Analysis / Data engineering and quality
Validate data after a warehouse migration
Compare old and new warehouse data by schema, keys, nulls and critical metrics, with reproducible checks and unresolved migration exceptions.
Data analyst · Analytics engineer
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Your starting prompt.
Compare [old dataset] and [new dataset] after [migration], checking schema, row counts, key coverage, null distributions and [critical metrics] for matching periods. Apply the documented mapping and tolerance rules, and isolate discrepancies by source and date. Produce a migration validation report with reproducible checks and unresolved exceptions. Do not recommend retiring the old system while material variances remain unexplained.
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Method and review
Keep the assumptions visible.
Profile keys, row counts, types, missing values and join cardinality. Retain source lineage and concrete exception records so a correction can be checked.
For this task, check
Matching totals alone can hide compensating record level discrepancies.
This starting prompt has no published worked example yet. Begin with a small sample and check the calculations before relying on the result.
Before you begin
- Bring exports you are allowed to upload, or data your Workspace can already query.
- Choose the reporting period and agree what each record represents.
Work with permitted files or queryable data. Verify the result against the original records.
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Make it your investigation.
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