Analytic Engineering & dbt: 0-100¶

Summary¶

This is a quick guide on how to get started and create your data models using dbt (data build tool). dbt is a Python-based packaged for managing Transformations in the ELT (Extract, Load, Transform) lifecyle.

You can find a quick slide deck on dbt from my past Lighting Talk on dbt, here. A video summary of this documentation can be found here, on Youtube. To see the resulting documentation portal, head to raulingaverage.dev/dbt-Docs-Tutorial-Portal/).

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Resources¶

Tutorial¶

Tutorial: onboarding-2-dbt
Queries: customer-analytics Github Repository
Youtube Overview: Analytics Engineering & dbt: 0-100 playlist

dbt Resources¶

Donate¶

If you would like to support this project, feel free to consider donating to one of the following:

PyLadies Donation
- Note: RLadies too, but I think they have enough funding, compared to PyLadies. Your choice, of course. :D
- Why? With the recent trend gatekeeping and impact on COVID-19 on women, I feel this is top priority.
Delta Analytics
- Why? Nonprofit I volunteer for
Dev/Mission
- Why? I really care about minorities, Blackx and Latinx groups going into tech.
NumFocus Donate
- Why? <3 Python community

Installation¶

The Tutorial¶

Go to this repository customer-analytics, and enter the following in your CLI (outside of this repo):

git clone https://github.com/CloudChaoszero/customer-analytics.git

Note: I used a Snowflake Trial to implement this tutorial.

Environment¶

conda create --name dbtcourse
python -m ipykernel install --user --name dbtcourse --display-name "Python (dbt Course)"

1. Installation