ELT vs ETL
Also known as: ETL vs ELT, extract, transform, load, extract, load, transform
ETL (extract, transform, load) and ELT (extract, load, transform) are two ways to move data into an analytics system.
ETL cleans and reshapes data before loading it; ELT loads raw data first and transforms it inside the warehouse using SQL. While the difference looks like rearranged letters, it reflects a real shift: cheap cloud computing made it sensible to keep raw data and transform it later, whenever needs change.
ELT vs ETL, explained at three levels
At home
Think about groceries. ETL is washing and chopping everything at the store, so only prepared food comes home, and anything you didn't prep is gone. ELT is bringing all the groceries home, putting them in the fridge, and preparing each meal when you decide what to cook. With data, the kitchen is the warehouse, and ELT keeps the raw ingredients there.
At work
ETL and ELT are two ways of moving data from business systems into an analytics platform. ETL, extract, transform, load, cleans and reshapes data in a separate tool before loading it. ELT, extract, load, transform, loads raw data first and transforms it inside the warehouse. Businesses care because ELT keeps the raw data, so new questions can be answered without going back to the source systems.
At home
Both approaches have three steps, done in a different order. Extract copies data out of a source, such as a store's sales system. Transform cleans it, fixing formats, removing duplicates, and joining tables. Load writes it into the analytics database. In ETL, transformation happens on the way in, so only the finished result arrives. In ELT, raw data lands first, and SQL running inside the warehouse builds clean tables from it.
At work
A typical ELT stack has three layers. Managed connectors, such as Fivetran or Airbyte, copy raw data from applications into a cloud warehouse or lakehouse. Transformation tools such as dbt then run SQL inside the warehouse, building staging, intermediate, and reporting tables. An orchestrator such as Airflow schedules and monitors the steps. ETL tools, such as Informatica, still dominate where data must be masked, validated, or heavily reshaped before it reaches the warehouse.
At home
The shift from ETL to ELT followed cost. When warehouse computing was expensive and fixed, it made sense to transform elsewhere and load only what you needed. Cloud warehouses bill by usage and scale on demand, so transforming inside them became cheaper and simpler. While that flexibility is ELT's strength, in my view it's also its trap: transformations multiply, and the warehouse bill quietly becomes the largest line in the data budget.
At work
ELT lands raw personal data in the warehouse, so masking, column-level access controls, and retention rules must be enforced there; under Canadian privacy law, that raw copy is still personal information you're accountable for. Many teams use a hybrid, filtering or pseudonymizing sensitive fields in flight and doing heavier modelling in the warehouse. Change data capture now feeds ELT continuously, cutting latency from a nightly batch to minutes, but raising cost and schema-change risk.
Real-world examples
At home
Aisha built a dashboard of her personal finances. She loaded raw bank exports into a free cloud database first, then wrote SQL to categorize spending. When she changed her categories, she simply reran the SQL.
At work
Jerome is a data engineering manager at a Saskatchewan agriculture company. Their old ETL jobs dropped fields no one had asked for. After switching to ELT, analysts found an unused weather field that improved crop yield forecasts.
How it compares
| feature | ETL | ELT |
|---|---|---|
| Order | Transform, then load | Load, then transform |
| Where transformation runs | Separate processing tool | Inside the warehouse |
| Raw data kept | Often not | Yes |
| Privacy control | Easier to mask before loading | Must be handled in the warehouse |
ELT trades upfront cleaning for flexibility later.
For analysts
Requirements change, and ELT handles that better because the raw data is still there. I'd still ask where personal information gets masked, because 'load everything' can quietly become 'expose everything' to every analyst.
History & origin
ETL tools emerged in the 1970s and 1980s and became a major software category in the 1990s with products such as Informatica and IBM DataStage, when warehouse storage and computing were expensive. Cloud warehouses changed the economics: Google BigQuery (previewed in 2010), Amazon Redshift (2012), and Snowflake (generally available in 2015) offered elastic computing and cheap storage. Tools such as Fivetran and Stitch automated loading raw data, and dbt, launched in 2016, made SQL transformations inside the warehouse manageable. ELT became the default 'modern data stack' pattern by around 2020. In my opinion, ELT's real advantage isn't speed. It's that you can fix a transformation mistake without going back to the source systems.