ETL vs ELT: what's the difference?
ETL (Extract, Transform, Load) transforms data before loading it into the destination store; ELT (Extract, Load, Transform) loads raw data first and transforms it inside the destination. ELT suits modern cloud warehouses (transform with the warehouse's compute); ETL suits cases needing cleansing or compliance before data lands.
Key takeaways
- ETL transforms data before loading; ELT loads first, then transforms inside the destination.
- ELT fits modern cloud warehouses; ETL fits cleansing or compliance before data lands.
- Both are techniques — data unification is the outcome they help deliver.
ETL (Extract, Transform, Load)
Data is pulled from sources, cleaned and reshaped in a staging layer, then loaded into the destination already in its final form. This is useful when data must be validated, masked or made compliant before it lands.
ELT (Extract, Load, Transform)
Raw data is loaded into a cloud warehouse first, then transformed there using the warehouse's compute. ELT is flexible and scalable for modern cloud stacks, and keeps the raw data available for re-modelling later.
Which should you use?
For most cloud-based analytics today, ELT is the default — storage is cheap, the warehouse has the compute, and keeping raw data lets you re-model later. Reach for ETL when data must be cleansed, masked or made compliant before it's allowed to land. Many real systems use both.
How this relates to data unification
ETL and ELT are techniques for moving and shaping data. Data unification is the goal — one connected, entity-resolved database across all your tools. Either technique can be used to deliver it; the outcome the business cares about is the single source of truth, not the pipeline pattern.
Related questions
Neither is universally better. ELT fits modern cloud warehouses and large, flexible workloads; ETL fits cases needing transformation or compliance before data lands. Many real systems use a mix.