ETL, which stands for Extract, Transform, and Load, is a crucial process in data engineering that facilitates the movement of data from various sources to a target system.

The first step, Extract, involves collecting data from multiple sources such as databases, files, or APIs. This step is essential for preparing the data for transformation. For instance, a business might extract sales data from a CRM system or social media platform.

The second step, Transform, is where the data is processed and converted into a suitable format for analysis. This may include cleaning the data, merging datasets, or applying business rules to derive meaningful insights. The transformation process can be resource-intensive, especially when dealing with large volumes of data.

Finally, the Load step involves transferring the transformed data into a target system, such as a data warehouse or database, where it can be accessed for reporting and analysis. This step ensures that the data is available for end-users and applications.

For more detailed insights, you can refer to the relevant documents ¹⁵.