Snowflake is one of the most popular cloud data warehouses, enabling users to store and leverage massive amounts of data. As with any cloud solution, understanding usage and cost is critical for success.

The data engineering organization at Grindr has saved $600,000 in Snowflake costs by monitoring their Snowflake usage across their organization with Airflow. Their solution also served as inspiration for SnowPatrol, an open-source application for anomaly detection and alerting for Snowflake usage, powered by machine learning and Airflow.

In this webinar, Matt Shancer, Staff Data Engineer at Grindr, discusses how they implemented their Snowflake monitoring solution using Astronomer. We also cover a comprehensive deep dive into SnowPatrol, including how it works as an MLOps reference implementation for using Airflow as a way to manage the training, testing, deployment, and monitoring of predictive models. To learn more about SnowPatrol, check out the GitHub repo.