jaffle_shop is a fictional ecommerce store. This dbt project transforms raw data from an app database into a customers and orders model ready for analytics.

The raw data from the app consists of customers, orders, and payments, with the following entity-relationship diagram:

This dbt project has a split personality:

• Tutorial: The master branch is a useful minimum viable dbt project to get new dbt users up and running with their first dbt project. It includes seed files with generated data so a user can run this project on their own warehouse.
• Demo: The demo branch is used to illustrate how we (Fishtown Analytics) would structure a dbt project. The project assumes that your raw data is already in your warehouse, so therefore the repo cannot be run as a standalone project. The demo is more complex than the tutorial as it is structured in a way that can be extended for larger projects.

To get up and running with this project:

1. Install dbt using these instructions.

2. Clone this repository. If you need extra help, see these instructions.

3. Change into the jaffle_shop directory from the command line:

$ cd jaffle_shop

4. Set up a profile called jaffle_shop to connect to a data warehouse by following these instructions. If you have access to a data warehouse, you can use those credentials – we recommend setting your target schema to be a new schema (dbt will create the schema for you, as long as you have the right priviliges). If you don't have access to an existing data warehouse, you can also setup a local postgres database and connect to it in your profile.

5. Ensure your profile is setup correctly from the command line:

$ dbt debug

6. Load the CSVs with the demo data set. This materializes the CSVs as tables in your target schema. Note that a typical dbt project does not require this step since dbt assumes your raw data is already in your warehouse.

$ dbt seed

7. Run the models:

$ dbt run

NOTE: If this steps fails, it might be that you need to make small changes to the SQL in the models folder to adjust for the flavor of SQL of your target database. Definitely consider this if you are using a community-contributed adapter.

8. Test the output of the models:

$ dbt test

9. Generate documentation for the project:

$ dbt docs generate

10. View the documentation for the project:

$ dbt docs serve

A jaffle is a toasted sandwich with crimped, sealed edges. Invented in Bondi in 1949, the humble jaffle is an Australian classic. The sealed edges allow jaffle-eaters to enjoy liquid fillings inside the sandwich, which reach temperatures close to the core of the earth during cooking. Often consumed at home after a night out, the most classic filling is tinned spaghetti, while my personal favourite is leftover beef stew with melted cheese.

For more information on dbt:

• Read the introduction to dbt.
• Read the dbt viewpoint.
• Join the chat on Slack for live questions and support.