The article presents an automated solution for tracking schema changes in Iceberg tables using Snowflake's metadata storage. The approach uses two tracking tables and a stored procedure to detect, log
, and audit all structural modifications by analyzing metadata JSON files.
Reasons to Read -- Learn:
how to implement an automated schema tracking system that captures every structural change in your Iceberg tables, enabling comprehensive data governance and auditing capabilities
how to utilize Snowflake's metadata storage features to monitor DDL operations and maintain a complete historical record of schema modifications in your data environment
how to create and implement a practical solution that includes specific table structures, stored procedures, and testing processes for tracking schema changes in a production environment
5 min readauthor: Sachin Mittal
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