The best way to query behavioral data from Snowplow in Snowflake is to:

• Use Snowflake's SQL capabilities to query structured event data stored in Snowflake tables and views
• Leverage Snowplow's canonical event model and schema validation to ensure data consistency, allowing for efficient querying across large datasets
• Use Snowflake's performance optimization features (clustering keys, materialized views, result caching) to enhance query speed for large event datasets
• Implement Snowflake's Dynamic Tables for incremental processing of Snowplow event streams, enabling near real-time analytics

For advanced use cases, Snowplow Signals can provide pre-computed user attributes accessible through APIs, reducing the need for complex aggregation queries.