Unlock the power of dbt Explorer in this hands-on workshop designed for analytics engineers, data analysts, stakeholders, and data leaders.
This quickstart guide accompanies the Explorer hands-on workshop and helps you dive into a production-level dbt Mesh implementation and discover how to explore your data workflows. Whether you're looking to streamline your data operations, improve data quality, or self-serve information about your data platform, this workshop will equip you with the tools and knowledge to take your dbt projects to the next level.
By the end of the guide and workshop, you'll understand how to leverage dbt Explorer and have the confidence to navigate multiple dbt projects, trace dependencies, and identify opportunities to improve performance and data quality.
Select the Exploring a dbt Mesh implementation with dbt Explorer option.
Use the passcode provided by the workshop facilitator.
Agree to the terms of service and click the Complete Registration button.
Wait about 30 seconds, you’ll be in the dbt Cloud account for this workshop and already connected to a data warehouse.
Toggle into the Platform project. Go to the Deploy tab and select Jobs from the dropdown menu.
Run each job you see by clicking on the job and then selecting Run. This will run the upstream project job in both a production and staging environment.
Toggle into the Analytics project. Go to the Deploy tab and select Jobs from the dropdown menu.
Run each job you see by clicking on the job and then selecting Run. This will run the downstream project job in both a production and staging environment.
dbt Explorer will show you your project's most executed models, longest model executions, most failed models and tests, and most consumed models all in one place: The performance tab.
With dbt Explorer, you can view your project's resources (such as models, tests, and metrics), their lineage, and model consumption to gain a better understanding of its latest production state.
Navigate and manage your projects within dbt Cloud to help you and other data developers, analysts, and consumers discover and leverage your dbt resources.
dbt Explorer provides a visualization of your project’s DAGA DAG is a Directed Acyclic Graph, a type of graph whose nodes are directionally related to each other and don’t form a directional closed loop. that you can interact with. The nodes in the lineage graph represent the project’s resources and the edges represent the relationships between the nodes. Nodes are color-coded and include iconography according to their resource type.
Use the search bar and node selectors to filter your DAG.
Lenses make it easier to understand your project’s contextual metadata at scales, especially to distinguish a particular model or a subset of models.
Applying a lens adds tags to the nodes, showing metadata like layer values, with color coding to help you distinguish them.
dbt Explorer's lineage graph
Use the advanced search feature to locate resources in your project.
Perform hard searches and keyword searches.
All resource names, column names, resource descriptions, warehouse relations, and code matching your search criteria will appear in the center of the page.
Apply filters to fully refine your search.
When searching for a column name, the results show all relational nodes containing that column in their schemas.
Congratulations! You've completed the dbt Explorer workshop. You now have the tools and knowledge to navigate multiple dbt projects, trace dependencies, and identify opportunities to improve performance and data quality.
You've learned how to:
Use dbt Explorer to visualize your project’s lineage and interact with the DAG
Search for resources in your project and apply filters to refine your search
Explore lenses and find table materializations in your current project
Navigate multiple dbt projects using dbt Explorer
Trace dependencies at the model and column level
Review project recommendations and implement improvements
For the next steps, you can check out the dbt Explorer documentation and FAQs to learn more about how to use dbt Explorer.
Keep an eye out for new features coming out soon, like:
More auto-exposure integrations (like PowerBI and Tableau).
Model query history for additional warehouses (like Redshift and Databricks)
