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Build, Test, Docs for dbt Models

Supademo

In this example we pull context from your dbt best practices stored in Knowledge Hub, pull requirements from a JIRA ticket, and update dbt models all within Cursor.

Walkthrough

This example demonstrates how our DataMates can be used to build, test, and deploy dbt (data build tool) models, streamlining analytics engineering workflows.

  • DataMates retrieve ticket details from Jira.
  • They understand the dbt project structure and existing models/schema.
  • An initial comment is added to the Jira ticket to signify the start of work.
  • The process includes adding documentation and tests to new dbt models, referencing organizational best practices.
  • The system helps in building the new model, documenting it, and testing the changes.

Knowledge Hub: Our Knowledge Hub centralizes an organization's best practices and tribal knowledge, providing verified context for IDEs. This allows AI workflows to move beyond generic code generation. The dbt Best Practices document from the Knowledge Hub is used as a reference for agents.

Integration Tools: Our DataMates integrate with dbt, Jira, and Cursor to execute the correct tools needed to refactor the dbt model. This example illustrates a process where DataMates automate the creation of a new dbt mart layer based on requirements from a Jira ticket (e.g., Jira ticket AI-2984).