Many organizations regularly perform this process to keep their data warehouse updated.
Once all the data has been loaded, the process is complete. This could be a target database, data warehouse, data store, data hub or data lake - on-premises or in the cloud. Loadįinally, the load phase moves the transformed data into a permanent target system. It also makes the data fit for consumption for analytics, business functions and other downstream activities. This helps to normalize, standardize and filter data. Typical transformations include aggregators, data masking, expression, joiner, filter, lookup, rank, router, union, XML, Normalizer, H2R, R2H and web service. The goal of transformation is to make all data fit within a uniform schema before it moves on to the last step. In the transformation phase, data is processed to make its values and structure conform consistently with its intended use case. Data that fails validation is rejected and doesn’t continue to the next step. This tests whether data meets the requirements of its destination.
It is then held in temporary storage, where the next two steps are executed.ĭuring extraction, validation rules are applied. Here’s a quick summary: ExtractĮxtraction is the first phase of “extract, transform, load.” Data is collected from one or more data sources. This could be a database, data warehouse, data store or data lake. ETL moves data in three distinct steps from one or more sources to another destination.
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