ELT
ELT stands for Extract, Load, Transform. It is a data integration approach where raw data is first loaded into a data warehouse or cloud platform, then transformed inside that platform.
ELT is common in modern cloud analytics because platforms such as Snowflake, BigQuery and Databricks can process transformations at scale after data has been loaded.
Why it matters
ELT supports flexible analytics when cloud platforms become the transformation engine.
Instead of transforming data before loading it, ELT keeps more raw data available and lets data teams transform it using SQL, dbt or warehouse-native processing.
Business example
A company wants to analyze sales, marketing and customer support data together. ELT can load raw data from multiple SaaS tools first, then build trusted reporting models for each department.
Technical example
An ELT pipeline may extract data from APIs, load it into BigQuery, and use SQL models or dbt transformations to create cleaned fact and dimension tables.
Common mistakes
Loading raw data without access controls or governance.
Creating too many transformation layers without documentation.
Assuming ELT removes the need for business definitions.
Related Datilog resources
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FAQ
Common questions about ELT
What is the difference between ETL and ELT?
ETL transforms data before loading it. ELT loads data first and transforms it inside the destination platform, usually a cloud data warehouse.
Is ELT better than ETL?
Not always. ELT is useful for cloud-scale analytics, but ETL can still be better when data must be cleaned, filtered or controlled before being loaded.