Snowflake Data Cloud Cheat Sheet

Back to Data EngineeringUpdated 2026-05-15

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 ConfigurationTable 2: Micro-Partitions and ClusteringTable 3: Time Travel and Fail-SafeTable 4: Zero-Copy CloningTable 5: Continuous Data IngestionTable 6: Streams and TasksTable 7: Snowpark Python APITable 8: Cortex AI FunctionsTable 9: Secure Data SharingTable 10: Data Governance PoliciesTable 11: Query Optimization FeaturesTable 12: Advanced Table TypesTable 13: Data Loading and UnloadingTable 14: File Formats and StagesTable 15: Security and Network ControlsTable 16: Resource Monitoring and Cost ControlTable 17: Data Replication and AvailabilityTable 18: Data Clean Rooms and CollaborationTable 19: Metadata and Information SchemaTable 20: Geospatial FunctionsTable 21: Advanced Query ConstructsTable 22: User-Defined ObjectsTable 23: Sequences and IdentityTable 24: Data Sampling and ProfilingTable 25: Storage Lifecycle and Archival

Table 1: Virtual Warehouse Configuration

Parameter	Example	Description
Warehouse Size	`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.
Multi-Cluster Mode	`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.
Scaling Policy	`ALTER WAREHOUSE wh SET` `SCALING_POLICY = 'ECONOMY';`	STANDARD starts clusters immediately; ECONOMY favors queuing to minimize cost — choose based on latency vs. budget priority.
Auto-Suspend	`CREATE WAREHOUSE wh` `AUTO_SUSPEND = 60;`	Suspends warehouse after N seconds of inactivity — critical for cost control; 60s is aggressive, 600s balances cache retention.

Table 1: Virtual Warehouse Configuration

Parameter	Example	Description
Warehouse Size	`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.
Multi-Cluster Mode	`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.
Scaling Policy	`ALTER WAREHOUSE wh SET` `SCALING_POLICY = 'ECONOMY';`	STANDARD starts clusters immediately; ECONOMY favors queuing to minimize cost — choose based on latency vs. budget priority.
Auto-Suspend	`CREATE WAREHOUSE wh` `AUTO_SUSPEND = 60;`	Suspends warehouse after N seconds of inactivity — critical for cost control; 60s is aggressive, 600s balances cache retention.