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

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ETL stands for Extract, Transform, Load, which is a critical process in data integration. This procedure involves three key stages:

Extract: In the extraction phase, data is collected from various source systems. These sources can be database systems, CRM systems, flat files, or even cloud applications. The objective is to gather all relevant data that needs to be integrated into a centralized data warehouse or repository.

Transform: After the data has been extracted, it undergoes transformation to ensure it is suitable for analysis. This may involve cleaning the data (removing duplicates or errors), normalizing it (standardizing formats, units, etc.), aggregating it (summarizing data), or applying business rules. This step is crucial as it prepares the data, ensuring consistency and quality so that it adheres to the business requirements and is structured appropriately for analysis.
Load: Finally, in the loading phase, the transformed data is loaded into a target data storage system, such as a data warehouse. The load process may vary depending on whether it’s a full load or an incremental load, and it may also include further optimizations for performance.

The importance of the ETL process is paramount in data integration