In data engineering, what does the term "ETL" stand for?

The term "ETL" stands for "Extract, Transform, Load," which is a fundamental process in data engineering. This process involves three key steps:

1. Extract: In this phase, data is pulled from various source systems, which can include databases, data warehouses, or external APIs. The extraction process is crucial as it enables the collection of raw data that will be further processed.

2. Transform: Once the data is extracted, it is transformed to fit operational needs or analysis requirements. This can involve cleaning the data, applying business rules, aggregating information, and converting data types. The transformation step is vital for ensuring that the data is accurate and usable.

3. Load: The final step involves loading the transformed data into a target data warehouse or database, where it can be accessed and queried by business intelligence tools or other applications. This step ensures that end-users have access to the data in a structured format suitable for analysis.

The other choices do not accurately represent the standard definition of ETL in data engineering. The meanings proposed in those options do not align with the established terminology and practices within the field. Thus, "Extract, Transform, Load" is the correct and accepted definition of ETL in data