Extract, load, transform
In today's world, Extract, load, transform has become a topic of increasing interest to a wide range of sectors. As society progresses over time, the importance of Extract, load, transform becomes increasingly evident, as it impacts our lives in ways we couldn't even imagine before. From its influence on the economy to its role in popular culture, Extract, load, transform has captured the attention of academics, experts, and consumers alike. In this article, we will explore the various dimensions of Extract, load, transform and its relevance in the contemporary world.
 | 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]
Benefits
Some of the benefits of an ELT process include speed and the ability to handle both structured and unstructured data.[5]
Cloud data lake components
Common storage options
- AWS
- Azure
- Azure Blob Storage
- GCP
- Google Storage (GCS)
Querying
- AWS
- Redshift Spectrum
- Athena
- EMR (Presto)
- Azure
- GCP
- BigQuery
References
- ^ "What is ELT (Extract, Load, Transform)? | IBM". www.ibm.com. October 2021. 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 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.
- ^ Mishra, Tanya (2023-09-02). "ETL vs ELT: Meaning, Major Differences & Examples". Analytics Insight. Retrieved 2024-01-30.
External links
- Dull, Tamara, "The Data Lake Debate: Pro is Up First", smartdatacollective.com, March 20, 2015.
- ELT: Extract, Load, and Transform A Complete Guide | Astera Software
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