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ETL stands for Extract, Transform, Load, and it is a critical process in data integration and data processing within data engineering.

In this context, "Extract" refers to the process of retrieving data from various source systems, which could include databases, APIs, or flat files. The goal is to gather all relevant data that will be needed for analysis or further processing.

"Transform" involves any operations that are performed on the extracted data to convert it into a suitable format or structure for downstream use. This could include cleaning the data, filtering it, joining it with other datasets, applying business rules, or aggregating data, among other transformations.

Finally, "Load" is the process of storing the transformed data into a target database or data warehouse where it can be accessed and analyzed by end-users or business intelligence tools.

Understanding ETL is essential for a data engineer because it encompasses the fundamental workflow for making data usable, enabling analytics, and supporting decision-making in organizations.