Optimize Queries for Performance in Snowflake

Snowflake is a powerful data platform, but poorly written queries can lead to high costs, slower performance, and resource bottlenecks. Optimizing your queries ensures faster results, better efficiency, and cost savings. Below are proven techniques to optimize queries for performance in Snowflake.

1. Use SELECT Only Required Columns

**Avoid SELECT *: Fetching all columns increases data transfer and processing time. Instead, retrieve only the necessary columns.

-- Inefficient
SELECT * FROM orders;

-- Optimized
SELECT order_id, customer_name, order_date FROM orders;

2. Filter Data at the Source

Apply filters using WHERE clauses to limit the amount of data scanned and processed.

-- Inefficient
SELECT * FROM orders;

-- Optimized
SELECT order_id, order_date FROM orders WHERE order_date > '2025-01-01';

3. Leverage Clustering Keys

Use clustering keys for large tables to improve query performance by reducing scan times for frequently filtered or joined columns.

Choose high-cardinality columns (e.g., customer_id, order_date).

ALTER TABLE orders CLUSTER BY (order_date);

4. Use Result Caching

Snowflake caches query results for 24 hours. Reuse cached results for repeated queries instead of…