![]() ![]() ![]() Learn more about Extract, Transform, Load (ELT) and the difference between ELT and ETL. This is done to automate the process, reduce repetitive tasks and manage large amounts of data more efficiently. Once data transformation is completed, data is loaded from the temporary staging area into the target data repository. ![]() Processing data often involves some of the following functions: Raw data is then transformed within the staging area. Data sources can include but are not limited to: This data is temporarily stored in a staging area. Raw structured or unstructured data is extracted either by being exported or copied from one or many data sources. ExtractĮxtraction is the first step in the ETL process. How ETL worksĭescribing each step of the extract, transform and load process is the best way to understand how ETL works. Organizations today use ETL for the same reasons: to clean and organize data for business insights and analytics.ĮTL is also used to describe the commercial software category that automates the three processes. It became a common method of data integration in the 1970s as a way for businesses to use data for business intelligence. Through the ETL process, data is properly formatted, normalized and loaded into these types of data storage systems to create a single, unified data view.Īn acronym for extract, transform, load, ETL is used as shorthand to describe the three stages of preparing data. ETL is a three-step data integration process that extracts, transforms, and loads raw data from a source or multiple sources to a data warehouse, data mart, data lake, or database. Extract > Transform > Load (ETL) In the ETL process, transformation is performed in a staging area outside of the data warehouse and before loading it into the data warehouse. ![]()
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