Editing Extract, transform, load (section)

=== Extract, load, transform (ELT) ===

{{main|Extract, load, transform}}
[[Extract, load, transform]] (ELT) is a variant of ETL where the extracted data is loaded into the target system first.<ref name="AWS Data Warehousing 9" >Amazon Web Services, Data Warehousing on AWS, p. 9</ref> The architecture for the analytics pipeline shall also consider where to cleanse and enrich data<ref name="AWS Data Warehousing 9" /> as well as how to conform dimensions.<ref name="Kimball 2004" /> Some of the benefits of an ELT process include speed and the ability to more easily handle both unstructured and structured data.<ref>{{Cite web |last=Mishra |first=Tanya |date=2023-09-02 |title=ETL vs ELT: Meaning, Major Differences & Examples |url=https://www.analyticsinsight.net/etl-vs-elt-meaning-major-differences-examples/ |access-date=2024-01-30 |website=Analytics Insight}}</ref>

[[Ralph Kimball]] and [[Joe Caserta]]'s book The Data Warehouse ETL Toolkit, (Wiley, 2004), which is used as a textbook for courses teaching ETL processes in data warehousing, addressed this issue.<ref>{{Cite web|url=https://www.oreilly.com/library/view/the-data-warehouse/9780764567575/|title = The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data &#91;Book&#93;}}</ref>

Cloud-based data warehouses like [[Amazon Redshift]], Google [[BigQuery]], [[Microsoft Azure Synapse Analytics]] and [[Snowflake Inc.]] have been able to provide highly scalable computing power. This lets businesses forgo preload transformations and replicate raw data into their data warehouses, where it can transform them as needed using [[SQL]].

After having used ELT, data may be processed further and stored in a data mart.<ref>Amazon Web Services, Data Warehousing on AWS, 2016, p. 10</ref>

Most data integration tools skew towards ETL, while ELT is popular in database and data warehouse appliances. Similarly, it is possible to perform TEL (Transform, Extract, Load) where data is first transformed on a blockchain (as a way of recording changes to data, e.g., token burning) before extracting and loading into another data store.<ref>{{cite book |last1=Bandara |first1=H. M. N. Dilum |last2=Xu |first2=Xiwei |last3=Weber |first3=Ingo |title=Proceedings of the European Conference on Pattern Languages of Programs 2020 |chapter=Patterns for Blockchain Data Migration |year=2020 |pages=1–19 |doi=10.1145/3424771.3424796 |arxiv=1906.00239|isbn=9781450377690 |s2cid=219956181 }}</ref>