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tool_dbt
(borrowing this from getdbt.com > "What is dbt")
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 T in ELT (Extract, Load, Transform) processes – it doesn’t extract or load data, but it’s extremely good at transforming data that’s already loaded into your warehouse.
The role of dbt within a modern data stack is discussed in more detail here.
dbt also enables analysts to work more like software engineers, in line with the dbt Viewpoint.
As a dbt user, your main focus will be on writing models (i.e. select queries) that reflect core business logic – there’s no need to write boilerplate code to create tables and views, or to define the order of execution of your models. Instead, dbt handles turning these models into objects in your warehouse for you.
- Jaffle Shop: A demonstration project (closely related to the Getting Started guide) for a fictional ecommerce store (source code)
- MRR Playbook: A demonstration project that models subscription revenue (source code, docs)
- Attribution Playbook: A demonstration project that models marketing attribution (source code, docs)
- GitLab: Gitlab's internal dbt project is open source and is a great example of how to use dbt at scale (source code, docs)