Extract, load, transform
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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. 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.[1]
Cloud data lake components[]
Common storage options[]
- AWS
- Simple Storage Service (S3)
- Amazon RDS
- Azure
- GCP
- Google Storage (GCS)
Querying[]
- AWS
- Azure
- GCP
References[]
- ^ Using Redshift Spectrum to load data pipelines Published by deductive.com on January 17, 2018, retrieved on April 3, 2019
External links[]
- Dull, Tamara, "The Data Lake Debate: Pro is Up First", smartdatacollective.com, March 20, 2015.
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