Notes taken by Horeb S.

🔗 My Github repo

This week focuses on transforming raw data into structured analytical views using dbt (Data Build Tool). Key topics include data modeling (fact and dimension tables), the ELT workflow, and dbt fundamentals like materialization, testing, and deployment. By the end of this week, I’ll have built and deployed a functional dbt project, preparing data for real-world analysis and visualization. Here are my notes.

Prerequisites

The prerequisites of this course include :

• a running warehouse (GCP or Postgres): I will use BigQuery
• a set of running pipelines ingesting the project dataset : have a quick look to the 🔗 Week 02 Notes to see how to use Kestra
• some datasets ingested :
  • Yellow taxi data - Years 2019 and 2020
  • Green taxi data - Years 2019 and 2020
  • fhv data - Year 2019.

I didn’t have all of these datasets ingested into my BigQuery. So, let’s use what we learned during Kestra Course to do it.

<aside> 💡

I don’t write any notes about it. But you can see my old ones here : 🔗 Week 02 Notes

Additionally, the Kestra Workflow I used to upload fhv files is 🔗 here

There is also an hack to upload the files quickly : 🔗 Link to the video

</aside>

• here, we decide to use BigQuery as warehouse. So we need to 🔗 sign up to dbt and set it up for BigQuery (BQ). The instructions to do this are 🔗 here.

Content of the course

Introduction to analytics engineering

Getting started project