 | This article relies largely or entirely on a single source. (November 2023) |
Extract, load, transform (ELT) is an alternative to extract, transform, load (ETL) used with data lake implementations. In contrast to ETL, in ELT models the data is not transformed on entry to the data lake, but stored in its original raw format. This enables faster loading times. However, ELT requires sufficient processing power within the data processing engine to carry out the transformation on demand, to return the results in a timely manner.[1][2] Since the data is not processed on entry to the data lake, the query and schema do not need to be defined a priori (although often the schema will be available during load since many data sources are extracts from databases or similar structured data systems and hence have an associated schema). ELT is a data pipeline model.[3][4]
- ^ "What is ELT (Extract, Load, Transform)? | IBM". www.ibm.com. Retrieved 2024-01-30.
- ^ Abdullahi, Aminu (2023-06-30). "ETL vs ELT: What Are the Main Differences and Which Is Better?". TechRepublic. Retrieved 2024-01-30.
- ^ Using Redshift Spectrum to load data pipelines Archived 2021-01-18 at the Wayback Machine Published by deductive.com on January 17, 2018, retrieved on April 3, 2019.
- ^ "What is ELT (Extract, Load, Transform)? | dbt Developer Hub". docs.getdbt.com. 2024-01-30. Retrieved 2024-01-30.