Data Quality Design Patterns

Modern data pipeline quality control leverages patterns like Write–Audit–Publish (WAP), Audit–Write–Audit–Publish (AWAP), Transform–Audit–Publish (TAP), and the Signal Table Pattern to balance integrity, cost, and latency. WAP and AWAP use staging and multiple audits to block bad data from production, while TAP validates in-memory to reduce storage and I/O costs; the Signal Table Pattern prioritizes speed with less safety. Choosing the right pattern ensures reliable pipelines, downstream trust, and business value.

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