Snowflake Data Cloud is a cloud-native data platform that separates storage and compute, enabling elastic scaling for analytics, data engineering, and AI workloads across AWS, Azure, and Google Cloud. Unlike traditional databases, Snowflake's micro-partition architecture automatically organizes data for optimal pruning, while features like Time Travel, Zero-Copy Cloning, and Secure Data Sharing redefine collaboration and recovery. Its serverless model — from Snowpipe ingestion to Cortex AI functions — eliminates infrastructure management, making Snowflake a unified platform for warehousing, data lakes, and streaming.
What This Cheat Sheet Covers
This topic spans 25 focused tables and 132 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.
Table 1: Virtual Warehouse Configuration
A virtual warehouse is the compute that actually runs your queries, and almost every cost decision in Snowflake starts here. These parameters control how much horsepower a warehouse has, how it scales out under concurrency, and — crucially — how quickly it suspends itself when idle, which is the single biggest lever you have on the credit bill.
| Parameter | Example | Description |
|---|---|---|
CREATE WAREHOUSE wh_medium WAREHOUSE_SIZE = 'MEDIUM'; | • Defines compute capacity: X-Small to 6X-Large — each size doubles credits/hour and parallelism • choose based on query complexity and concurrency | |
ALTER WAREHOUSE wh SET MIN_CLUSTER_COUNT = 2 MAX_CLUSTER_COUNT = 8; | • Scales horizontally by adding clusters for concurrent queries • MIN/MAX define scaling boundaries • AUTO_SUSPEND handles idle clusters | |
ALTER WAREHOUSE wh SET SCALING_POLICY = 'ECONOMY'; | • STANDARD starts clusters immediately • ECONOMY favors queuing to minimize cost — choose based on latency vs. budget priority | |
CREATE WAREHOUSE wh AUTO_SUSPEND = 60; | • Suspends warehouse after N seconds of inactivity — critical for cost control • 60s is aggressive, 600s balances cache retention |