Day 34 of 45 · Pro lesson

Data Quality & Anomaly Detection

The curriculum for this day stays visible. The lesson, code, and workspace unlock with Pro.

Teaches: Defense in depth: Pre-load vs In-flight vs Post-load verification, Column invariants (uniqueness, referential integrity, range bounds), Row count anomaly detection & statistical deviation (Z-score), Freshness SLAs & silent pipeline stalls, Circuit breakers: quarantine vs pipeline abort, Data contract enforcement

Build: Data quality & circuit breaker engine: schema enforcement → statistical volume anomaly detector → referential integrity validator → quarantine isolation router.

•Complete 15 drills on contract validation, foreign key orphaned checks, statistical anomaly detection, and quarantine routing.
•Run 6 Python modules testing circuit breaker trip points, Z-score thresholds, and automated quarantine generation.
•Simulate 5 production failures: silent 0-row load, duplicate primary keys, foreign key orphan explosion, and 30% revenue drop anomaly.
•Defend 10 senior data quality questions and complete the blank-page exam.

A failing pipeline that halts execution is an inconvenience; a silent corrupt pipeline that writes dirty metrics to production is an executive catastrophe.

This section walks through the idea with a short example, then the trade-offs you should mention in an interview.

In practice you start from the raw rows, apply the transform step by step, and check the shape of the result before you move on.

A common mistake is to jump straight to the final query without naming the grain or the join keys that keep the result correct.

Once the core path works, you harden it for nulls, duplicates, and late data so the pipeline stays reliable under load.

The Pro write-up covers the full explanation, worked examples, and the code you can run in the studio.

# Locked example
result = transform(frame)
print(result.head())

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