Unit tests effectively validate known expectations, such as ensuring a column contains no null values or that an integer falls within a specific range. However, assertions often fail to capture systemic issues. A test suite passes even if the pipeline never runs, and a valid schema today does not guarantee compatibility tomorrow. Observability addresses these gaps by providing continuous visibility into the internal state of the system based on its external outputs.
This chapter moves from static testing to dynamic monitoring. We establish the technical pillars necessary for tracking pipeline health: logs, metrics, and traces. You will learn to instrument your data workflows to detect "silent failures" that often bypass standard quality gates.
The curriculum covers the implementation of monitors for three primary classes of anomalies:
By the end of this module, you will be able to construct a monitoring system that alerts on these conditions, ensuring that reliability issues are identified before they affect downstream consumers.
3.1 The Pillars of Data Observability
3.2 Monitoring Freshness and Latency
3.3 Volume and Row Count Anomalies
3.4 Schema Drift Detection
3.5 Practice: Building a Freshness Monitor
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