![]() It also describes which source field maps to which destination field. Mapping provides detailed instructions to an application about how to get the data it needs to process. The transformed data is then loaded into the target.ĭata mapping is part of the transformation process. Transformations, business rules and adaptersĪfter extracting data, ETL uses business rules to transform the data into new formats. Structured query language is the most common method of accessing and transforming data within a database. ETL and ELT are both important parts of an organization’s broader data integration strategy. Extract, transform, load is now just one of several methods organizations use to collect, import and process data. Over time, the number of data formats, sources and systems has expanded tremendously. Coupled with mergers and acquisitions, many organizations wound up with several different ETL solutions that were not integrated. But different departments often chose different ETL tools to use with different data warehouses. A distinct type of database, data warehouses provided integrated access to data from multiple systems – mainframe computers, minicomputers, personal computers and spreadsheets. ![]() In the late 1980s and early 1990s, data warehouses came onto the scene. ETL became the standard method for taking data from disparate sources and transforming it before loading it to a target source, or destination. The need to integrate data that was spread across these databases grew quickly. ETL gained popularity in the 1970s when organizations began using multiple data repositories, or databases, to store different types of business information.
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