Paradime enhances dbt™️ Mesh with cross-project dependencies, model groups, access control, graph lineage, and data discovery. Trigger dbt™️ runs in production and track column changes in your PRs.
Paradime supports dbt™️ Mesh—enabling cross-project dependencies in your data mesh implementation. With Paradime, you can seamlessly integrate multiple dbt™️ projects, ensuring that various data teams can collaborate efficiently and maintain a cohesive data ecosystem.
This enhanced capability allows for streamlined data workflows, improved data quality, and robust data lineage tracking across projects.
Cross-project references - this is the foundational feature that enables the multi-project deployments. {{ ref() }}
Model Groups - With groups, you can organize nodes in your dbt™️ DAG that share a logical connection (for example, by functional area) and assign an owner to the entire group.
Model Access - access configs allow you to control who can reference models.
Paradime offers additional features to simplify the implementation of dbt™️ Mesh workflows, enabling data teams to work both independently and collaboratively.
Providing a fully enabled cross-project lineage allows for a comprehensive exploration of lineage dependencies across various dbt™️ projects.
This feature empowers users to gain deeper insights, track data transformation processes, and identify interconnections among multiple projects, enhancing overall data transparency and workflow efficiency.
Enabling comprehensive search and streamlined discovery of dbt™️ public models originating from the linked "producer" project.
This feature allows users to efficiently locate and utilize shared dbt models, enhancing collaboration and the overall data workflow within the connected project environment.
Allowing dbt™️ runs to be triggered based on the activities of a connected "producer" dbt™️ project streamlines the workflow and enhances integration.
This capability ensures that changes or updates in the producer project can automatically initiate corresponding runs in consumer projects, promoting continuity and reducing the need for manual intervention.
The Column Lineage Diff Analysis in Paradime lets users track changes in a Pull Request and see their impact on columns in the consumer project. This ensures users have visibility into dependencies and modifications.
By leveraging this functionality, users can identify and address potential breaking changes early, leading to smoother integration and deployment. This enhances data integrity and reliability by ensuring compatibility between producer and consumer projects during changes.
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