What does ETL stand for in data engineering?

ETL stands for Extract, Transform, Load, which represents a crucial process in data engineering. This methodology is essential for integrating data from various sources before it is analyzed and used for decision-making or reporting.

In the first stage, "Extract," data is gathered from different data sources, which can include databases, APIs, flat files, or other sources. This phase focuses on retrieving the data needed for further processing.

The second stage, "Transform," involves manipulating the extracted data into a suitable format or structure for analysis. This may include cleaning the data, applying business rules, aggregating information, or converting data types to ensure consistency and accuracy.

Finally, the "Load" stage refers to the process of loading the transformed data into a target database or data warehouse, making it available for business intelligence tools or analysis by users.

Understanding ETL is fundamental for anyone involved in data engineering, as it provides a framework for managing data workflows and ensuring data quality and accessibility. The other options presented do not accurately reflect the standard terminology and processes recognized in the field of data engineering, making ETL the correct and widely accepted definition.