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@@ -4,6 +4,118 @@ Real-world examples showing what altimate can do across data engineering workflo
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## Onboarding a Junior Data Engineer — Automated Peer Review
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`dbt``SQL Quality``A-F Grading`
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A junior engineer always gets senior-level peer review — altimate grades the mart layer, flags every anti-pattern, fixes the offending SQL, and re-grades to prove the improvement.
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**Prompt:**
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> Run quality checks and grading on the mart layer queries of my dbt project to find out the SQL anti patterns. Also fix those issues, validate and re-grade them.
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[](https://us06web.zoom.us/clips/share/aJ5crYpRT4WSH6crh4iwcA)
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<small>*Click on the image to watch the demo.*</small>
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---
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## An Upstream Schema Changed. What Just Broke?
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`dbt``Lineage``Incident Response`
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A staging model renames a column and the dbt run fails. altimate reads the error log, traces lineage, pinpoints every broken downstream model, and fixes the references — no hunting through dozens of files.
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**Prompt:**
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> Switch to the `bug/column-rename` branch. A staging model was updated to rename a column, but downstream models weren't updated. Check the error log at `logs/dbt_run_error.log` and identify all the downstream models that broke. Fix them.
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[](https://us06web.zoom.us/clips/share/AZcsChIhRF6Mwk4Sl6QaCA)
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<small>*Click on the image to watch the demo.*</small>
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---
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## Column-Level Lineage Diff for PR Reviews
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`dbt``Column-Level Lineage``PR Review`
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Before approving a risky refactor PR, get a precise column-level lineage diff between the two branches — new dependencies, removed ones, source changes — so you know the blast radius before you merge.
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**Prompt:**
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> I am reviewing a PR that refactors `mart_patient_360` — the PR claims to fix a cartesian explosion, hash SSN for HIPAA, and add financial metrics. Before I approve, I need to understand exactly what changed at the column-level data flow. Run a column-level lineage diff between the old version (on `main`) and the new version (on `refactor/mart-patient-360-fix-cartesian-and-pii`) of `models/marts/mart_patient_360.sql`. Show me which column dependencies were added, removed, or changed source — I want to know the blast radius before merging.
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[](https://youtu.be/SpUWKq7x_T0)
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<small>*Click on the image to watch the demo.*</small>
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---
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## From Idea to Production dbt Model in One Terminal Session
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`dbt``Model Scaffolding``Tests & Docs`
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A plain-English analytics request becomes a production-ready dbt asset — SQL, documentation, schema tests, validation — all in a single session.
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**Prompt:**
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> I need a new dbt model called `mart_monthly_revenue`. It should show monthly revenue broken down by merchant risk tier. Include total revenue, transaction count, unique merchants, and average transaction value. Use `stg_transactions` and `stg_merchants` as the upstream models.
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[](https://us06web.zoom.us/clips/share/caXUfjyqQmqdylPQzao86A)
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<small>*Click on the image to watch the demo.*</small>
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---
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## Refactoring dbt Models Without Breaking Everything
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`dbt``Refactor``Impact Analysis`
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Plan a schema refactor with a complete blast-radius report before merging — every downstream model that needs a change, and exactly what change it needs.
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**Prompt:**
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> Switch to the `feat/refactor-stg-transactions` branch. The `stg_transactions` model renames `created_at` to `transaction_at` and drops `card_last_four` and `ip_address`. Before I merge this, tell me every downstream model that will break and what changes each one needs.
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[](https://us06web.zoom.us/clips/share/-I9EE6LAQguh0tS1oLG6aA)
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<small>*Click on the image to watch the demo.*</small>
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---
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## Migrating From MS SQL Server to MS Fabric via dbt
End-to-end warehouse migration: review the stored procedures, generate dbt models targeting Fabric, run cross-database data-diff validation, schema-difference checks, build the project, and produce an interactive migration validation dashboard.
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**Prompt:**
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> We need to perform MS SQL Server to MS Fabric migration via dbt. We have SQL Server code present at `sql_server_objects/stored_procedures` — review it and create dbt models for the same with Fabric as the target, following `migration_best_practices`. Raw layer tables are already populated in Fabric. Once done, perform compilation, cross-database `data_diff` validation between existing and new code, a schema-difference check, and then build the project in Fabric. Finally, produce an interactive migration validation dashboard with migration status, validation results, lineage, etc.
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[](https://us06web.zoom.us/clips/share/4intYUDRQ2Wo7C42qIeJig)
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<small>*Click on the image to watch the demo.*</small>
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---
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## A Platform Admin's First Day With Microsoft Fabric
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`MS Fabric``Governance``Lineage``PII Audit`
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Drop altimate into an unfamiliar Fabric instance and get an immediate picture — lineage, code quality, active roles, users, PII exposures — in one prompt.
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**Prompt:**
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> I am new to the Microsoft Fabric instance. Show me the lineage, code quality, active roles, users, PII exposures etc.
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[](https://us06web.zoom.us/clips/share/Fh00mmQnRKOR6oxjQf6utA)
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<small>*Click on the image to watch the demo.*</small>
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