Using Snowplow's event pipeline and trackers, you can implement this capability with granular, first‑party data and real‑time processing.

When storing Snowplow events in Snowflake:

• Raw Events Schema: Store atomic events in Snowplow's canonical event model with fixed columns for standard properties (user_id, timestamp, event) and VARIANT columns for flexible contexts and properties
• Enriched Events Schema: Use Snowplow's enriched schema that includes additional columns for IP geolocation, user agent parsing, campaign attribution, and custom enrichments
• Optimization Strategy: Implement both schemas - raw events for data lineage and reprocessing, enriched events optimized for analytics with proper clustering and partitioning
• Schema Evolution: Leverage Snowflake's schema evolution capabilities with Snowplow's Iglu schema registry to handle changes without breaking downstream processes

This dual approach provides flexibility for reprocessing while optimizing performance for analytical workloads.