What is the difference between ETL and ELT?

Definition: ETL (extract, transform, load) transforms data before loading it into a target system, while ELT (extract, load, transform) loads raw data first and transforms it inside the warehouse or lakehouse.

ETL

ETL grew up when warehouse compute was expensive, so data was cleaned and shaped on a separate server before loading. It is still useful when sensitive fields must be removed before data lands, or when the target cannot run heavy transformations.

ELT

Cloud platforms such as Snowflake, BigQuery and Databricks made it affordable to load raw data and transform it with SQL in place. Tools such as Fivetran or Airbyte handle extraction and loading, and dbt manages transformations as versioned, tested code. Keeping raw data also makes it easy to rebuild models later.

Choosing between them

Many teams use ELT by default and add ETL steps for privacy, such as PII redaction, or for streaming sources. Both are types of data pipeline.

How Ryz helps

Ryz Snowflake developers and data engineers build and test pipelines in your stack, working on your team.

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