What is ETL vs ELT?

Definition
ETL (Extract Transform Load) and ELT (Extract Load Transform) are patterns for moving data from operational systems to analytical warehouses. They differ in WHERE transformations happen.
The Core Problem: Companies have operational databases optimized for fast transactions (millisecond latencies) but analytics teams need to scan terabytes of history. These OLTP (Online Transaction Processing) databases cannot handle analytical queries without degrading performance.

You need dedicated analytical storage and pipelines that reshape operational data into queryable datasets. An ecommerce platform might have separate databases for users, orders, payments, and clickstream events. Analytics teams want to join years of this data to measure metrics like customer lifetime value or conversion funnels.

How ETL Works: Data is extracted from sources, transformed on a separate processing system (cleaning, joining, aggregating), then loaded into the warehouse. Only clean, modeled data reaches the analytical environment.

How ELT Works: Raw data is loaded first into the analytical warehouse or lakehouse. Transformations run inside the warehouse using its compute power. Analysts work with both raw and curated data.

⚠️ Common Pitfall: This is not about which is "better." ETL gives control and governance. ELT gives flexibility and speed. Production systems often use both patterns for different data types.

The decision impacts where you spend compute resources, how you enforce data quality, and whether you can answer unexpected questions without rebuilding pipelines from scratch.

💡 Key Takeaways

✓ETL transforms data before loading into the warehouse, only storing clean and modeled datasets

✓ELT loads raw data first into the warehouse, then transforms using warehouse compute resources

✓ETL provides stricter governance since only validated data enters the analytical environment

✓ELT enables flexibility because raw data is preserved for future unknown use cases

✓Production systems commonly use both patterns: ETL for regulated data like payments, ELT for behavioral data like clickstreams

📌 Examples

1ETL example: Extract daily order transactions, join with customer and product reference data, apply business rules for refunds, then load summary fact tables into the warehouse

2ELT example: Stream clickstream events directly into a raw zone in Snowflake within 30 seconds, then run hourly sessionization jobs inside Snowflake to create curated tables

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