ETL (Extract, Transform, Load) Cheat Sheet

Updated 2026-04-21

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ETL is the foundational data integration pattern that moves data from source systems to target destinations, transforming it along the way to meet analytical or operational requirements. It powers data warehouses, business intelligence, and analytics platforms across industries by ensuring clean, consistent, and queryable data. The key distinction: transformations happen before loading (unlike ELT, where transformations occur after loading into the destination). Understanding ETL patterns, from extraction strategies to slowly changing dimensions, is essential for building reliable, scalable, and performant data pipelines that teams trust.

What This Cheat Sheet Covers

This topic spans 16 focused tables and 163 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: Core ETL ConceptsTable 2: Extraction TechniquesTable 3: Transformation TechniquesTable 4: Loading StrategiesTable 5: Data Quality PatternsTable 6: Error Handling and RecoveryTable 7: Performance OptimizationTable 8: Dimensional Modeling ConceptsTable 9: Slowly Changing Dimensions (SCD)Table 10: Data Orchestration and SchedulingTable 11: ETL Tools and PlatformsTable 12: Metadata and GovernanceTable 13: Testing and ValidationTable 14: Monitoring and ObservabilityTable 15: Security and ComplianceTable 16: Advanced Patterns

Table 1: Core ETL Concepts

Concept	Example	Description
ETL (Extract, Transform, Load)	`Extract from DB → Transform in pipeline → Load to warehouse`	• Data integration pattern where transformation happens before loading • ensures clean, validated data enters the target system.
ELT (Extract, Load, Transform)	`Extract from DB → Load to warehouse → Transform with SQL`	• Data lands raw in the destination, then transformed using the warehouse's compute • default for modern cloud warehouses like Snowflake and BigQuery.
Data pipeline	`Source → Ingestion → Transformation → Destination → Monitoring`	• End-to-end workflow that orchestrates data movement through multiple stages • ETL is one type of pipeline architecture.
Staging area	`raw_layer` `staging_db` `landing_zone`	• Temporary storage for extracted data before transformation • allows validation and rollback without touching production sources.
Data warehouse	`Snowflake` `BigQuery` `Redshift`	• Centralized repository optimized for analytical queries • typically the target destination for ETL processes.

Table 1: Core ETL Concepts

Concept	Example	Description
ETL (Extract, Transform, Load)	`Extract from DB → Transform in pipeline → Load to warehouse`	• Data integration pattern where transformation happens before loading • ensures clean, validated data enters the target system.
ELT (Extract, Load, Transform)	`Extract from DB → Load to warehouse → Transform with SQL`	• Data lands raw in the destination, then transformed using the warehouse's compute • default for modern cloud warehouses like Snowflake and BigQuery.
Data pipeline	`Source → Ingestion → Transformation → Destination → Monitoring`	• End-to-end workflow that orchestrates data movement through multiple stages • ETL is one type of pipeline architecture.
Staging area	`raw_layer` `staging_db` `landing_zone`	• Temporary storage for extracted data before transformation • allows validation and rollback without touching production sources.
Data warehouse	`Snowflake` `BigQuery` `Redshift`	• Centralized repository optimized for analytical queries • typically the target destination for ETL processes.