Overview
The Datagraph turns independent Datasources into a single relational data model. It has two objects:
A published Entity is consumable by the Query an entity Data Node in a Workflow, so the same logic is written once and reused across Workflows.
Before You Begin
- Every table in the Datagraph Schema points to an existing Datasource, so create and publish your Datasources first. See Creating a Datasource.
- Column types come from the Datasource field mapping — the Datagraph does not re-type your data.
- A Datasource must have a Live version before it can be queried through an Entity.
- Datasources that use row-level filters cannot be queried through an Entity.
Model Structure
A Datagraph Schema is a list of tables. Each table carries the following attributes.
Table names always match the selected Datasource, which keeps the model and the DataHub aligned. Relations behave like foreign keys: a list of local columns pointing at the same number of columns in a referenced table.
Versions and Publishing
Both objects follow the same lifecycle.
Publishing promotes the Draft to Live, marks the previous Live version Inactive, then creates a fresh Draft copy of the new Live definition. You therefore always have a Draft to work in, and publishing never leaves you without an editable version.
Entity output columns are recomputed against the Live Datagraph Schema. If you add a column in the Schema Draft, publish the Schema before relying on that column in an Entity.
Permissions
Access is controlled by two separate permissions, both scoped to Company and Team: Datagraph Schema and Datagraph Entity. Each supports access, create, update, and delete actions. Configuring the Query an entity Data Node in a Workflow is restricted to tech admins. See Permissions.What’s Next?
Schema
Define tables, columns, and relations.
Explorer
Validate the model and inspect join matches.
Entities
Write parameterised SQL and publish it for Workflows.
Datagraph Schema API
Manage the Datagraph Schema programmatically.