How would you optimize a SQL query in Snowflake that involves multiple joins?

To optimize a multi‑join query in Snowflake, start by ensuring each table is properly clustered or partitioned so that the engine can prune data early. Use the Snowflake Query Profile to identify bottlenecks. Rewrite joins to use the most selective tables first and consider using semi‑joins or EXISTS when you only need existence checks. Replace cross joins with explicit join conditions to avoid Cartesian products. Leverage result caching by reusing the same warehouse size and ensuring the query is deterministic. If the same join logic is reused, create a materialized view or a temporary table to store intermediate results. Finally, choose the right warehouse size and concurrency level to balance cost and performance.