What is the difference between ETL and ELT? When would you choose one over the other?

Question

Accepted Answer

ETL and ELT are both data integration patterns, but they differ in where the transformation happens and are suited for different scenarios.

In ETL, transformation happens before loading. Data is extracted from sources, transformed in a separate processing layer or ETL tool, and then loaded into the target system in its final form. The transformation engine is typically outside the target database. Traditional ETL tools like Informatica, Talend, and SSIS follow this pattern.

In ELT, data is loaded first in its raw form, and transformation happens afterward inside the target system. Data is extracted from sources, loaded directly into a data lake or modern cloud warehouse, and then transformed using the processing power of the target system. This pattern leverages the scalable compute capabilities of platforms like Snowflake, BigQuery, Databricks, and Redshift.

Choose ETL when working with traditional on-premises data warehouses that have limited processing power. ETL is also appropriate when transformations require specialized tools or when you need to reduce load on the target system. It works well when data volumes are moderate and transformation logic is complex.

Choose ELT when using modern cloud data platforms that can scale compute resources. ELT is ideal when you want to preserve raw data for future use cases you have not yet defined. It performs better with large data volumes because cloud warehouses can parallelize transformations efficiently. ELT also allows analysts and data scientists to transform data themselves using SQL without waiting for engineering teams.

The industry trend is moving toward ELT because cloud platforms make compute cheap and scalable. However, many organizations use a hybrid approach, applying some transformations during extraction while doing others after loading.