Data Observability Setup: Data Observability Guide

Data Observability Guide A practical guide to implementing data observability for Databricks-based data platforms. By Datanest Digital 1. The Five Pillars of Data Observability Data observability borrows from software observability but applies it specifically to data quality and reliability. The five pillars are: 1.1 Freshness Question : Is the data up to date? Track the last modification timestamp of every table Define SLAs per table/domain (e.g., "orders must update within 2 hours") Measure "time since last update" continuously Alert when SLAs are breached 1.2 Volume Question : Is the expected amount of data arriving? Record row counts per pipeline run Track bytes written to each table Compare against historical baselines (moving average, percentiles) Flag zero-row writes, dramatic drops, or unexplained spikes 1.3 Schema Question : Has the structure of the data changed? Monitor for added/dropped/renamed columns Detect type changes (string → int, nullable → required) Version schemas a