You monitor schema changes in a data warehouse by periodically querying metadata catalogs (like INFORMATION_SCHEMA ), subscribing to event-driven notifications, or comparing structural hashes of your tables over time. Each method trades off between detection latency, implementation complexity, and warehouse compatibility. Schema changes are the silent killers of data pipelines. A column rename, a type change from INTEGER to VARCHAR , or a dropped table can cascade through downstream models, dashboards, and ML features without any error until someone notices the numbers look wrong. Monitoring schema changes means catching these mutations before they reach your consumers. This guide covers what schema changes are, why they break things, how to detect them across Snowflake, Databricks, and PostgreSQL, and which tools can automate the process. What counts as a schema change? A schema change is any modification to the structure of a table, view, or other database object.…