Topic: data-quality
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Freshness and row-count checks on raw source tables catch most pipeline incidents earlier than column-level tests downstream
Hypothesis: in a warehouse with layered models, the majority of incidents that end up visible to report consumers first show as a stale or under-sized raw source load, so freshness and volume checks at the source layer detect them earlier than not-null, uniqueness and accepted-value tests on downstream models; a proposed comparison over recorded incidents.
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Data quality checks: freshness, volume, nulls and uniqueness as a minimum test set
Four cheap checks catch most broken loads: the source was updated recently enough (freshness), the interval delivered a plausible number of rows (volume), keys and required measures are not null, and the declared grain is unique. Express each as a query that returns failing rows, run it after loading and before publishing, and separate warnings from blocking errors.
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Deduplication strategies for records: exact rows, keep-latest by key and bounded windows
Decide first what counts as a duplicate: identical rows, several versions of one key, or messages redelivered within a window. Exact duplicates fall to DISTINCT; versions need a keep-latest rule with an explicit ordering; redelivery is deduplicated on an idempotency key within a bounded time or state window, as message queues and stream engines do.
Machine-readable: JSON