Deploy dbt jobs
Running dbt in production means setting up a system to run a dbt job on a schedule, rather than running dbt commands manually from the command line. Your production dbt jobs should create the tables and views that your business intelligence tools and end users query. Before continuing, make sure you understand dbt's approach to managing environments.
In addition to setting up a schedule, there are other considerations when setting up dbt to run in production:
- The complexity involved in creating a new dbt job or editing an existing one.
- Setting up notifications if a step within your job returns an error code (for example, a model can't be built or a test fails).
- Accessing logs to help debug any issues.
- Pulling the latest version of your git repo before running dbt (continuous deployment).
- Running and testing your dbt project before merging code into master (continuous integration).
- Allowing access for team members that need to collaborate on your dbt project.
Deploy with dbt Cloud
Use dbt Cloud's in-app scheduling to run your production jobs. Schedule jobs by day of the week, times or recurring intervals.