Data Quality

Data Quality¶

"Observability is just empathy for future engineers."

Ensuring data is reliable, accurate, and trustworthy.

"Pipelines fail quietly. People fail when no one explains why they exist."

Overview¶

Data quality isn't a nice-to-have—it's a prerequisite for trust. Without quality, your platform becomes a liability, not an asset.

Key Topics¶

Governance ¶

Data governance, SLAs, schema enforcement, observability.

Learn about: - SLAs and freshness - Schema enforcement - Metadata and lineage - Ownership and accountability

Checks ¶

Data quality checks and testing.

Learn about: - Quality dimensions - Testing frameworks - Automated checks - Quality scores

Quality Dimensions¶

Completeness - Are all expected records present?
Accuracy - Does data reflect reality?
Consistency - Is data consistent across sources?
Timeliness - Is data fresh enough?
Validity - Does data conform to schema?
Uniqueness - Are there duplicates?

Best Practices¶

At Ingestion¶

Schema validation
Completeness checks
Uniqueness checks

Post-Transformation¶

Business rule validation
Referential integrity
Statistical checks

Continuous Monitoring¶

Quality scores
Anomaly detection
Alerting

Data Engineering - Platform fundamentals
Data Architecture - Storage patterns

Next: Governance →