In the context of data integration, what does ETL stand for?

ETL stands for Extract, Transform, Load, and is a fundamental process in data integration and data warehousing. This process involves three key steps:

1. Extract: In this initial stage, data is collected from various sources. These sources could be databases, flat files, or web services. The extraction process ensures that relevant and necessary data is pulled for further processing.

2. Transform: During this phase, the extracted data undergoes transformation to match specific formats, structures, or standards required for analysis. This can include cleaning the data (removing duplicates, correcting errors), enriching it (adding additional information), filtering it (selecting only necessary data), and converting data types.

3. Load: Finally, the transformed data is loaded into a destination system, such as a data warehouse or a database, where it can be accessed and analyzed. The loading process can occur in real-time or during scheduled intervals depending on the needs of the organization.

Understanding ETL is crucial for anyone involved in data engineering, as it supports the integration of data from disparate systems, enabling more efficient data analysis and business intelligence. The other options do not encompass these critical components of the data integration process.