Snowflake is a cloud-native data platform built from scratch for the cloud, offering a unique multi-cluster shared data architecture that separates compute, storage, and services into independent, scalable layers. Unlike traditional data warehouses, Snowflake runs entirely on major cloud providers (AWS, Azure, GCP) with zero infrastructure management, enabling instant elasticity, multi-region deployment, and seamless data sharing across organizations. What makes Snowflake particularly powerful is its ability to handle both structured and semi-structured data (JSON, Parquet, Avro) natively using VARIANT columns, while providing enterprise features like time travel, zero-copy cloning, and automatic optimization—all accessible through standard SQL. The platform's consumption-based pricing and serverless compute model mean you only pay for what you use, making it ideal for workloads ranging from BI analytics to data engineering pipelines and machine learning.
What This Cheat Sheet Covers
This topic spans 20 focused tables and 140 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.
Table 1: Virtual Warehouse Fundamentals
| Concept | Example | Description |
|---|---|---|
CREATE WAREHOUSE analytics_wh WITH WAREHOUSE_SIZE = 'MEDIUM' AUTO_SUSPEND = 300; | • Named compute cluster that executes queries • independent from storage and billed by compute seconds used. | |
ALTER WAREHOUSE etl_wh SET WAREHOUSE_SIZE = 'XLARGE'; | • Sizes from X-Small to 6X-Large • each doubling doubles both compute power and cost (X-Small = 1 credit/hour, Medium = 4). | |
AUTO_SUSPEND = 60 | • Automatically pauses warehouse after N seconds of inactivity • minimizes idle compute costs. | |
AUTO_RESUME = TRUE | • Automatically starts suspended warehouse when query is submitted • enabled by default for seamless query execution. |