TLDR Tech / Data
Where Is the Right Place to Catch Data Volume Anomalies?
Monitoring data volume anomalies at the data warehouse layer (rather than solely at the source) is critical when sources are diverse, failure modes are silent, and real-time streams lack batch boundaries. This centralized approach creates a unified detection and communication point, bridging upstream producers and downstream consumers while providing actionable data health signals. Introducing a suppression layer for known, context-specific anomalies minimizes alert fatigue without incurring technical debt.
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