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The ETL process is crucial for data processing in business intelligence and analytics. It enables organizations to combine, clean, and structure their data from various sources. Through ETL, companies can gain valuable insights into their data and make informed decisions.
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Extract, Transform, Load (ETL) describes a data pipeline: data is taken from sources, standardised, and loaded into a target system.
ETL emerged with the spread of data warehouses and the need to prepare heterogeneous operational sources for shared analysis. The term therefore describes a data-integration lineage from classic batch processes to today’s cloud pipelines.
A pipeline reads source data, applies rules such as type conversion and cleansing, and writes the result to a warehouse or another store. Error handling, repeatability, and data lineage determine its reliability.
The conceptual focus and typical structure of the approach.
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ETL helps design data-integration flows, clarify responsibilities, and assess the quality of analytical data.
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