Snowflake is a self-managed data platform that enables users to store, process, analyze, and share high volumes of structured and semi-structured data. It has gained widespread adoption due to its unified, scalable, and high-performance platform, flexibility in handling diverse workloads, and built-in dashboards and integration with business intelligence tools. To ensure the optimal use of Snowflake, it's essential to monitor various metrics such as compute utilization, storage usage, query performance, and data quality. Monitoring these metrics helps teams identify bottlenecks, optimize costs, and improve overall performance. Key metrics include `EXECUTION_TIME`, `BYTES_WRITTEN`, `BYTES_SCANNED`, `AVG_RUNNING`, `AVG_QUEUED_LOAD`, `AVG_BLOCKED`, `CREDITS_USED`, `CREDITS_USED_COMPUTE`, `CREDITS_BILLED`, and `QUERY_RETRY_TIME`. Additionally, monitoring query patterns with `QUERY_PARAMETERIZED_HASH` and `OPERATOR_STATISTICS` can help identify areas for optimization. By tracking these metrics and using tools like Datadog, teams can ensure the performance of their Snowflake virtual warehouses, databases, and data quality.