Data Quality, Testing & Observability
The dimensions of data quality, where to enforce checks across source/pipeline/warehouse, schema contracts and breaking-change detection, freshness and anomaly monitoring, dbt tests vs. data-observability tooling, and incident response for a broken pipeline
A practitioner's tour of data quality as a data engineering interview topic: the five dimensions of data quality (accuracy, completeness, timeliness, consistency, uniqueness) illustrated with concrete failures, the tradeoffs of enforcing checks at the source, in-pipeline, or at the warehouse, schema contracts between producers and consumers and how to catch breaking changes before they ship, freshness/volume/distribution anomaly monitoring and why it catches what a schema check never will, dbt tests versus dedicated data-observability tooling and when each is the right layer, and a full incident-response walkthrough — triage, containment, root cause, prevention — for a pipeline that silently broke a downstream dashboard.
Practice questions (4)
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Diagnosing a Dashboard That Broke Days After the Actual Cause
Intermediate · Free -
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Diagnosing Which Data Quality Dimension a Symptom Belongs To
Intermediate -
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Designing Layered Checks for a New Ingestion Pipeline
Intermediate -
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Choosing Between dbt Tests and a Data Observability Platform
Intermediate