Thinking of building out an ETL process or refining your current one? Read more to learn about how ETL tools give you time to focus on building data models. ETL stands for extract-transform-load, and is commonly used when referring to the process of data integration. Extract refers to pulling data from a particular data source. Transforms are used to make that data into a processable format. Load is the final step to drop the data into the designated target.

The key differences between Fivetran, MuleSoft, and Xplenty: Hiring a data scientist or engineer can cost up to $140,000 per year —something many businesses can't afford. Still, organizations need to pull data from different locations into a data lake or warehouse for business insights. An Extract, Transform, and Load (ETL) platform makes this process easier, but few organizations have the technical or coding know-how to make it happen.

Companies use their data to accelerate business growth and overtake their competitors. To achieve this, they invest a lot in their ETL (extract-transform-load) operations, which take raw data and transform it into actionable information. It’s no wonder, then, that ETL testing is a crucial part of a well-functioning ETL process, since the ETL process generates mission-critical data.

Learn why ELT is better than ETL and how you can get started with it.