Extract, load, transform
In this article, we will explore the exciting world of Extract, load, transform and everything that this theme has to offer. From its origins to its impact today, we will immerse ourselves in a journey of discovery to fully understand the importance and relevance of Extract, load, transform in our lives. Through an exhaustive analysis, we will examine the different facets and aspects that make Extract, load, transform a topic of universal interest, addressing everything from its social impact to its global implications. With interviews with experts, statistical data and diverse perspectives, this article aims to be a complete guide that unravels the mysteries and virtues of Extract, load, transform, offering a comprehensive vision that allows the reader to delve into the fascinating universe of this topic.
 | 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[usurped] 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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