This is a sandbox project for exploring the basic functionality and latest features of dbt. It's based on a fictional restaurant called the Jaffle Shop that serves jaffles.

This README will guide you through setting up the project on dbt Cloud. Working through this example should give you a good sense of how dbt Cloud works and what's involved with setting up your own project. We'll also optionally cover some intermediate topics like setting up Environments and Jobs in dbt Cloud, working with a larger dataset, and setting up pre-commit hooks if you'd like.

Ready to go? Grab some water and a nice snack, and let's dig in!

Welcome to the Jaffle Shop - Watch Intro Video

1. Prerequisites
2. Create new repo from template
3. Platform setup
   1. dbt Cloud IDE
   2. dbt Cloud CLI
   3. Load the data
4. Project setup
5. Going further
   1. Setting up dbt Cloud Environments and Jobs
      1. Creating an Environment
      2. Creating a Job
      3. Explore your DAG
   2. Working with a larger dataset
      1. Load the data from S3
      2. Generate via jafgen and seed the data with dbt Core
   3. Pre-commit and SQLFluff

• A dbt Cloud account
• A data warehouse (BigQuery, Snowflake, Redshift, Databricks, or Postgres) with adequate permissions to create a fresh database for this project and run dbt in it
• Optional Python 3.9 or higher (for generating synthetic data with jafgen)

1. Click the green "Use this template" button at the top of the page to create a new repository from this template.

   

2. Follow the steps to create a new repository. You can choose to only copy the main branch for simplicity, or take advantage of the Write-Audit-Publish (WAP) flow we use to maintain the project and copy all branches (which will include main and staging along with any active feature branches). Either option is fine!

1. Create a logical database in your data warehouse for the Jaffle Shop project. We recommend using the name jaffle_shop for consistency with the project. This looks different on different platforms (for instance on BigQuery this constitutes creating a new project, on Snowflake this is achieved via create database jaffle_shop;, and if you're running Postgres locally you can probably skip this). If you're not sure how to do this, we recommend checking out the Quickstart Guide for your data platform in the dbt Docs.

2. Set up a dbt Cloud account (if you don't have one already, if you do, just create a new project) and follow Step 4 in the Quickstart Guide for your data platform, to connect your platform to dbt Cloud. Make sure the user you configure for your connections has adequate database permissions to run dbt in the jaffle_shop database.

3. Choose the repo you created in Step 1 of the Create new repo from template section as the repository for your dbt Project's codebase.

The following should now be done:

• dbt Cloud connected to your warehouse
• Your copy of this repo set up as the codebase
• dbt Cloud and the codebase pointed at a fresh database or project in your warehouse to work in

You're now ready to start developing with dbt Cloud! Choose a path below (either the dbt Cloud IDE or the Cloud CLI to get started.

1. Click Develop in the dbt Cloud nav bar. You should be prompted to run a dbt deps, which you should do. This will install the dbt packages configured in the packages.yml file.

1. Run git clone [new repo git link] (or gh repo clone [repo owner]/[new repo name] if you prefer GitHub's excellent CLI) to clone your new repo from the first step of the Create new repo from template section to your local machine.

2. Follow the steps on this page to install and set up a dbt Cloud connection with the dbt Cloud CLI.

There are a few ways to load the data for the project:

• Using the sample data in the repo. Add "jaffle-data" to the seed-paths config in your dbt_project.yml as below. This means that when dbt is scanning folders for seeds to load it will look in both the seeds folder as is default, but also the jaffle-data folder which contains a sample of the project data. Seeds are static data files in CSV format that dbt will upload, usually for reference models, like US zip codes mapped to country regions for example, but in this case the feature is hacked to do some data ingestion. This is not what seeds are meant to be used for (dbt is not a data loading tool), but it's useful for this project to give you some data to get going with quickly. Run a dbt seed and when it's done either delete the jaffle-data folder, remove jaffle-data from the seed-paths list, or ideally, both.

seed-paths: ["seeds", "jaffle-data"]

dbt seed

• Load the data via S3. If you'd prefer a larger dataset (6 years instead of 1), and are working via the dbt Cloud IDE and your platform's web interface, you can also copy the data from a public S3 bucket to your warehouse into a schema called raw in your jaffle_shop database. This is discussed here.

• Generate a larger dataset on the command line. If you're working with the dbt Cloud CLI and comfortable with command line basics, you can generate as many years of data as you'd like (up to 10) to load into your warehouse. This is discussed here.

Once your development platform of choice and dependencies are set up, use the following steps to get the project ready for whatever you'd like to do with it.

1. Ensure that you've deleted the jaffle-data folder or removed it from the seed-paths list in your dbt_project.yml (or, ideally, both) if you used the seed method to load the data. This is important, if you don't do this, dbt build will re-run the seeds unnecessarily and things will get messy.

2. Run a dbt build to build the project.

The following should now be done:

• Synthetic data loaded into your warehouse
• Development environment set up and ready to go
• The project built and tested

You're free to explore the Jaffle Shop from here, or if you want to learn more about setting up Environment and Jobs, generating a larger dataset, or setting up pre-commit hooks to standardize formatting and linting workflows, carry on!

dbt Cloud has a powerful abstraction called an Environment. An Environment in dbt Cloud is a set of configurations that dbt uses when it runs your code. It includes things like what version of dbt to use, what schema to build into, credentials to use, and more. You can set up multiple environments in dbt Cloud, and each environment can have its own set of configurations. This is very useful for running Jobs. A Job is a set of dbt commands which run in an Environment. Understanding these two concepts is key for getting those most out of dbt Cloud, especially building a robust deployment workflow. Now that we're able to develop in our project, this section will walk you through setting up an Environment and a Job to deploy our project to production.

1. Go to the Deploy tab in the dbt Cloud nav bar and click Environments.

2. On the Environment page, click + Create Environment.

   

3. Name your Environment Prod and set it as a Production Environment.

   

4. Fill out the credentials with your warehouse connection details, in real production you'll want to make a Service Account or similar and only give access to the production schema to that user, so that only dbt Cloud Jobs can build into production. For this demo project, it's okay to just use your account credentials.

5. Set the branch that this Environment runs on to main, then the schema that this Environment builds into to prod. This ensures that Jobs configured in this Environment always build into the prod schema and run on the main branch which we've protected as our production branch.

   

6. Click Save.

Now we'll create a Job to deploy our project to production. This Job will run the dbt build command in the prod Environment we just created.

1. Go to the Prod Environment you just created.

2. Click + Create Job and choose Deploy Job as the Job type.

   

3. Name your Job Production Build.

4. You can otherwise leave the defaults in place and just click Save.

5. Click into your newly created Job and click Run Now in the top right corner.

   

6. This will kick off a Job to build your project in the Prod Environment, which will build into the prod schema in your warehouse.

7. Go check out the prod schema in your jaffle_shop database on your warehouse, you should see the project's models built there!

From here, you should be able to use dbt Explorer (in the Explore tab of the dbt Cloud nav bar) to explore your DAG! Explorer is populated with metadata from your designated Production and Staging Environments, so you can see the lineage of your project visually, and much more.

There are two ways to work with a larger dataset than the default one year of data that comes with the project:

1. Load the data from S3 which will let you access the canonical 6 year dataset the project is tested against.

2. Generate via jafgen and seed the data with dbt Core which will allow you to generate up to 10 years of data.

To load the data from S3, consult the dbt Documentation's Quickstart Guides for your data platform to see how to copy data from an S3 bucket to your warehouse. The S3 bucket URIs of the tables you want to copy into your raw schema are:

• raw_customers: s3:https://jaffle-shop-raw/raw_customers.csv
• raw_orders: s3:https://jaffle-shop-raw/raw_orders.csv
• raw_order_items: s3:https://jaffle-shop-raw/raw_order_items.csv
• raw_products: s3:https://jaffle-shop-raw/raw_products.csv
• raw_supplies: s3:https://jaffle-shop-raw/raw_supplies.csv
• raw_stores: s3:https://jaffle-shop-raw/raw_stores.csv

You'll need to be working on the command line for this option. If you're more comfortable working via web apps, the above method is the path you'll need. jafgen is a simple tool for generating synthetic Jaffle Shop data that is maintained on a volunteer-basis by dbt Labs employees. This project is more interesting with a larger dataset generated and uploaded to your warehouse. 6 years is a nice amount to fully observe trends like growth, seasonality, and buyer personas that exist in the data. Uploading this amount of data requires a few extra steps, but we'll walk you through them. If you have a preferred way of loading CSVs into your warehouse or an S3 bucket, that will also work just fine, the generated data is just CSV files.

1. Create a profiles.yml file in the root of your project. This file is already .gitignored so you can keep your credentials safe. If you'd prefer you can instead set up a profiles.yml file at the ~/.dbt/profiles.yml path instead to be extra sure you don't accidentally commit the file.

2. Add a profile for your warehouse connection in this file and add this configuration to your dbt_project.yml file as a top-level key called profile e.g. profile: my-profile-name.

3. Create a new virtual environment in your project (I like to call mine .venv) and activate it, then install the project's dependencies in it. This will install the jafgen tool which you can use to generate the larger datasets. Then install dbt-core and your warehouse's adapter. We install dbt Core temporarily because by connecting directly to your warehouse, it can upload larger file sizes than the dbt Cloud server¹. You can do this manually or with task:

python3 -m venv .venv
source .venv/bin/activate
python3 -m pip install -r requirements.txt
python3 -m pip install dbt-core dbt-[your warehouse adapter] # e.g. dbt-bigquery

OR

task venv
task install DB=[name of warehouse] # e.g. task install DB=bigquery

5. Add jaffle-data to your seed-paths config in your dbt-project.yml as detailed here, then run jafgen and seed the data it generates.

jafgen [number of years to generate] # e.g. jafgen 6
dbt seed

OR

task gen YEARS=[integer of years to generate] # e.g. task gen YEARS=6
task seed

6. Remove the jaffle-data folder, then uninstall the temporary dbt Core installation. Again, this was to allow you to seed the large data files, you don't need it for the rest of the project which will use the dbt Cloud CLI. You can then delete your profiles.yml file and the configuration in your dbt_project.yml file. You should also delete the jaffle-data path from the seed-paths list in your dbt_project.yml.

rm -rf jaffle-data
python3 -m pip uninstall dbt-core dbt-[your warehouse adapter] # e.g. dbt-bigquery

OR

task clean

You now have a much more interesting and expansive dataset in your raw schema to build with! You should now run a dbt build to build the project with the new data into your dev schema or trigger your Production Build Job in dbt Cloud to build the project in your prod schema.

There's an optional tool included with the project called pre-commit.

pre-commit automatically runs a suite of of processes on your code, like linters and formatters, when you commit. If it finds an issue and updates a file, you'll need to stage the changes and commit them again (the first commit will not have gone through because pre-commit found and fixed an issue). The outcome of this is that your code will be more consistent automatically, and everybody's changes will be running through the same set of processes. We recommend it for any project.

You can see the configuration for pre-commit in the .pre-commit-config.yaml file. It's installed as part of the project's requirements.txt, but you'll need to opt-in to using it by running pre-commit install. This will install git hooks which run when you commit. You can also run the checks manually with pre-commit run --all-files to see what it does without making a commit.

At present the following checks are run:

• ruff - an incredibly fast linter and formatter for Python, in case you add any Python models
• check-yaml - which validates YAML files
• end-of-file-fixer - which ensures all files end with a newline
• trailing-whitespace - which trims trailing whitespace from files

At present, the popular SQL linter and formatter SQLFluff doesn't play nicely with the dbt Cloud CLI, so we've omitted it from this project for now. We've already built the backend for linting via the Cloud CLI, so this will change very soon! At present if you'd like auto-formatting and linting for SQL, check out the dbt Cloud IDE!

We have kept a .sqlfluff config file to show what that looks like, and to future proof the repo for when the Cloud CLI support linting and formatting.