Add stores table cleaning, profiling, and standardization workflow#73
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📝 WalkthroughWalkthroughThis PR documents and implements a complete bronze-to-standardized data cleaning pipeline for store records. It adds 657 lines of Markdown documentation detailing per-attribute transformation objectives and defensive data-quality techniques, and extends the SQL script with profiling, validation CTEs, and final output transformations for state, ZIP, phone, and related fields. ChangesStore Data Standardization Pipeline
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This PR introduces comprehensive profiling, validation, cleaning, and standardization workflows for the bronze.stores table.
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Reviewed commit: a7926fe215
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| WHEN zip_code IS NULL OR TRY_CONVERT(INT, zip_code) IS NULL OR LEN(zip_code) < 5 THEN NULL | ||
| ELSE TRY_CONVERT(INT, zip_code) |
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Converting zip_code with TRY_CONVERT(INT, zip_code) silently drops leading zeros (for example, 02108 becomes 2108), which corrupts valid U.S. ZIP codes and breaks joins/grouping against canonical 5-character ZIP dimensions. This is a data-loss issue in the final cleaned projection, so ZIP codes should remain text and be normalized as padded strings instead of integers.
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| WHEN TRIM(state) = 'New Mexico' THEN 'NM' | ||
| WHEN TRIM(state) = 'Ohio' THEN 'OH' | ||
| WHEN state IS NULL OR TRIM(state) = '' OR LEN(TRIM(state)) < 2 THEN 'Unknown' | ||
| ELSE state |
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The fallback branch returns raw state values, so inputs that are already abbreviations but contain casing/whitespace noise (for example " ca " or "tx") pass through unstandardized. That defeats the deterministic state normalization this query is introducing and can fragment downstream aggregations by state.
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Actionable comments posted: 2
🤖 Prompt for all review comments with AI agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
Inline comments:
In `@explore_database/stores/stores.md`:
- Around line 655-657: The file ends without a newline (CI warning) in the
snippet with identifier "text id=\"p3\"" containing "Author : Ritik__"; fix it
by adding a single trailing newline character at the end of
explore_database/stores/stores.md so the final line (the "Author : Ritik__"
block) is followed by one newline to satisfy formatting checks.
In `@explore_database/stores/stores.sql`:
- Line 276: The WHERE clause currently filters for the misspelled value
'Unkonwn' so no rows match; update the WHERE condition to use the correct value
'Unknown' to match the CASE output (see the CASE that produces 'Unknown' around
the earlier lines) so the profiling query returns the intended rows.
🪄 Autofix (Beta)
Fix all unresolved CodeRabbit comments on this PR:
- Push a commit to this branch (recommended)
- Create a new PR with the fixes
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📒 Files selected for processing (2)
explore_database/stores/stores.mdexplore_database/stores/stores.sql
| ```text id="p3" | ||
| Author : Ritik__ | ||
| ``` No newline at end of file |
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Add trailing newline to satisfy CI formatting checks.
The file ends without a newline (pipeline warning). Add a single trailing newline after Line 657 to prevent recurring CI noise.
✅ Minimal fix
```text id="p3"
Author : Ritik__</details>
<!-- suggestion_start -->
<details>
<summary>📝 Committable suggestion</summary>
> ‼️ **IMPORTANT**
> Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
```suggestion
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@explore_database/stores/stores.md` around lines 655 - 657, The file ends
without a newline (CI warning) in the snippet with identifier "text id=\"p3\""
containing "Author : Ritik__"; fix it by adding a single trailing newline
character at the end of explore_database/stores/stores.md so the final line (the
"Author : Ritik__" block) is followed by one newline to satisfy formatting
checks.
| SELECT | ||
| state | ||
| FROM clean_state_abbr | ||
| WHERE state = 'Unkonwn' ; |
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Typo: 'Unkonwn' should be 'Unknown'.
The CASE statement on line 268 produces 'Unknown' but this WHERE clause filters for 'Unkonwn' (misspelled), so this profiling query will never return any matching rows.
Proposed fix
-WHERE state = 'Unkonwn' ;
+WHERE state = 'Unknown' ;📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| WHERE state = 'Unkonwn' ; | |
| WHERE state = 'Unknown' ; |
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@explore_database/stores/stores.sql` at line 276, The WHERE clause currently
filters for the misspelled value 'Unkonwn' so no rows match; update the WHERE
condition to use the correct value 'Unknown' to match the CASE output (see the
CASE that produces 'Unknown' around the earlier lines) so the profiling query
returns the intended rows.
1 participant
Summary
This PR introduces comprehensive profiling, validation, cleaning, and standardization workflows for the
bronze.storestable.The implementation focuses on improving retail-store data quality, geographical consistency, operational reliability, and downstream analytical usability by handling malformed records, inconsistent categorical values, heterogeneous formatting ecosystems, and invalid operational attributes.
Additionally, detailed markdown documentation has been added to describe the transformation framework, validation methodology, data-quality issues identified, standardization logic, and engineering principles applied throughout the stores cleaning pipeline.
Changes made
Added complete data profiling workflow for the
bronze.storestableImplemented validation and cleaning logic for:
store_idstore_namestore_typeaddresscitystatestate_fullzip_codecountryregiondistrictphonemanager_nameopened_datesq_footagenum_employeesannual_rent_usdis_activehas_parkinghas_cafeAdded distribution-analysis queries for categorical columns
Implemented phone-number standardization across multiple raw patterns
Added defensive date parsing and normalization logic for heterogeneous date formats
Added categorical normalization for boolean-style operational columns
Added final unified clean projection query for downstream analytical consumption
Added detailed markdown documentation for the stores cleaning and standardization framework
Validation
Related issues
Notes for reviewers
Summary by CodeRabbit
Documentation
Refactor