Automated Schema Drift Management in Snowflake Using Cortex LLM

From Manual Schema Updates to AI-Driven Data Engineering

Photo by JJ Ying on Unsplash

One of the biggest challenges I face when building data analytics platforms is managing changes in data structures from source systems to destination tables, which affects my ETL/ELT pipeline.

While I work with multiple platforms, recently most of my projects have been in Snowflake. In Snowflake, schema drift detection is easier, and there are several ways to implement it. Schema drift refers to changes in the structure of our database, such as adding, deleting, or modifying columns, data types, and constraints.

In this blog, I leveraged Snowflake Cortex LLM to automatically understand and propagate schema changes without any manual effort. I’ll explain with a simple example how I used Snowflake Cortex LLM to solve the schema drift challenge. I used the Claude-3.5-Sonnet model, though other models can work as well. Snowflake Cortex allows me to automatically analyze table structures and generate the appropriate DDL statements to propagate schema changes — essentially having AI write SQL for me based on the context of my data pipeline.

Prerequisites

This schema drift solution requires Snowflake Enterprise edition or higher with Cortex…