Often, the three ETL phases are run in parallel to save time. The data transformation that takes place usually involves various operations, such as filtering, sorting, aggregating, joining data, cleaning data, deduplicating, and validating data. The transformation work in ETL takes place in a specialized engine, and it often involves using staging tables to temporarily hold data as it is being transformed and ultimately loaded to its destination. It then transforms the data according to business rules, and it loads the data into a destination data store. Extract, transform, and load (ETL) processĮxtract, transform, and load (ETL) is a data pipeline used to collect data from various sources. The following sections highlight the common methods used to perform these tasks. No matter the process used, there's a common need to coordinate the work and apply some level of data transformation within the data pipeline. Various tools, services, and processes have been developed over the years to help address these challenges. Often the format is different, or the data needs to be shaped or cleaned before loading it into its final destination. The destination might not be the same type of data store as the source. Then you'd need to move it to one or more data stores.
#Extract transform load how to
A common problem that organizations face is how to gather data from multiple sources, in multiple formats.
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