For decades, the process for getting data ready for analysis was set in stone: Extract, Transform, and then Load (ETL). You pulled data from various sources, cleaned and reshaped it on a separate server, and then loaded the polished result into a data warehouse. This was a rigid, upfront process. If you later realized you needed a different piece of data from the source, you had to start all over.

The rise of cloud data warehouses flipped this model on its head. The new philosophy is Extract, Load, Transform (ELT). You extract raw data from your sources and immediately load it into the cloud warehouse. All the cleaning, joining, and reshaping happens later, directly inside the warehouse, using its powerful processing engine. This "load first, transform later" approach is a fundamental shift. It preserves the raw, untouched data, giving you the flexibility to go back and change your transformation logic anytime without re-running the entire extraction pipeline.

This shift to ELT wasn't just a change in preference; it was enabled by a radical change in technology. Traditional data warehouses bundled processing power (compute) and storage together. If you needed more storage, you had to buy more compute, and vice versa. This made storing massive amounts of raw data prohibitively expensive.

Cloud platforms like Snowflake, BigQuery, and Databricks changed the game by decoupling compute from storage. Storage became incredibly cheap and virtually limitless. Compute resources could be spun up on demand to run transformations and then shut down when finished. This means you only pay for the processing power you actually use, making it economically feasible to load everything first and transform it as needed. You can store petabytes of raw data affordably and then apply massive computational power for a few minutes or hours to process it.