ETL stands for Extract, Transform, Load. It is a process used to move data from one source to another. ETL involves extracting data from one or more sources, transforming it into the desired format, and then loading it into a target database or data warehouse. ETL is performed in order to ensure the data is accurate and up-to-date and is used by businesses to make informed decisions.
The first step of ETL is the extraction process, in which data is extracted from one or more sources and stored in a staging area. This staging area is essentially a temporary storage space where data is manipulated in preparation for loading into a final destination. The extract process can involve simple loading of data from an existing database, or more complex procedures that require custom queries to select specific data from numerous sources.
Once the data has been extracted from its source, ETL moves on to the transformation process. During this step, the data is filtered, transformed, or manipulated into a format that is compatible with the target system. This could include reformatting dates, converting numeric values, or separating data that needs to be split across multiple tables. It is during this stage that the data is validated, cleansing rules are applied, and data integration takes place.
The final phase of ETL is the loading phase, where data is loaded into its target system. This step typically involves transferring the data from the staging area to the data warehouse or other target system. Once the data is loaded, it can then be used for analytics and reporting.
ETL processes are important for businesses because they ensure its data is accurate, up-to-date, and consistent across all systems. By using these processes, organizations can have faith in the data it has and use it for better decision making.
