In this part of the guide, you will learn how to schedule a job to be run in your production environment. Scheduling a job is sometimes called deploying a project.
jaffle_shop business gains more customers, and those customers create more orders, you will see more records added to your source data. Because you materialized the
customers model as a table, you'll need to periodically rebuild your table to ensure that the data stays up-to-date. This update will happen when you run a job.
Commit your changes
Now that you've built your customer model, you need to commit the changes you made to the project so that the repository has your latest code.
- Click Commit and add a message. For example, "Add customers model, tests, docs."
- Click merge to main To add these changes to the main branch on your repo.
Create a deployment environment
- In the upper left, select Deploy, then click Environments.
- Click Create Environment.
- Name your deployment environment. For example, "Production."
- Add a target dataset, for example, "Analytics." dbt will build into this dataset. For some warehouses this will be named "schema."
- Click Save.
Create and run a job
Jobs are a set of dbt commands that you want to run on a schedule. For example,
dbt run and
- After creating your deployment environment, you should be directed to the page for new environment. If not, select Deploy in the upper left, then click Jobs.
- Click Create one and provide a name, for example "Production run", and link to the Environment you just created.
- Scroll down to "Execution Settings" and select Generate docs on run.
- Under "Commands," add these commands as part of your job if you don't see them:
- For this exercise, do NOT set a schedule for your project to run -- while your organization's project should run regularly, there's no need to run this project on a schedule.
- Select Save, then click Run now to run your job.
- Click the run and watch its progress under "Run history."
- Once the run is complete, click View Documentation to see the docs for your project.
Congratulations 🎉! You've just deployed your first dbt project!
Congratulations! Now that you've got a working dbt project, you can read about dbt best practices.
You can improve your dbt skills with these fun exercises:
- Turn your raw data references (for example, turn
`dbt-tutorial`.jaffle_shop.orders) into sources.
- Build a new models for
orders, that uses the
paymentstable to calculate the total order amount.
- Reorganize your project into how we structure dbt projects.
- If you want a more in-depth learning experience, we recommend taking the dbt Fundamentals on our dbt Learn online courses site.
Here are some ways to learn more essential dbt skills: