Data Validation and Anomaly Detection Questions
Techniques for validating data quality and detecting anomalies using SQL: identifying nulls and missing values, finding duplicates and orphan records, range checks, sanity checks across aggregates, distribution checks, outlier detection heuristics, reconciliation queries across systems, and building SQL based alerts and integrity checks. Includes strategies for writing repeatable validation queries, comparing row counts and sums across pipelines, and documenting assumptions for investigative analysis.
HardSystem Design
0 practiced
Design monitoring metrics for the health of the validation checks themselves: what to measure (coverage, runtime, flakiness, false-positive rate), how to compute them in SQL, and how to visualize these metrics for both SRE and analytics audiences.
MediumTechnical
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Design a lightweight incident playbook for when an important validation alert fires (e.g., payment mismatch). The playbook should define immediate steps, who to notify, what SQL checks to run in the first 30 minutes, and acceptable temporary mitigations for dashboards.
MediumTechnical
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Describe how to implement reconciliation checks as idempotent SQL jobs that can be retried safely after a failure. What patterns (e.g., upserts, transactional writes, checkpoint tables) would you use and why? Provide pseudo-SQL examples.
HardTechnical
0 practiced
A pipeline shows matching row counts between source and warehouse, but the revenue sums differ significantly. Provide a prioritized set of SQL steps to isolate whether the mismatch is due to currency conversion, late-arriving records, rounding errors, joins, or transformation bugs. Mention the expected indicators for each cause.
HardTechnical
0 practiced
Write an optimized SQL query to detect orphan records in a massive analytics dataset where 'events' (1B rows) reference 'users(user_id)'. Include strategies such as partition pruning, using NOT EXISTS vs LEFT JOIN, and leveraging indexes or bloom filters. Explain expected performance considerations.
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