dbt Core™ transformations
Build the models your business runs on.
Turn warehouse data into reporting tables and customer audiences with dbt Core™. Write SQL, test your models, and run them alongside ingestion and activation in Supaflow.
Watch the dbt Core demoUse your existing dbt project
Import an existing Git repository or start a new dbt Core project.
Check the logic and the results
Preview model output, inspect compiled SQL, and review test results.
See how the models connect
Use generated lineage, documentation, and catalog views to understand dependencies.
Model previews
Make business rules easy to explain.
See the records behind a result while you refine your SQL. For a customer audience, that means understanding who qualifies for an offer and why others are excluded.
- Edit model files in the project workspace
- Preview model output and inspect compiled SQL
- Review saved command logs and test results
In practice
Build a customer expansion audience.
Bring sales history, invoice balances, and email engagement into one set of eligibility rules. Give marketing an audience with a clear reason behind each customer’s inclusion.
- Combine customer activity and billing history in reusable models.
- Test the rules that determine who belongs in the audience.
- Pass the modeled audience to an activation pipeline when it is ready.
Screenshots use synthetic customer data. Delivery to a business application is a separate activation step.
Questions before you start
Can I use my existing dbt project and Git workflow?
Import a dbt Core project from Git, including projects in a repository subdirectory. Work on a personal branch, review diffs, and commit, pull, and push from Supaflow. After pushing, open your Git provider to create a pull request. The default branch stays read-only in Supaflow.
Can I preview a model before building it?
Yes. Edit and save a SQL model, then preview its output in the project workspace. You can also inspect compiled SQL and upstream and downstream dependencies while checking the logic.
Do I have to run the whole project after every change?
No. Run, test, or build a selected model, or use dbt selectors and exclusions to choose a larger set. Command options include variables, thread counts, and full refreshes where applicable.
Can I test my data and check source freshness?
Run dbt data and unit tests, or check freshness rules defined for your sources. Review job logs and results for individual models, tests, seeds, and snapshots, including status and execution time.
Where can I see model dependencies and documentation?
The project includes lineage, model and column descriptions, and a catalog of models, sources, columns, and types. These views use generated dbt metadata; run dbt docs generate when you need to refresh the documentation and catalog.
Which warehouses can I use?
Transformation projects support Snowflake, PostgreSQL, Amazon Redshift, Google BigQuery, Databricks, Microsoft SQL Server, and Azure SQL.
Can transformations run between ingestion and activation?
Yes. Add the transformation project to an orchestration. Set it to wait for the ingestion steps it needs, then place activation after the models that prepare its data. The same workflow can include SQL and Snowflake Python tasks.
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