Data ecosystem

dbt Cloud is a hosted service that many organizations use for their dbt deployments. Among other things, it provides an interface for creating and managing deployment jobs. When triggered (e.g., cron schedule, API trigger), the jobs generate various artifacts that contain valuable metadata related to the dbt project and the run results.

dbt Cloud provides a REST API for managing jobs, run artifacts and other dbt Cloud resources. Data/analytics engineers would often write custom scripts for issuing automated calls to the API using tools cURL or Python Requests. In some cases, the engineers would go on and copy/rewrite them between projects that need to interact with the API. Now, they have a bunch of scripts on their hands that they need to maintain and develop further if business requirements change. If only there was a dedicated tool for interacting with the dbt Cloud API that abstracts away the complexities of the API calls behind an easy-to-use interface… Oh wait, there is: the dbt-cloud-cli!

Special Thanks: Emilie Schario, Matt Winkler

dbt has done a great job of building an elegant, common interface between data engineers, analytics engineers, and any data-y role, by uniting our work on SQL. This unification of tools and workflows creates interoperability between what would normally be distinct teams within the data organization.

I like to call this interoperability a “baton pass.” Like in a relay race, there are clear handoff points & explicit ownership at all stages of the process. But there’s one baton pass that’s still relatively painful and undefined: the handoff between machine learning (ML) engineers and analytics engineers.

In my experience, the initial collaboration workflow between ML engineering & analytics engineering starts off strong but eventually becomes muddy during the maintenance phase. This eventually leads to projects becoming unusable and forgotten.

In this article, we’ll explore a real-life baton pass between ML engineering and analytics engineering and highlighting where things went wrong.

Having a GitHub pull request template is one of the most important and frequently overlooked aspects of creating an efficient and scalable dbt-centric analytics workflow. Opening a pull request is the final step of your modeling process - a process which typically involves a lot of complex work!

For you, the dbt developer, the pull request (PR for short) serves as a final checkpoint in your modeling process, ensuring that no key elements are missing from your code or project.

Airflow and dbt are often framed as either / or:

You either build SQL transformations using Airflow’s SQL database operators (like SnowflakeOperator), or develop them in a dbt project.

You either orchestrate dbt models in Airflow, or you deploy them using dbt Cloud.

In my experience, these are false dichotomies, that sound great as hot takes but don’t really help us do our jobs as data people.