Why businesses need ETL, ELT, and Reverse ETLData coming into an organization rarely have similar or compatible formats. They may be structured or unstructured, aligning with the organization's existing data models or not at all. ETL, ELT, and reverse ETL help clean and standardize (or normalize) the incoming data before unifying them with the rest of the organization's data. This way, when business teams retrieve information from their central warehouse or end systems, they can view a complete picture, and derive useful, actionable insights. Regardless of the method you use, whether ETL vs. ELT vs. Reverse ETL, your data management process would involve the following actions:
- Extract: Extracting is the process of pulling data from the data source or original database.
- Transform: Transforming is the process of cleaning, normalizing, and unifying the structure of data gathered in order to integrate with the rest of your data.
- Load: Loading is the process of moving data into its final data destination.
Overview of ETL, ELT, and Reverse ETLLet's begin with definitions:
Extract, Transform, Load (ETL)ETL extracts and moves data from one or many data sources to a staging area where the data is cleaned, normalized, transformed, and integrated into a central data warehouse. In summary, ETL integrates data following this order:
- Extracts raw data from various individual systems and sources
- Transforms data on a secondary processing server
- Loads the data into a central database or data warehouse
Extract, Load, Transform (ELT)ELT extracts and moves data straight into a data warehouse to handle integration, skipping staging on a separate processing server. In summary, ELT integrates data following this order:
- Extracts data from individual systems and sources
- Loads the data into a central database or data warehouse and transforms them according to each organization’s setup