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Analytics Corner - 3. Basic Modeling

drexel-uhc.github.io · 1,377 words · saved by 1 readers

We previously learned how to load data into DBT. Now we can start orchestrating transformations using DBT. The goal to today is to introduce best practices for project structure and start with basic modeling. This section is a curated copy and paste of DBT’s analytics engineering documentation! Before dbt was released, the most reliable way that to model data was SQL scripting. This often looked like writing one 10,000 line SQL file, or if you want to get fancy, you could split that file into a bunch of separate SQL files or stored procedures that are run in order with a Python script. Very few people in the org would be aware of my scripts, so that even if someone else was looking to model data in a similar way, they'd start from source data rather than leveraging what I'd already built. Not that I didn't want to share! There just wasn't an easy way to do so. We could call this a monolithic or traditional approach to data modeling: each consumer of data would rebuild their own data tr

Analytics Corner - 3. Basic Modeling We previously learned how to load data into DBT. Now we can start orchestrating transformations using DBT. The goal to today is to introduce best practices for project structure and start with basic modeling. The specific learning goals for today’s session are: Introduce DBT structure Create base models Interact with DBT with VS code 3.1 Intro to DBT Project structure This section is a curated copy and paste of DBT’s analytics engineering documentation ! 3.1.1 Traditional, monolithic data modeling techniques Before dbt was released, the most reliable way th

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