To optimize storage costs with Snowplow and Snowflake:

• Data Partitioning: Partition large event tables by date or event type to optimize query performance and reduce scanning costs
• Clustering: Apply clustering keys on frequently queried columns (user_id, event_timestamp) to improve query efficiency and reduce compute costs
• Data Retention Policies: Implement lifecycle policies to automatically archive or delete older Snowplow event data based on business requirements
• Compression Optimization: Ensure efficient data compression by using optimal file formats (Parquet) and Snowflake's automatic compression
• Materialized Views: Pre-aggregate frequently accessed Snowplow metrics to reduce query costs while maintaining real-time insights

Incremental Processing: Use dbt's incremental models to process only new Snowplow events, minimizing compute costs for transformations