The guest of our show presented the Data Build Tool, DBT, in one of our knowledge-sharing sessions. Especially the fun part is what I recognize when I look at this knowledge-sharing session. The guest of the show presented it in an enthusiastic way and used terms like: the macro functionality makes DBT awesome and Ephemeral is the Unique Selling Point.
What the episode covers
- What is a Data Build Tool?
- What problems does it solve?
- What are the two versions? Cloud, CLI
- Why is it much more interesting to use than other ETL, transformation tools?
- Analytics Engineering
- Explain a bit more in debt the Basics of a DBT project: dbt project data pipeline, data model, tests, macros.
- Model dependencies
- DBT-packages ecosystem
- One of the powers of DBT is Jinja.
- What could you compare Jinja to?
- Is it easy to comprehend and learn?
- Ephemeral (CTE) was the unique selling point for you, why?
- An engineer enthusiastic about a documentation feature… that asks for an explanation.
- Usage started as an experiment, how are we using it in bol.com?
- Can you share some learnings you and your team had when you started using DBT?
Sander Boumeester – Software Engineer in the experimentation team.
On the website of getdbt there's a cool picture that explains the framework really well.
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