SnowPatrol is an application for anomaly detection and alerting for Snowflake usage, powered by Machine Learning. It's also an MLOps reference implementation, an example of how to use Airflow as a way to manage the training, testing, deployment, and monitoring of predictive models. The Astronomer team built SnowPatrol to help identify abnormal Snowflake usage and simplify overage root-cause analysis and remediation. To automate adding query tags to every DAG and Task, the team leveraged advanced Airflow features designed to simplify the management of Airflow Deployments. They built a custom cluster policy to attach query tags to all Snowflake-related Tasks, ensuring that every single DAG and Task deployed to an Airflow Deployment will automatically get query tags. This allows for the creation of dashboards to visualize Airflow DAG execution costs, detected anomalies over time, and storage/compute usage by warehouse, schema, database, tables, etc. The Astronomer's data team managed to cut almost 25% of its Snowflake spend and is now able to keep a close eye on any unexpected increase in Storage or Compute costs.