![]() It is a data integration process that combines data from multiple data sources into a single, consistent data store that is loaded into a data warehouse or other target system. However, there are several distinct differences between ELT and ETL, which stands for extract, transform and load. It’s possible to confuse ELT with its sister process known by a nearly identical acronym. Formatting the data into tables or joined tables based on the schema deployed in the warehouse.Removing, encrypting, hiding, or otherwise protecting data governed by government or industry regulations.This may include everything from changing row and column headers for consistency to converting currencies or units of measurement as well as editing text strings and adding or averaging values-whatever is needed to suit the organization’s specific BI or analytical purposes. Performing calculations, translations, data analysis or summaries based on the raw data.Filtering, cleansing, de-duplicating, validating and authenticating the data.In this stage, a schema-on-write approach is employed, which applies the schema for the data using SQL, or transforms the data, prior to analysis. Typically, ELT takes place during business hours when traffic on the source systems and the data warehouse is at its peak and consumers are waiting to use the data for analysis or otherwise. In this step, the transformed data is moved from the staging area into a data storage area, such as a data warehouse or data lake.įor most organizations, the data loading process is automated, well-defined, continuous and batch-driven. ![]() ![]() That said, it is more typically used with unstructured data. The data set can consist of many data types and come from virtually any structured or unstructured source, including but not limited to: Extractĭuring data extraction, data is copied or exported from source locations to a staging area. ELT consists of three primary stages Extract, Load, and Transform. ![]()
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