ELT is a practice of data management in data warehousing where data is stored before being processed/transformed for usage. ELT stands for Extract, Load, and Transform.
ETL transforms data before loading it into a warehouse or any database, while ELT loads data first and then performs the transformations.
The modern cloud-based data platforms are all being designed to suit ELT needs because of the following reason:
- ELT reduces the turn-around-time (TAT) from planning to getting data in the warehouse.
- ELT enables the end user with more flexibility to transform data on a need-basis instead of having to prepare for it while setting up the pipeline.
When to choose ETL over ELT?
A real-life example of when someone might choose ETL over ELT is in the context of a retail company. Let's say the company collects data from various sources such as point-of-sale systems, online transactions, and customer feedback. Before loading this data into their data warehouse, they need to clean and structure it to ensure consistency and accuracy.
For instance, they may need to remove duplicates, standardise product names, and categorise customer feedback into specific topics. By using ETL, they can perform these data transformations before loading the data into the warehouse. This allows them to have clean, structured data readily available for analysis and decision-making.
Read more about ETL, Data ingestion, and streaming.
