Data build tool: Difference between revisions
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'''dbt''' is an [[Open-source software|open-source]] command line tool that helps analysts and engineers transform data in their warehouse more effectively. |
'''dbt''' is an [[Open-source software|open-source]] command line tool that helps analysts and engineers transform data in their warehouse more effectively.<ref>{{cite book |last1=Atwal |first1=Harvinder |title=Practical DataOps: Delivering Agile Data Science at Scale |date=9 December 2019 |publisher=Apress |isbn=978-1-4842-5104-1 |page=223 |url=https://books.google.co.uk/books?id=ADLDDwAAQBAJ&pg=PA223 |language=en}}</ref> |
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==Overview== |
==Overview== |
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Revision as of 16:18, 4 November 2021
 | This article needs additional citations for verification. (August 2021) |
| Developer(s) | dbt-Labs |
|---|---|
| Initial release | [1] |
| Stable release | 0.19.2
/ June 29, 2021 |
| Repository | |
| Written in | Python |
| Operating system | Microsoft Windows, macOS, Linux |
| Available in | Python |
| Type | Data analytics, algorithms |
| License | Apache License 2.0 |
| Website | docs |
dbt is an open-source command line tool that helps analysts and engineers transform data in their warehouse more effectively.[1]
Overview
dbt enables analytics engineers to transform data in their warehouses by simply writing select statements. dbt handles turning these select statements into tables and views. dbt does the transformation (T) in extract, load, transform (ELT) processes – it doesn’t extract or load data, but it’s extremely good at transforming data that’s already loaded into your warehouse. dbt also enables analysts to work more like software engineers, in line with the dbt [Viewpoint](viewpoint).
Developers
dbt-Labs (previously Fishtown Analytics) is on a mission to help analysts create and disseminate organization knowledge. dbt Labs pioneered the practice of analytics engineering, built the primary tool in the analytics engineering toolbox, and has been fortunate enough to see a fantastic community coalesce to help push the boundaries of the analytics engineering workflow. Today there are 5,500 companies using dbt every week, 15,000 folks in the dbt Community Slack, and 1,000 companies paying for dbt Cloud.
Notes
References
- ^ Atwal, Harvinder (9 December 2019). Practical DataOps: Delivering Agile Data Science at Scale. Apress. p. 223. ISBN 978-1-4842-5104-1.
