What is the Difference Between ETL and ELT?


ETL is a data integration method that extracts raw data from sources, transforms it on a secondary processing server, and then loads it into a target database. Raw data is transformed outside of the data warehouse, usually with the help of a dedicated “staging server,” and only transformed data is loaded into the warehouse.

Extract: Extraction is the process of extracting data from a database or data source. With ETL, the data is temporarily stored in a staging area.
Transform: The process of changing the structure of information so that it integrates with the target data system and the rest of the data in that system is referred to as transformation.
Load: The process of depositing data into a data storage system is referred to as loading.


ELT symbolize “Extract, Load, Transform”. Data is leveraged over a data warehouse in this process to perform basic transformations. This eliminates the need for data staging. For all types of data, including structured, unstructured, semi-structured, and even raw data, ELT uses cloud-based data warehousing solutions. Raw data is transformed inside the data warehouse without the use of a staging server, and your data warehouse now contains both raw and transformed data.

What Are The Differences Between ETL And ELT?

ETL and ELT difference in two fundamental ways: where data is transformed and how data warehouses hold data.

  • Data is transformed on a separate processing server with ETL, whereas data is transformed within the data warehouse itself with ELT. Raw data is not transferred into the data warehouse by ETL, whereas raw data is sent directly to the data warehouse by ELT.
  • When using ETL, the data ingestion process is slowed by transforming data on a separate server before loading. ELT allows for faster data ingestion because data is not sent to a secondary server for restructuring. In fact, ELT allows data to be loaded and transformed at the same time.
  • ELT’s raw data retention creates a rich historical archive for business intelligence generation. BI teams can re-query raw data to develop new transformations using comprehensive datasets as goals and strategies change. On the other hand, ETL does not produce complete raw data sets that can be queried indefinitely.
  • These characteristics make ELT more adaptable, efficient, and scalable, especially when it comes to ingesting large amounts of data, processing data sets that include both structured and unstructured data and developing a variety of business intelligence. On the other hand, ETL is ideal for compute-intensive transformations, systems with legacy architectures, or data workflows that require manipulation before entering a target system, such as erasing personal identifying information.


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