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Using dbt Cloud to build for scale

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· 5 min read
Joel Labes
The Bottom Line:

You should split your Jobs across Environments in dbt Cloud based on their purposes (e.g. Production and Staging/CI) and set one environment as Production. This will improve your CI experience and enable you to use dbt Explorer.

Environmental segmentation has always been an important part of the analytics engineering workflow:

  • When developing new models you can process a smaller subset of your data by using or an environment variable.
  • By building your production-grade models into a different schema and database, you can experiment in peace without being worried that your changes will accidentally impact downstream users.
  • Using dedicated credentials for production runs, instead of an analytics engineer's individual dev credentials, ensures that things don't break when that long-tenured employee finally hangs up their IDE.

Historically, dbt Cloud required a separate environment for Development, but was otherwise unopinionated in how you configured your account. This mostly just worked – as long as you didn't have anything more complex than a CI job mixed in with a couple of production jobs – because important constructs like deferral in CI and documentation were only ever tied to a single job.

But as companies' dbt deployments have grown more complex, it doesn't make sense to assume that a single job is enough anymore. We need to exchange a job-oriented strategy for a more mature and scalable environment-centric view of the world. To support this, a recent change in dbt Cloud enables project administrators to mark one of their environments as the Production environment, just as has long been possible for the Development environment.

Explicitly separating your Production workloads lets dbt Cloud be smarter with the metadata it creates, and is particularly important for two new features: dbt Explorer and the revised CI workflows.