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ETLvsELT
What is the difference between ETL and ELT?
Updated 3 min read7 differences
In short
ETL transforms data on a separate system and loads only clean results; ELT loads raw data into the warehouse first and transforms it there, usually with SQL.
ETL
Extract, Transform, Load
ETL is a data integration process that extracts data from source systems, transforms it into a clean, consistent shape, and loads it into a target store.
Read the page on ETLELT
Extract, Load, Transform
ELT is a data integration approach that extracts data from sources, loads it raw into a data warehouse, and then transforms it there, usually with SQL.
Read the page on ELTETL and ELT compared
| Aspect | ETL | ELT |
|---|---|---|
| Order of steps | Extract, transform, then load | Extract, load, then transform |
| Where transforms run | A separate processing server or engine | Inside the target warehouse or lakehouse |
| What the target stores | Only cleaned, ready-to-use data | Raw data plus the cleaned tables built from it |
| Typical tools | Integration platforms, Spark jobs, custom scripts | Ingestion tools plus SQL models, often with dbt |
| Reprocessing | Extract the data again from the sources | Rerun the SQL on the raw copy already stored |
| Sensitive data | Masked or dropped before loading | Lands raw, so warehouse access controls must protect it |
| Best for | Strict compliance, limited targets, older on-premises warehouses | Cloud warehouses, changing questions, teams fluent in SQL |
The difference, explained
ETL and ELT are two ways to move data from the systems where it is created into a warehouse or lake where it is analyzed. Both extract data from sources such as application databases, SaaS tools and logs; they differ in when and where the transform step happens. ETL, extract, transform, load, cleans and reshapes the data on its way, in a separate processing system, and loads only the finished result. ELT, extract, load, transform, copies the raw data into the target first and transforms it there.
The order changed because the economics changed. When warehouses were expensive machines in a company's own data center, it made sense to shrink and clean data before it arrived, so ETL was the standard for decades. In the 2010s, cloud warehouses such as BigQuery, Snowflake and Amazon Redshift made storage cheap and computing power easy to scale, so loading everything raw and transforming it with SQL, often managed with dbt, became simpler and more flexible.
Keeping the raw copy is ELT's main advantage: when a bug is fixed or a new question comes up, you rerun the SQL instead of extracting the data again. ETL's main advantage is control: sensitive fields can be dropped, masked or validated before they ever reach the warehouse, and the target only stores clean tables. ELT moves that responsibility into the warehouse, where access controls matter more and heavy transformations add to the bill.
A common misconception is that ELT has replaced ETL. Many real pipelines mix both: light cleaning or anonymization on the way in, then the heavy modeling in the warehouse. Another is thinking that ETL or ELT is a product; both are patterns, and the same orchestration tools can run either.
Which one should you use?
Choose ETL when…
- Personal or regulated data must be filtered or anonymized before it reaches the target.
- The target can't handle heavy processing, such as an operational database or a small warehouse.
- Transformations need a general-purpose language, for example to parse files or call services.
- The target should hold only clean, validated data.
Choose ELT when…
- You load into a cloud warehouse or lakehouse that scales its own computing power.
- Questions change often, and you want to rebuild results from the raw data.
- Your analysts know SQL and should own the transformations.
- You want to load new sources quickly and model them later.
Readers ask
Is ELT better than ETL?
Neither is better in general. ELT is the common default with cloud warehouses because it is flexible and keeps raw data; ETL fits when data must be cleaned or protected before it lands.
Is dbt an ETL or an ELT tool?
dbt handles the T in ELT: it runs SQL transformations inside the warehouse. It doesn't extract or load data; ingestion tools do that part.
What is reverse ETL?
Reverse ETL copies cleaned data from the warehouse back into operational tools, such as a CRM or a marketing platform, so teams can act on it. It is the same idea pointed in the other direction.