0
--- /dev/null
+++ b/tests/e2e_dbt_project/docker/trino/etc/node.properties
@@ -0,0 +1,2 @@
+node.environment=docker
+node.data-dir=/data/trino
diff --git a/tests/e2e_dbt_project/external_seeders/__init__.py b/tests/e2e_dbt_project/external_seeders/__init__.py
new file mode 100644
index 000000000..f7707b5ac
--- /dev/null
+++ b/tests/e2e_dbt_project/external_seeders/__init__.py
@@ -0,0 +1,5 @@
+from external_seeders.base import ExternalSeeder
+from external_seeders.dremio import DremioExternalSeeder
+from external_seeders.spark import SparkExternalSeeder
+
+__all__ = ["ExternalSeeder", "DremioExternalSeeder", "SparkExternalSeeder"]
diff --git a/tests/e2e_dbt_project/external_seeders/base.py b/tests/e2e_dbt_project/external_seeders/base.py
new file mode 100644
index 000000000..c79de9be2
--- /dev/null
+++ b/tests/e2e_dbt_project/external_seeders/base.py
@@ -0,0 +1,67 @@
+"""Base class for external seed loaders."""
+
+from __future__ import annotations
+
+import csv
+import glob
+import os
+import shlex
+import subprocess
+from abc import ABC, abstractmethod
+
+
+class ExternalSeeder(ABC):
+    """Common interface for loading seed CSVs into a warehouse externally."""
+
+    def __init__(self, data_dir: str, schema_name: str) -> None:
+        self.data_dir = os.path.abspath(data_dir)
+        self.schema_name = schema_name
+
+    # ------------------------------------------------------------------
+    # Helpers
+    # ------------------------------------------------------------------
+
+    @staticmethod
+    def run(cmd: list[str], check: bool = True, **kw: object) -> subprocess.CompletedProcess:  # type: ignore[type-arg]
+        """Run a command, printing it first."""
+        print(f"  -> {shlex.join(cmd)}")
+        return subprocess.run(cmd, check=check, **kw)
+
+    @staticmethod
+    def csv_has_data(path: str) -> bool:
+        """Return *True* when the CSV has a header AND at least one data row."""
+        with open(path) as f:
+            reader = csv.reader(f)
+            try:
+                next(reader)  # header
+                next(reader)  # first data row
+                return True
+            except StopIteration:
+                return False
+
+    @staticmethod
+    def csv_columns(path: str) -> list[str]:
+        """Return the header row of a CSV (empty list when file is empty)."""
+        with open(path) as f:
+            reader = csv.reader(f)
+            try:
+                return next(reader)
+            except StopIteration:
+                return []
+
+    def iter_seed_csvs(self):
+        """Yield ``(subdir, csv_path, table_name)`` for every seed CSV."""
+        for subdir in ("training", "validation"):
+            csv_dir = os.path.join(self.data_dir, subdir)
+            for csv_path in sorted(glob.glob(os.path.join(csv_dir, "*.csv"))):
+                fname = os.path.basename(csv_path)
+                table_name, _ = os.path.splitext(fname)
+                yield subdir, csv_path, table_name
+
+    # ------------------------------------------------------------------
+    # Public API
+    # ------------------------------------------------------------------
+
+    @abstractmethod
+    def load(self) -> None:
+        """Load all seed CSVs into the target warehouse."""
diff --git a/tests/e2e_dbt_project/external_seeders/dremio.py b/tests/e2e_dbt_project/external_seeders/dremio.py
new file mode 100644
index 000000000..949e8bdaa
--- /dev/null
+++ b/tests/e2e_dbt_project/external_seeders/dremio.py
@@ -0,0 +1,422 @@
+"""Dremio external seed loader – uploads CSVs to MinIO and creates Iceberg tables."""
+
+from __future__ import annotations
+
+import os
+import re
+import time
+
+from external_seeders.base import ExternalSeeder
+from ruamel.yaml import YAML
+
+
+def _docker_defaults() -> dict[str, str]:
+    """Read default credentials from docker-compose.yml and dremio-setup.sh.
+
+    These are local Docker test credentials, not production secrets.
+    """
+    project_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+    defaults: dict[str, str] = {}
+
+    # --- docker-compose.yml: MinIO credentials ---
+    compose_path = os.path.join(project_dir, "docker-compose.yml")
+    try:
+        _yaml = YAML()
+        with open(compose_path) as fh:
+            cfg = _yaml.load(fh)
+        services = cfg.get("services", {})
+        for item in services.get("dremio-minio", {}).get("environment", []):
+            if isinstance(item, str) and "=" in item:
+                k, v = item.split("=", 1)
+                # Resolve ${VAR:-default} patterns to just the default value
+                m = re.match(r"\$\{[^:}]+:-([^}]+)\}", v)
+                if m:
+                    v = m.group(1)
+                if k == "MINIO_ROOT_USER":
+                    defaults["MINIO_ACCESS_KEY"] = v
+                elif k == "MINIO_ROOT_PASSWORD":
+                    defaults["MINIO_SECRET_KEY"] = v
+    except FileNotFoundError:
+        pass
+    except Exception as e:
+        print(f"  Warning: failed parsing docker defaults from {compose_path}: {e}")
+
+    # --- dremio-setup.sh: Dremio login credentials ---
+    # Extract default values from bash variable assignments like:
+    #   DREMIO_PASS="${DREMIO_PASS:-dremio123}"
+    setup_path = os.path.join(project_dir, "docker", "dremio", "dremio-setup.sh")
+    try:
+        with open(setup_path) as fh:
+            content = fh.read()
+        m = re.search(r'DREMIO_PASS="\$\{DREMIO_PASS:-([^}]+)\}"', content)
+        if m:
+            defaults["DREMIO_PASS"] = m.group(1)
+        m = re.search(r'DREMIO_USER="\$\{DREMIO_USER:-([^}]+)\}"', content)
+        if m:
+            defaults["DREMIO_USER"] = m.group(1)
+    except FileNotFoundError:
+        pass
+    except Exception as e:
+        print(f"  Warning: failed parsing dremio defaults from {setup_path}: {e}")
+
+    return defaults
+
+
+class DremioExternalSeeder(ExternalSeeder):
+    """Load seeds into Dremio via MinIO S3 + Nessie Iceberg tables.
+
+    Credentials are read from environment variables.  If not set, defaults
+    are extracted from the sibling ``docker-compose.yml`` file so the script
+    works out-of-the-box in the CI Docker environment.
+    """
+
+    HTTP_TIMEOUT = (5, 30)  # (connect, read) in seconds
+
+    def __init__(self, data_dir: str, schema_name: str) -> None:
+        super().__init__(data_dir, schema_name)
+        _defaults = _docker_defaults()
+        self.dremio_host = os.environ.get("DREMIO_HOST", "localhost")
+        self.dremio_port = int(os.environ.get("DREMIO_PORT", "9047"))
+        self.dremio_user = os.environ.get(
+            "DREMIO_USER", _defaults.get("DREMIO_USER", "dremio")
+        )
+        self.dremio_pass = os.environ.get(
+            "DREMIO_PASS", _defaults.get("DREMIO_PASS", "")
+        )
+        self.minio_access_key = os.environ.get(
+            "MINIO_ACCESS_KEY", _defaults.get("MINIO_ACCESS_KEY", "")
+        )
+        self.minio_secret_key = os.environ.get(
+            "MINIO_SECRET_KEY", _defaults.get("MINIO_SECRET_KEY", "")
+        )
+
+    # ------------------------------------------------------------------
+    # Helpers
+    # ------------------------------------------------------------------
+
+    @staticmethod
+    def _quote_ident(name: str) -> str:
+        """Double-quote a SQL identifier, escaping embedded double-quotes."""
+        return '"' + name.replace('"', '""') + '"'
+
+    def _headers(self, token: str) -> dict[str, str]:
+        return {
+            "Content-Type": "application/json",
+            "Authorization": f"_dremio{token}",
+        }
+
+    def _get_token(self) -> str:
+        import requests
+
+        resp = requests.post(
+            f"http://{self.dremio_host}:{self.dremio_port}/apiv2/login",
+            json={"userName": self.dremio_user, "password": self.dremio_pass},
+            timeout=self.HTTP_TIMEOUT,
+        )
+        resp.raise_for_status()
+        return resp.json()["token"]
+
+    def _sql(self, token: str, sql: str, *, timeout: int = 120) -> dict:
+        """Execute SQL on Dremio and block until the job finishes."""
+        import requests
+
+        headers = self._headers(token)
+        resp = requests.post(
+            f"http://{self.dremio_host}:{self.dremio_port}/api/v3/sql",
+            headers=headers,
+            json={"sql": sql},
+            timeout=self.HTTP_TIMEOUT,
+        )
+        resp.raise_for_status()
+        job_id = resp.json()["id"]
+
+        deadline = time.time() + timeout
+        while time.time() < deadline:
+            resp = requests.get(
+                f"http://{self.dremio_host}:{self.dremio_port}/api/v3/job/{job_id}",
+                headers=headers,
+                timeout=self.HTTP_TIMEOUT,
+            )
+            resp.raise_for_status()
+            state = resp.json()["jobState"]
+            if state == "COMPLETED":
+                return resp.json()
+            if state in ("FAILED", "CANCELED", "CANCELLED"):
+                raise RuntimeError(
+                    f"Dremio SQL failed ({state}): {resp.json()}\nSQL: {sql}"
+                )
+            time.sleep(1)
+        raise TimeoutError(f"Dremio job {job_id} timed out after {timeout}s")
+
+    # ------------------------------------------------------------------
+    # Nessie namespace creation
+    # ------------------------------------------------------------------
+
+    def _create_nessie_namespace(self, token: str, namespace: str) -> None:
+        """Create a Nessie namespace via Dremio SQL.
+
+        Uses ``CREATE FOLDER IF NOT EXISTS`` which goes through the
+        Dremio-Nessie integration and is more reliable than calling
+        the Nessie REST API directly.
+        """
+        qi = self._quote_ident
+        folder_sql = f"CREATE FOLDER IF NOT EXISTS NessieSource.{qi(namespace)}"
+        try:
+            self._sql(token, folder_sql, timeout=30)
+            print(f"  Created Nessie namespace '{namespace}' via CREATE FOLDER")
+        except Exception as e:
+            # Not fatal – CREATE TABLE may implicitly create the namespace
+            print(f"  Warning: CREATE FOLDER failed ({e}), continuing...")
+
+    # ------------------------------------------------------------------
+    # MinIO upload
+    # ------------------------------------------------------------------
+
+    def _upload_csvs_to_minio(self) -> None:
+        """Upload seed CSVs to the Dremio MinIO bucket.
+
+        Mounts the local ``data_dir`` into a temporary ``minio/mc`` container
+        and copies files directly into the MinIO bucket.
+        """
+        import shlex
+
+        network = os.environ.get("DREMIO_NETWORK", "e2e_dbt_project_dremio-lakehouse")
+        mc_cmds = " && ".join(
+            [
+                "mc alias set myminio http://dremio-storage:9000"
+                f" {shlex.quote(self.minio_access_key)}"
+                f" {shlex.quote(self.minio_secret_key)}",
+                "mc mb --ignore-existing myminio/datalake/seeds/training",
+                "mc mb --ignore-existing myminio/datalake/seeds/validation",
+                "mc cp --recursive /seed-data/training/ myminio/datalake/seeds/training/",
+                "mc cp --recursive /seed-data/validation/ myminio/datalake/seeds/validation/",
+                "echo Upload complete",
+            ]
+        )
+        self.run(
+            [
+                "docker",
+                "run",
+                "--rm",
+                "--network",
+                network,
+                "-v",
+                f"{self.data_dir}:/seed-data:ro",
+                "--entrypoint",
+                "/bin/sh",
+                "minio/mc",
+                "-c",
+                mc_cmds,
+            ]
+        )
+
+    # ------------------------------------------------------------------
+    # S3 source creation
+    # ------------------------------------------------------------------
+
+    def _create_s3_source(self, token: str) -> None:
+        import requests
+
+        headers = self._headers(token)
+        # Dremio OSS uses the Catalog API (v3) for source management.
+        # For MinIO compatibility we must set compatibilityMode, path-style
+        # access, and point the endpoint at the Docker-internal hostname.
+        payload = {
+            "entityType": "source",
+            "name": "SeedFiles",
+            "config": {
+                "credentialType": "ACCESS_KEY",
+                "accessKey": self.minio_access_key,
+                "accessSecret": self.minio_secret_key,
+                "secure": False,
+                "externalBucketList": ["datalake"],
+                "rootPath": "/",
+                "compatibilityMode": True,
+                "enableAsync": True,
+                "propertyList": [
+                    {"name": "fs.s3a.path.style.access", "value": "true"},
+                    {"name": "fs.s3a.endpoint", "value": "dremio-storage:9000"},
+                ],
+                "whitelistedBuckets": ["datalake"],
+                "isCachingEnabled": False,
+                "defaultCtasFormat": "ICEBERG",
+            },
+            "type": "S3",
+            "metadataPolicy": {
+                "deleteUnavailableDatasets": True,
+                "autoPromoteDatasets": True,
+                "namesRefreshMillis": 3600000,
+                "datasetDefinitionRefreshAfterMillis": 3600000,
+                "datasetDefinitionExpireAfterMillis": 10800000,
+                "authTTLMillis": 86400000,
+                "updateMode": "PREFETCH_QUERIED",
+            },
+        }
+
+        # Try the v3 Catalog API first (Dremio OSS ≥ 24), fall back to v2.
+        for attempt in range(3):
+            # v3 catalog API – POST to create
+            resp = requests.post(
+                f"http://{self.dremio_host}:{self.dremio_port}/api/v3/catalog",
+                headers=headers,
+                json=payload,
+                timeout=self.HTTP_TIMEOUT,
+            )
+            if resp.status_code in (200, 201):
+                print("  SeedFiles S3 source created via v3 Catalog API")
+                return
+            if resp.status_code == 409:
+                # Source already exists – update it with PUT
+                print("  SeedFiles source exists, updating...")
+                # Get the existing source id + tag for the update
+                get_resp = requests.get(
+                    f"http://{self.dremio_host}:{self.dremio_port}/api/v3/catalog/by-path/SeedFiles",
+                    headers=headers,
+                    timeout=self.HTTP_TIMEOUT,
+                )
+                if get_resp.status_code == 200:
+                    existing = get_resp.json()
+                    payload["id"] = existing["id"]
+                    payload["tag"] = existing.get("tag", "")
+                    put_resp = requests.put(
+                        f"http://{self.dremio_host}:{self.dremio_port}/api/v3/catalog/{existing['id']}",
+                        headers=headers,
+                        json=payload,
+                        timeout=self.HTTP_TIMEOUT,
+                    )
+                    if put_resp.status_code == 200:
+                        print("  SeedFiles S3 source updated")
+                        return
+                    print(
+                        f"  Warning: update returned {put_resp.status_code}: {put_resp.text}"
+                    )
+                else:
+                    print(
+                        f"  Warning: failed fetching existing source "
+                        f"({get_resp.status_code}: {get_resp.text[:200]})"
+                    )
+                # Continue to v2 fallback / retry.
+
+            # v2 fallback
+            resp2 = requests.put(
+                f"http://{self.dremio_host}:{self.dremio_port}/apiv2/source/SeedFiles",
+                headers=headers,
+                json=payload,
+                timeout=self.HTTP_TIMEOUT,
+            )
+            if resp2.status_code in (200, 409):
+                print("  SeedFiles S3 source created/updated via v2 API")
+                return
+
+            print(
+                f"  Attempt {attempt + 1}/3: source creation failed "
+                f"(v3={resp.status_code}: {resp.text[:200]}, "
+                f"v2={resp2.status_code}: {resp2.text[:200]})"
+            )
+            time.sleep(5)
+
+        raise RuntimeError("Failed to create SeedFiles source after 3 attempts")
+
+    # ------------------------------------------------------------------
+    # Public API
+    # ------------------------------------------------------------------
+
+    def load(self) -> None:
+        """Load seeds using COPY INTO (no fragile CSV promotion needed).
+
+        1. Upload CSVs to MinIO.
+        2. Create an S3 source so Dremio can read those files.
+        3. Create Nessie namespace via CREATE FOLDER.
+        4. For each CSV, CREATE TABLE in Nessie + COPY INTO from S3.
+        5. Refresh source metadata so the VDS validator can see new tables.
+
+        Seeds are placed in the *same* Nessie namespace as models (the
+        target schema, e.g. ``elementary_tests``) because the dbt-dremio
+        REST API is stateless and the VDS view validator cannot resolve
+        cross-namespace Nessie references.
+        """
+        failures: list[str] = []
+        # For Dremio, seeds must live in the same Nessie namespace as models
+        # because the REST API is stateless (no persistent USE BRANCH) and
+        # the VDS view validator cannot resolve cross-namespace references.
+        # self.schema_name is the target schema (e.g. "elementary_tests").
+        seed_schema = self.schema_name
+
+        print("\n=== Loading Dremio seeds via MinIO + COPY INTO ===")
+
+        print("\nStep 1: Uploading CSVs to MinIO...")
+        self._upload_csvs_to_minio()
+
+        print("\nStep 2: Creating SeedFiles S3 source...")
+        token = self._get_token()
+        self._create_s3_source(token)
+
+        # Explicitly create the Nessie namespace before creating tables.
+        # Uses CREATE FOLDER via Dremio SQL (more reliable than Nessie REST API).
+        print("\nStep 3: Creating Nessie namespace...")
+        self._create_nessie_namespace(token, seed_schema)
+
+        qi = self._quote_ident
+        nessie_ns = f"NessieSource.{qi(seed_schema)}"
+
+        print(f"\nStep 4: Creating Iceberg tables at '{nessie_ns}'...")
+        created_tables: list[str] = []
+        for subdir, csv_path, table_name in self.iter_seed_csvs():
+            cols = self.csv_columns(csv_path)
+            if not cols:
+                print(f"  Skipping {table_name} (completely empty file)")
+                continue
+
+            fqn = f"{nessie_ns}.{qi(table_name)}"
+
+            # Drop + recreate to ensure idempotent seeding (no stale data)
+            col_defs = ", ".join(f"{qi(c)} VARCHAR" for c in cols)
+            try:
+                self._sql(token, f"DROP TABLE IF EXISTS {fqn}", timeout=30)
+            except Exception:
+                pass  # Table may not exist yet
+            create_sql = f"CREATE TABLE {fqn} ({col_defs})"
+            try:
+                self._sql(token, create_sql, timeout=60)
+                created_tables.append(table_name)
+            except Exception as e:
+                failures.append(f"create {table_name}: {e}")
+                continue
+
+            if not self.csv_has_data(csv_path):
+                print(f"  Created empty table: {table_name}")
+                continue
+
+            # Load CSV data using COPY INTO from the S3 source
+            fname = os.path.basename(csv_path)
+            s3_path = f"@SeedFiles/datalake/seeds/{subdir}/{fname}"
+            copy_sql = (
+                f"COPY INTO {fqn} "
+                f"FROM '{s3_path}' "
+                f"FILE_FORMAT 'csv' "
+                f"(EXTRACT_HEADER 'true', TRIM_SPACE 'true')"
+            )
+            print(f"  COPY INTO: {table_name}")
+            try:
+                self._sql(token, copy_sql, timeout=120)
+            except Exception as e:
+                failures.append(f"copy {table_name}: {e}")
+
+        # Force Dremio to refresh its metadata cache for the Nessie source.
+        # The VDS view validator uses a separate metadata system that may not
+        # immediately see tables created via the SQL API.  Without this,
+        # CREATE VIEW statements fail with "Object ... not found".
+        print("\nStep 5: Refreshing NessieSource metadata...")
+        try:
+            self._sql(token, "ALTER SOURCE NessieSource REFRESH STATUS", timeout=30)
+            print("  Metadata refresh triggered")
+        except Exception as e:
+            failures.append(f"metadata refresh: {e}")
+
+        # Give Dremio a moment to complete the background metadata scan.
+        print("  Waiting 10s for metadata propagation...")
+        time.sleep(10)
+
+        if failures:
+            raise RuntimeError("Dremio seeding failed:\n - " + "\n - ".join(failures))
+        print("\nDremio seed loading complete.")
diff --git a/tests/e2e_dbt_project/external_seeders/spark.py b/tests/e2e_dbt_project/external_seeders/spark.py
new file mode 100644
index 000000000..3d9be0e72
--- /dev/null
+++ b/tests/e2e_dbt_project/external_seeders/spark.py
@@ -0,0 +1,103 @@
+"""Spark external seed loader – reads CSVs from a volume mount and creates Delta tables."""
+
+from __future__ import annotations
+
+import os
+
+from external_seeders.base import ExternalSeeder
+
+
+class SparkExternalSeeder(ExternalSeeder):
+    """Load seeds into Spark via PyHive from CSV files mounted in the container."""
+
+    # dbt_project.yml sets ``+schema: test_seeds`` for seeds and the default
+    # ``generate_schema_name`` macro returns that verbatim, so the actual seed
+    # schema is always ``test_seeds`` regardless of the target schema name.
+    SEED_SCHEMA = "test_seeds"
+
+    @staticmethod
+    def _q(name: str) -> str:
+        """Quote a Spark SQL identifier, escaping any embedded backticks."""
+        return f"`{name.replace('`', '``')}`"
+
+    def load(self) -> None:
+        failures: list[str] = []
+        q = self._q
+        seed_schema = self.SEED_SCHEMA
+        print(
+            f"\n=== Loading Spark seeds via external CSV tables "
+            f"(schema={seed_schema}) ==="
+        )
+
+        try:
+            from pyhive import hive
+        except ImportError as e:
+            raise RuntimeError(
+                "pyhive is required for SparkExternalSeeder. Install dbt-spark[PyHive] (CI does this automatically)."
+            ) from e
+
+        host = os.environ.get("SPARK_HOST", "127.0.0.1")
+        port = int(os.environ.get("SPARK_PORT", "10000"))
+
+        print(f"Connecting to Spark Thrift at {host}:{port}...")
+        conn = None
+        cursor = None
+        try:
+            conn = hive.Connection(host=host, port=port, username="dbt")
+            cursor = conn.cursor()
+            print(f"Creating schema '{seed_schema}'...")
+            cursor.execute(f"CREATE DATABASE IF NOT EXISTS `{seed_schema}`")
+
+            for subdir, csv_path, table_name in self.iter_seed_csvs():
+                fname = os.path.basename(csv_path)
+
+                if not self.csv_has_data(csv_path):
+                    cols = self.csv_columns(csv_path)
+                    if not cols:
+                        print(f"  Skipping {table_name} (completely empty file)")
+                        continue
+                    col_defs = ", ".join(f"{q(c)} STRING" for c in cols)
+                    print(f"  Creating empty table: {table_name}")
+                    try:
+                        cursor.execute(
+                            f"DROP TABLE IF EXISTS {q(seed_schema)}.{q(table_name)}"
+                        )
+                        cursor.execute(
+                            f"CREATE TABLE {q(seed_schema)}.{q(table_name)} "
+                            f"({col_defs}) USING delta"
+                        )
+                    except Exception as e:
+                        failures.append(f"{table_name}: {e}")
+                    continue
+
+                container_path = f"/seed-data/{subdir}/{fname}"
+                # Escape single quotes in path to prevent SQL injection
+                safe_path = container_path.replace("'", "''")
+                tmp_view = f"_tmp_csv_{table_name}"
+                print(f"  Loading: {table_name}")
+                try:
+                    cursor.execute(
+                        f"CREATE OR REPLACE TEMPORARY VIEW {q(tmp_view)} "
+                        f"USING csv "
+                        f"OPTIONS (path '{safe_path}', header 'true', "
+                        f"inferSchema 'true')"
+                    )
+                    cursor.execute(
+                        f"DROP TABLE IF EXISTS {q(seed_schema)}.{q(table_name)}"
+                    )
+                    cursor.execute(
+                        f"CREATE TABLE {q(seed_schema)}.{q(table_name)} "
+                        f"USING delta AS SELECT * FROM {q(tmp_view)}"
+                    )
+                except Exception as e:
+                    failures.append(f"{table_name}: {e}")
+        finally:
+            if cursor is not None:
+                cursor.close()
+            if conn is not None:
+                conn.close()
+        if failures:
+            raise RuntimeError(
+                "Spark seed loading failed:\n - " + "\n - ".join(failures)
+            )
+        print("\nSpark seed loading complete.")
diff --git a/tests/e2e_dbt_project/generate_data.py b/tests/e2e_dbt_project/generate_data.py
index e6b3f8faf..6acf56b15 100644
--- a/tests/e2e_dbt_project/generate_data.py
+++ b/tests/e2e_dbt_project/generate_data.py
@@ -11,8 +11,12 @@
 EPOCH = datetime.utcfromtimestamp(0)
 DATE_FORMAT = "%Y-%m-%d %H:%M:%S"
 
+# Fixed seed so that generated CSVs are deterministic and cacheable across CI runs.
+RANDOM_SEED = 42
+
 
 def generate_fake_data():
+    random.seed(RANDOM_SEED)
     generate_string_anomalies_training_and_validation_files()
     generate_numeric_anomalies_training_and_validation_files()
     generate_any_type_anomalies_training_and_validation_files()
@@ -128,8 +132,8 @@ def get_training_row(date, row_index, rows_count):
         return {
             "updated_at": date.strftime(DATE_FORMAT),
             "occurred_at": (date - timedelta(hours=1)).strftime(DATE_FORMAT),
-            "min": random.randint(100, 200),
-            "max": random.randint(100, 200),
+            "min_val": random.randint(100, 200),
+            "max_val": random.randint(100, 200),
             "zero_count": 0
             if row_index < (3 / 100 * rows_count)
             else random.randint(100, 200),
@@ -139,7 +143,7 @@ def get_training_row(date, row_index, rows_count):
             "average": random.randint(99, 101),
             "standard_deviation": random.randint(99, 101),
             "variance": random.randint(99, 101),
-            "sum": random.randint(100, 200),
+            "sum_val": random.randint(100, 200),
         }
 
     def get_validation_row(date, row_index, rows_count):
@@ -147,8 +151,8 @@ def get_validation_row(date, row_index, rows_count):
         return {
             "updated_at": date.strftime(DATE_FORMAT),
             "occurred_at": (date - timedelta(hours=7)).strftime(DATE_FORMAT),
-            "min": random.randint(10, 200),
-            "max": random.randint(100, 300),
+            "min_val": random.randint(10, 200),
+            "max_val": random.randint(100, 300),
             "zero_count": 0
             if row_index < (80 / 100 * rows_count)
             else random.randint(100, 200),
@@ -158,20 +162,20 @@ def get_validation_row(date, row_index, rows_count):
             "average": random.randint(101, 110),
             "standard_deviation": random.randint(80, 120),
             "variance": random.randint(80, 120),
-            "sum": random.randint(300, 400),
+            "sum_val": random.randint(300, 400),
         }
 
     numeric_columns = [
         "updated_at",
         "occurred_at",
-        "min",
-        "max",
+        "min_val",
+        "max_val",
         "zero_count",
         "zero_percent",
         "average",
         "standard_deviation",
         "variance",
-        "sum",
+        "sum_val",
     ]
     dates = generate_rows_timestamps(base_date=EPOCH - timedelta(days=2))
     training_rows = generate_rows(rows_count_per_day, dates, get_training_row)
diff --git a/tests/e2e_dbt_project/load_seeds_external.py b/tests/e2e_dbt_project/load_seeds_external.py
new file mode 100644
index 000000000..b4fe28068
--- /dev/null
+++ b/tests/e2e_dbt_project/load_seeds_external.py
@@ -0,0 +1,52 @@
+#!/usr/bin/env python3
+"""Load seed data via external files instead of ``dbt seed``.
+
+Delegates to adapter-specific seeders defined in the ``external_seeders``
+package.  Run ``python load_seeds_external.py --help`` for usage.
+"""
+
+from __future__ import annotations
+
+import os
+import sys
+
+import click
+
+# Ensure the script's directory is on sys.path so that the
+# ``external_seeders`` package can be imported regardless of cwd.
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+from external_seeders import DremioExternalSeeder, SparkExternalSeeder  # noqa: E402
+
+SEEDERS = {
+    "dremio": DremioExternalSeeder,
+    "spark": SparkExternalSeeder,
+}
+
+
+@click.command()
+@click.argument("adapter", type=click.Choice(list(SEEDERS.keys())))
+@click.argument("schema_name")
+@click.argument(
+    "data_dir",
+    type=click.Path(exists=True, file_okay=False, resolve_path=True),
+)
+def main(adapter: str, schema_name: str, data_dir: str) -> None:
+    """Load seed CSVs into ADAPTER using external tables.
+
+    \b
+    ADAPTER      Target warehouse adapter (dremio | spark).
+    SCHEMA_NAME  Target schema / namespace for the seed tables.
+                 NOTE: Spark ignores this value and always uses the fixed
+                 schema defined in SparkExternalSeeder.SEED_SCHEMA (currently
+                 "test_seeds") because the generate_schema_name macro returns
+                 that name verbatim.
+    DATA_DIR     Path to the directory containing training/ and validation/ CSVs.
+    """
+    seeder_cls = SEEDERS[adapter]
+    seeder = seeder_cls(data_dir=data_dir, schema_name=schema_name)
+    seeder.load()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tests/e2e_dbt_project/macros/system/drop_test_schemas.sql b/tests/e2e_dbt_project/macros/system/drop_test_schemas.sql
index cfa924660..225405063 100644
--- a/tests/e2e_dbt_project/macros/system/drop_test_schemas.sql
+++ b/tests/e2e_dbt_project/macros/system/drop_test_schemas.sql
@@ -34,3 +34,23 @@
     {% set schema_relation = api.Relation.create(database=target.database, schema=schema_name) %}
     {% do dbt.drop_schema(schema_relation) %}
 {% endmacro %}
+
+{% macro trino__edr_drop_schema(schema_name) %}
+    {% set schema_relation = api.Relation.create(database=target.database, schema=schema_name) %}
+    {% do dbt.drop_schema(schema_relation) %}
+{% endmacro %}
+
+{% macro dremio__edr_drop_schema(schema_name) %}
+    {# Dremio does not support DROP SCHEMA; Docker container cleanup handles this in CI #}
+    {% do log("Skipping schema drop for Dremio (not supported); Docker cleanup handles this.", info=true) %}
+{% endmacro %}
+
+{% macro duckdb__edr_drop_schema(schema_name) %}
+    {% set quoted_schema = adapter.quote(schema_name) %}
+    {% do run_query("DROP SCHEMA IF EXISTS " ~ quoted_schema ~ " CASCADE") %}
+{% endmacro %}
+
+{% macro spark__edr_drop_schema(schema_name) %}
+    {% set quoted_schema = adapter.quote(schema_name) %}
+    {% do run_query("DROP DATABASE IF EXISTS " ~ quoted_schema ~ " CASCADE") %}
+{% endmacro %}
diff --git a/tests/e2e_dbt_project/macros/system/generate_schema_name.sql b/tests/e2e_dbt_project/macros/system/generate_schema_name.sql
index e30a0e706..0d4f7d59e 100644
--- a/tests/e2e_dbt_project/macros/system/generate_schema_name.sql
+++ b/tests/e2e_dbt_project/macros/system/generate_schema_name.sql
@@ -5,8 +5,23 @@
     {% endif %}
 
     {% if node.resource_type == "seed" %}
+      {#- For Dremio with enterprise_catalog_namespace, seeds must live in the
+          same Nessie namespace as models.  The REST API is stateless so
+          session-level USE BRANCH does not persist, and the VDS view
+          validator cannot resolve cross-namespace Nessie references. -#}
+      {% if target.type == 'dremio' %}
+        {% do return(default_schema) %}
+      {% endif %}
       {% do return(custom_schema_name) %}
     {% endif %}
 
+    {#- For Dremio with enterprise_catalog_namespace, delegate to the adapter's
+        generate_schema_name for non-seed nodes (views/tables) so that
+        root_path is correctly applied. Seeds use flat schema (e.g. test_seeds)
+        to avoid nested Nessie namespaces that Dremio can't create folders for. -#}
+    {% if target.type == 'dremio' %}
+      {{ return(dremio__generate_schema_name(custom_schema_name, node)) }}
+    {% endif %}
+
     {% do return("{}_{}".format(default_schema, custom_schema_name)) %}
 {%- endmacro %}
diff --git a/tests/e2e_dbt_project/models/nested/models/tree/nested.sql b/tests/e2e_dbt_project/models/nested/models/tree/nested.sql
index ec17f8541..7a76dd150 100644
--- a/tests/e2e_dbt_project/models/nested/models/tree/nested.sql
+++ b/tests/e2e_dbt_project/models/nested/models/tree/nested.sql
@@ -1 +1 @@
-select 1 as one
+select 1 as col1
diff --git a/tests/e2e_dbt_project/models/numeric_column_anomalies.sql b/tests/e2e_dbt_project/models/numeric_column_anomalies.sql
index 8eebe37d8..a547d7f5c 100644
--- a/tests/e2e_dbt_project/models/numeric_column_anomalies.sql
+++ b/tests/e2e_dbt_project/models/numeric_column_anomalies.sql
@@ -16,14 +16,14 @@ source as (
      select
          updated_at,
          occurred_at,
-         min,
-         max,
+         min_val,
+         max_val,
          zero_count,
          zero_percent,
          average,
          standard_deviation,
          variance,
-         sum
+         sum_val
      from source
  )
 
diff --git a/tests/e2e_dbt_project/models/one.sql b/tests/e2e_dbt_project/models/one.sql
index ec17f8541..7a76dd150 100644
--- a/tests/e2e_dbt_project/models/one.sql
+++ b/tests/e2e_dbt_project/models/one.sql
@@ -1 +1 @@
-select 1 as one
+select 1 as col1
diff --git a/tests/e2e_dbt_project/models/schema.yml b/tests/e2e_dbt_project/models/schema.yml
index 31f376284..7934fae2a 100644
--- a/tests/e2e_dbt_project/models/schema.yml
+++ b/tests/e2e_dbt_project/models/schema.yml
@@ -7,7 +7,7 @@ models:
     meta:
       owner: "{{ var('one_owner', none) }}"
     columns:
-      - name: one
+      - name: col1
         tests:
           - accepted_values:
               meta:
@@ -255,7 +255,7 @@ models:
             - average_length
             - null_count
     columns:
-      - name: min
+      - name: min_val
         tests:
           - elementary.column_anomalies:
               tags: ["numeric_column_anomalies", "column_anomalies"]
@@ -269,7 +269,7 @@ models:
               tags: ["numeric_column_anomalies", "column_anomalies"]
               column_anomalies:
                 - average
-      - name: max
+      - name: max_val
         tests:
           - elementary.column_anomalies:
               tags: ["numeric_column_anomalies", "column_anomalies"]
@@ -327,7 +327,7 @@ models:
         tests:
           - elementary.column_anomalies:
               tags: ["numeric_column_anomalies", "column_anomalies"]
-      - name: sum
+      - name: sum_val
         tests:
           - elementary.column_anomalies:
               column_anomalies:
@@ -347,7 +347,7 @@ models:
   - name: groups
     columns:
       - name: group_a
-        data_type: "{{ 'strIng' if (target.type == 'bigquery' or target.type == 'databricks' or target.type == 'athena') else 'CHArACTER varying' if target.type == 'redshift' else 'teXt' }}"
+        data_type: "{{ 'strIng' if (target.type == 'bigquery' or target.type == 'databricks' or target.type == 'athena' or target.type == 'spark') else 'varchar' if (target.type == 'trino' or target.type == 'dremio' or target.type == 'duckdb') else 'CHArACTER varying' if target.type == 'redshift' else 'teXt' }}"
       - name: group_b
         data_type: double
       - name: group_c
@@ -365,7 +365,7 @@ models:
   - name: stats_players
     columns:
       - name: player
-        data_type: "{{ 'STRING' if (target.type == 'bigquery' or target.type == 'databricks' or target.type == 'athena') else 'character varying' if target.type == 'redshift' else 'TEXT' }}"
+        data_type: "{{ 'STRING' if (target.type == 'bigquery' or target.type == 'databricks' or target.type == 'athena' or target.type == 'spark') else 'varchar' if (target.type == 'trino' or target.type == 'dremio' or target.type == 'duckdb') else 'character varying' if target.type == 'redshift' else 'TEXT' }}"
       - name: goals
         data_type: BOOLEAN
       - name: coffee_cups_consumed
@@ -530,7 +530,7 @@ models:
 
 sources:
   - name: training
-    schema: test_seeds
+    schema: "{{ target.schema if target.type == 'dremio' else 'test_seeds' }}"
     tables:
       - name: users_per_hour_daily_seasonal_training
         columns:
@@ -633,7 +633,7 @@ sources:
 
       - name: users_per_day_weekly_seasonal_training
   - name: validation
-    schema: test_seeds
+    schema: "{{ target.schema if target.type == 'dremio' else 'test_seeds' }}"
     tables:
       - name: users_per_hour_daily_seasonal_validation
       - name: any_type_column_anomalies_validation
diff --git a/tests/e2e_dbt_project/models/test_alerts_union.sql b/tests/e2e_dbt_project/models/test_alerts_union.sql
index 0567ab927..0970cdae3 100644
--- a/tests/e2e_dbt_project/models/test_alerts_union.sql
+++ b/tests/e2e_dbt_project/models/test_alerts_union.sql
@@ -1,7 +1,7 @@
 with dbt as (
     select * from {{ ref('alerts_dbt_tests') }}
 ),
-{%- if target.type != 'databricks' %}
+{%- if target.type not in ['databricks', 'spark'] %}
 schema_changes as (
     select * from {{ ref('alerts_schema_changes') }}
 ),
@@ -12,7 +12,7 @@ anomalies as (
 select * from dbt
 union all
 select * from anomalies
-{%- if target.type != 'databricks' %}
+{%- if target.type not in ['databricks', 'spark'] %}
 union all
 select * from schema_changes
 {%- endif %}
diff --git a/tests/e2e_dbt_project/tests/singular_test_with_no_ref.sql b/tests/e2e_dbt_project/tests/singular_test_with_no_ref.sql
index f7dfdc589..c6b54244e 100644
--- a/tests/e2e_dbt_project/tests/singular_test_with_no_ref.sql
+++ b/tests/e2e_dbt_project/tests/singular_test_with_no_ref.sql
@@ -1,2 +1,2 @@
 {% set relation = api.Relation.create(database=elementary.target_database(), schema=target.schema, identifier='numeric_column_anomalies') %}
-select min from {{ relation }} where min < 100
+select min_val from {{ relation }} where min_val < 100
diff --git a/tests/e2e_dbt_project/tests/singular_test_with_one_ref.sql b/tests/e2e_dbt_project/tests/singular_test_with_one_ref.sql
index 22931a7fb..3e72d931e 100644
--- a/tests/e2e_dbt_project/tests/singular_test_with_one_ref.sql
+++ b/tests/e2e_dbt_project/tests/singular_test_with_one_ref.sql
@@ -1 +1 @@
-select min from {{ ref('numeric_column_anomalies') }} where min < 100
\ No newline at end of file
+select min_val from {{ ref('numeric_column_anomalies') }} where min_val < 100
diff --git a/tests/e2e_dbt_project/tests/singular_test_with_source_ref.sql b/tests/e2e_dbt_project/tests/singular_test_with_source_ref.sql
index 2d61ce9a1..230939c27 100644
--- a/tests/e2e_dbt_project/tests/singular_test_with_source_ref.sql
+++ b/tests/e2e_dbt_project/tests/singular_test_with_source_ref.sql
@@ -1 +1 @@
-select min from {{ source('training', 'numeric_column_anomalies_training') }} where min < 105
\ No newline at end of file
+select min_val from {{ source('training', 'numeric_column_anomalies_training') }} where min_val < 105
diff --git a/tests/e2e_dbt_project/tests/singular_test_with_two_refs.sql b/tests/e2e_dbt_project/tests/singular_test_with_two_refs.sql
index 2524955af..4837270e8 100644
--- a/tests/e2e_dbt_project/tests/singular_test_with_two_refs.sql
+++ b/tests/e2e_dbt_project/tests/singular_test_with_two_refs.sql
@@ -3,7 +3,7 @@ with min_len_issues as (
 ),
 
 min_issues as (
-    select min as min_issue from {{ ref('numeric_column_anomalies') }} where min < 100
+    select min_val as min_issue from {{ ref('numeric_column_anomalies') }} where min_val < 100
 ),
 
 all_issues as (
diff --git a/tests/profiles/profiles.yml.j2 b/tests/profiles/profiles.yml.j2
index c1c2accf7..77138bf93 100644
--- a/tests/profiles/profiles.yml.j2
+++ b/tests/profiles/profiles.yml.j2
@@ -23,6 +23,43 @@ elementary_tests:
       schema: {{ schema_name }}
       threads: 4
 
+    trino: &trino
+      type: trino
+      method: none
+      host: localhost
+      port: 8086
+      user: admin
+      database: iceberg
+      schema: {{ schema_name }}
+      threads: 4
+
+    dremio: &dremio
+      type: dremio
+      software_host: localhost
+      port: 9047
+      user: dremio
+      password: dremio123
+      use_ssl: false
+      enterprise_catalog_namespace: NessieSource
+      enterprise_catalog_folder: {{ schema_name }}
+      threads: 4
+
+    duckdb: &duckdb
+      type: duckdb
+      path: /tmp/elementary_test.duckdb
+      schema: {{ schema_name }}
+      threads: 1
+
+    spark: &spark
+      type: spark
+      method: thrift
+      host: 127.0.0.1
+      port: 10000
+      user: dbt
+      schema: {{ schema_name }}
+      file_format: delta
+      threads: 4
+
     # ── Cloud targets (secrets substituted at CI time) ─────────────────
 
     snowflake: &snowflake
@@ -83,9 +120,9 @@ elementary_tests:
 elementary:
   target: postgres
   outputs:
-{%- set targets = ['postgres', 'clickhouse', 'snowflake', 'bigquery', 'redshift', 'databricks_catalog', 'athena'] %}
+{%- set targets = ['postgres', 'clickhouse', 'trino', 'dremio', 'duckdb', 'spark', 'snowflake', 'bigquery', 'redshift', 'databricks_catalog', 'athena'] %}
 {%- for t in targets %}
     {{ t }}:
       <<: *{{ t }}
-      {{ 'dataset' if t == 'bigquery' else 'schema' }}: {{ schema_name }}_elementary
+      {{ 'dataset' if t == 'bigquery' else ('enterprise_catalog_folder' if t == 'dremio' else 'schema') }}: {% if t == 'dremio' %}{{ schema_name }}.elementary{% else %}{{ schema_name }}_elementary{% endif %}
 {%- endfor %}