As part of the data integration workflow, data transformation is the process of preparing, formatting, and joining data for placement in a data repository or data warehouse for analytics. And for decades, the data transformation method of choice has been ETL (extract, transform, load). Today, thanks to the rise of new-generation cloud data warehouses, a second option has become popular: ELT (extract, load, transform).
The choice between ETL and ELT depends on several factors that are unique to each organization, including data schema requirements, transformation complexity, performance, and budget constraints, just to name a few. And that’s what we’ve outlined in this guide.
We’ll cover both ETL and ELT approaches, including:
- A side-by-side comparison of functionality between ETL and ELT
- Considerations for which approach is best for your organization
- How to simplify the data transformation process with either approach
Whether you’re a data practitioner or a technology executive, read on to learn the functional differences between the two processes and explore their respective considerations and use cases.