Week 4: Analytics Engineering

Goal: Transforming the data loaded in DWH to Analytical Views developing a dbt project. Slides

Prerequisites

We will build a project using dbt and a running data warehouse. By this stage of the course you should have already:

A running warehouse (BigQuery or postgres)
A set of running pipelines ingesting the project dataset (week 3 completed): Datasets list
- Yellow taxi data - Years 2019 and 2020
- Green taxi data - Years 2019 and 2020
- fhv data - Year 2019.

Note:

A quick hack has been shared to load that data quicker, check instructions in week3/extras
If you recieve an error stating "Permission denied while globbing file pattern." when attemting to run fact_trips.sql this video may be helpful in resolving the issue

🎥 Video

Setting up dbt for using BigQuery (Alternative A - preferred)

You will need to create a dbt cloud account using this link and connect to your warehouse following these instructions. More detailed instructions in dbt_cloud_setup.md

Optional: If you feel more comfortable developing locally you could use a local installation of dbt as well. You can follow the official dbt documentation or follow the dbt with BigQuery on Docker guide to setup dbt locally on docker. You will need to install the latest version (1.0) with the BigQuery adapter (dbt-bigquery).

Setting up dbt for using Postgres locally (Alternative B)

As an alternative to the cloud, that require to have a cloud database, you will be able to run the project installing dbt locally. You can follow the official dbt documentation or use a docker image from oficial dbt repo. You will need to install the latest version (1.0) with the postgres adapter (dbt-postgres). After local installation you will have to set up the connection to PG in the profiles.yml, you can find the templates here