ETL vs ELT: Which One Should You Use and Why?

What’s the Difference Between ETL and ELT? What is ETL? Extract, transform, and load (ETL) is the process of combining data from multiple sources into a large, central repository called a data warehouse. ETL uses a set of business rules to clean and organize raw data and prepare it for storage, data analytics, and machine learning (ML). What is ELT? Extract, load, and transform (ELT) is an extension of extract, transform, and load (ETL) that reverses the order of operations. You can load data directly into the target system before processing it. The intermediate staging area is not required because the target data warehouse has data mapping capabilities within it. ELT has become more popular with the adoption of cloud infrastructure, which gives target databases the processing power they need for transformations. ETL process ETL has three steps: You extract raw data from various sources You use a secondary processing server to transform that data You load that data into a target databa