A three-level observability maturity framework for Snowflake is presented, mapping native platform capabilities to increasing operational sophistication. Level 1 covers pipeline health using Event Tables, Task DAGs, Streams, and Alerts to detect failures within minutes. Level 2 addresses data quality enforcement via Data Metric Functions (DMFs) and cost attribution using QUERY_TAG, WAREHOUSE_METERING_HISTORY, and Resource Monitors. Level 3 tackles AI observability through CORTEX_AI_FUNCTIONS_USAGE_HISTORY, AI_OBSERVABILITY_EVENTS for agent tracing, and CORTEX_AI_GUARDRAILS_USAGE_HISTORY for prompt injection monitoring. The framework advocates an incremental adoption path — reliability first, then quality, then AI traceability — arguing that Snowflake's native primitives now eliminate the need for external observability platforms in most cases. A working GitHub demo with SQL, DMFs, Cortex AI classification, and a Streamlit dashboard is provided.
