January 31, 2023

First, ETL stands for extract transform and load. For those in dilemma, ETL is a data integration process that combines data from multiple data sources into single, consistent data store that is loaded into a data warehouse. As the databases continue to rapidly grow, the ETL process has become one of the major methods to process data for data warehousing projects.

It provides the foundation for data analytics and machine learning workstreams. Through a series of business regulations, ETL cleanses and organizes data in a way which addresses specific business intelligence needs. Let’s take a closer look at some of the steps involved in ETL process.

Extract

This is one of the steps involved in ETL process. During data extraction, raw data is copied or even exported from sources locations to a staging area. Data management teams can extract data from a variety of data sources, which can be structured or even unstructured. Some of these sources include; SQL or NoSQL servers, CRM, flat files, and web pages.

Transform

During staging area, the raw data undergoes data processing. Here, the data is transformed and consolidated for its intended analytical case use. In this stage, some several tasks are involved. First, cleansing, de-duplicating, validating, and authenticating the data. The next task is performing calculations, translations, or summarizations based on the raw data.

This can include changing row and column headers for consistency, converting currencies or other units of measurement, editing texts strings and more. Finally, there is formatting the data into tables or joined tables to match the schema of the target data warehouse.

The Bottom Line

Finally, the transformed data is moved from the staging area into a target of data warehouse. Typically, this involves an initial loading of all data, followed by periodic loading of incremental data changes, and, less often, full refreshes to erase and replace data.

Most organizations that use ETL process know it is automated, well-defined, and continuous and batch driven. In addition, ETL takes place during off-hours when traffic of the source systems and the data warehouse is at the lowest.

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