Data Quality

Expectations, completeness, uniqueness and quarantine flows that stop bad data reaching consumers. This path sequences the category's 14 resources by roadmap stage and composite score — work top-down inside each step.

Data Quality

1. Pipeline → DQ → quarantine → publish 38.65 — Pipeline → DQ → quarantine → publish
2. Quarantine then Publish 37.25 — Quarantine then Publish
3. Duplicate records 35.25 — Duplicate records
4. Data quality failure 34.75 — Data quality failure
5. Deduplicate records without rewriting the table 34.15 — Deduplicate records without rewriting the table
6. Name the four dimensions of data quality 34.15 — Name the four dimensions of data quality

8 more on the category page.

• Express a quality contract as executable checks
• Design a quarantine table with a reason
• Alert on the quarantine rate, not just on failure
• Count duplicates per key with one GROUP BY query
• Correlate the first duplicate date with a deploy
• Rewrite affected partitions instead of the whole table
• Name the four dimensions of data quality — freshness, completeness, validity, consistency
• Map each dimension to a concrete signal — SLA, null rate, range, uniqueness

Security-flagged: Pipeline → DQ → quarantine → publish, Duplicate records, Data quality failure, Data Quality Reviewer, Pre-deployment DQ Check — review the security posture first. Verification on these records: 14 community. Nothing here is independently audited — the trust scan and your own review are the controls.

Phase 3 of the roadmap. Stage context: Data Quality · side-by-side comparison · all learning topics