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:

• Freshness: Measuring the latency between data generation and its availability in the warehouse.
• Volume: Identifying significant deviations in row counts or data size. For instance, you might trigger an alert when the current volume $V_t$ deviates significantly from a historical baseline, formalized as: $| V_t - \mu | > k \cdot \sigma$ where $\mu$ is the moving average and $\sigma$ is the standard deviation.
• Schema Drift: Programmatically identifying changes in column types, field additions, or deletions that threaten backward compatibility.

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.