Databricks AI assets

Updated: June 17, 2026

The Databricks Unity Catalog integration for AI in Collibra comes out of the box with your software.

The functions of the Databricks Unity Catalog AI integration is limited if you do not enable AI Governance. When you enable AI Governance, you can add custom attributes or relations to the Databricks AI asset.

Tip

You can also integrate the metadata of Databricks databases, schemas, tables, database views, and columns using the Databricks Unity Catalog metadata integration. For more information, go to the Databricks Unity Catalog documentation.
If you're looking for information on how to create technical lineage for Databricks Unity Catalog via Edge, go to the technical lineage for Databricks Unity Catalog documentation.

Asset type	Description
AI Base Model	Represents foundational business concepts that define the model’s purpose and governing principles in your Databricks AI workspace.
Databricks AI Model Version	A subtype of AI Model Version that represents AI model versions in Databricks AI.
AI Model Deployment	The operational instance where a model version is assigned computational resources to execute and generate real-time outputs.
AI Agent	A system that uses one or more AI models to perceive its environment, make decisions, and take actions.
AI Agent Tool	A service accessible to an AI agent that automates tasks, generates content, or delivers intelligent insights.
AI Agent Version	A specific, immutable implementation of the model logic and parameters that serves as the audited "raw" model version before it is activated.
Databricks Volume (in preview)	Unity Catalog objects for governing non-tabular datasets.
AI Endpoint	The access point for external systems that provides a consistent interface while allowing for the seamless exchange of underlying deployments.
AI Monitor	The configuration of automated tracking parameters and alerting thresholds used to observe a deployment’s real-world performance and detect deviations from expected behavior.
File	Represents the dataset files used to fine-tune your AI base models Databricks AI workspace. This asset is optional, you can ingest this data during the integration by selecting the Ingest input datasets and deployment output for AI models and deployments checkbox.
Storage Container	Represents the folder that contains the dataset files used to fine-tune your AI base models Databricks AI workspace. This asset is optional, you can ingest this data during the integration by selecting the Ingest input datasets and deployment output for AI models and deployments checkbox.
Database	A collection of data that is systematically organized or structured to make it easy to create, update, and query the information. This asset is optional, you can ingest this data during the integration by selecting the Ingest input datasets and deployment output for AI models and deployments checkbox.
Schema	An asset that contains the location of specific data. It provides all the details that are required for setting up a connection to a database or server. This asset is optional, you can ingest this data during the integration by selecting the Ingest input datasets and deployment output for AI models and deployments checkbox.
Table	An implementation of data entities in columns and rows, in a given database system. It is the basic structure of a relational database. This asset is optional, you can ingest this data during the integration by selecting the Ingest input datasets and deployment output for AI models and deployments checkbox.
Column	An atomic unit of data that can be stored in a database table. This asset is optional, you can ingest this data during the integration by selecting the Ingest input datasets and deployment output for AI models and deployments checkbox.

Databricks Unity Catalog diagram view

The following image provides a comprehensive overview of the relations between Databricks Unity Catalog asset types and cardinality of the relation types in the assets' assignment.

For metadata ingestion data from Databricks Unity Catalog, go to Integrated Databricks Unity Catalog data.
For AI ingestion data from Databricks Unity Catalog, go to Synchronized Databricks AI Catalog data.

Steps

Open the asset page.
Click the Diagram tab.
The diagram is shown in the default diagram view.
Click to add a new view.
Select the Text option below the diagram view name.
The diagram view text editor is shown.
Copy the code from the Show JSON code section below and paste it in the diagram view text editor.
Click Save.
Edit the name and description of the diagram view as needed.

Show JSON code

Copy

{
  "nodes": [
    {
      "id": "System",
      "type": {
        "id": "00000000-0000-0000-0000-000000031302"
      }
    },
    {
      "id": "Database",
      "type": {
        "id": "00000000-0000-0000-0000-000000031006"
      }
    },
    {
      "id": "Schema",
      "type": {
        "id": "00000000-0000-0000-0001-000400000002"
      }
    },
    {
      "id": "Databricks Schema",
      "type": {
        "id": "00000000-0000-0000-0000-000000031413"
      }
    },
    {
      "id": "Table",
      "type": {
        "id": "00000000-0000-0000-0000-000000031007"
      }
    },
    {
      "id": "Database View",
      "type": {
        "id": "00000000-0000-0000-0001-000400000009"
      }
    },
    {
      "id": "Column",
      "type": {
        "id": "00000000-0000-0000-0000-000000031008"
      }
    },
    {
      "id": "Foreign Key",
      "type": {
        "id": "00000000-0000-0000-0001-000400000003"
      }
    },
    {
      "id": "Databricks Volume",
      "type": {
        "id": "00000000-0000-0000-0000-000000031412"
      }
    },
    {
      "id": "Directory",
      "type": {
        "id": "00000000-0000-0000-0000-000000031303"
      }
    },
    {
      "id": "File",
      "type": {
        "id": "00000000-0000-0000-0000-000000031304"
      }
    },
    {
      "id": "AI Base Model",
      "type": {
        "id": "00000000-0000-0000-0000-000000031422"
      }
    },
    {
      "id": "Databricks AI Model Version",
      "type": {
        "id": "00000000-0000-0000-0000-000000031405"
      }
    },
    {
      "id": "AI Model Deployment",
      "type": {
        "id": "00000000-0000-0000-0000-000000031500"
      }
    },
    {
      "id": "AI Endpoint",
      "type": {
        "id": "00000000-0000-0000-0000-000000031501"
      }
    },
    {
      "id": "AI Monitor",
      "type": {
        "id": "00000000-0000-0000-0000-000000031502"
      }
    },
    {
      "id": "AI Agent",
      "type": {
        "id": "00000000-0000-0000-0000-000000031450"
      }
    },
    {
      "id": "AI Agent Version",
      "type": {
        "id": "00000000-0000-0000-0000-000000031452"
      }
    },
    {
      "id": "AI Agent Tool",
      "type": {
        "id": "00000000-0000-0000-0000-000000031453"
      }
    }
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      "to": "Database",
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      "style": "arrow",
      "type": {
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      "from": "Database",
      "to": "Databricks Schema",
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      "from": "Schema",
      "to": "Table",
      "label": "",
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      },
      "roleDirection": true
    },
    {
      "from": "Schema",
      "to": "Database View",
      "label": "",
      "style": "arrow",
      "type": {
        "id": "00000000-0000-0000-0000-000000007043"
      },
      "roleDirection": true
    },
    {
      "from": "Databricks Schema",
      "to": "Table",
      "label": "",
      "style": "arrow",
      "type": {
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      },
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    },
    {
      "from": "Databricks Schema",
      "to": "Database View",
      "label": "",
      "style": "arrow",
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      "roleDirection": true
    },
    {
      "from": "Table",
      "to": "Column",
      "label": "",
      "style": "arrow",
      "type": {
        "id": "00000000-0000-0000-0000-000000007042"
      },
      "roleDirection": false
    },
    {
      "from": "Database View",
      "to": "Column",
      "label": "",
      "style": "arrow",
      "type": {
        "id": "00000000-0000-0000-0000-000000007042"
      },
      "roleDirection": false
    },
    {
      "from": "Column",
      "to": "Foreign Key",
      "label": "",
      "style": "arrow",
      "type": {
        "id": "00000000-0000-0000-0000-000000007093"
      },
      "roleDirection": true
    },
    {
      "from": "Foreign Key",
      "to": "Column",
      "label": "constrains",
      "style": "arrow",
      "type": {
        "id": "00000000-0000-0000-0000-000000007091"
      },
      "roleDirection": true
    },
    {
      "from": "Foreign Key",
      "to": "Column",
      "label": "references",
      "style": "arrow",
      "type": {
        "id": "00000000-0000-0000-0000-000000007092"
      },
      "roleDirection": true
    },
    {
      "from": "Databricks Schema",
      "to": "Databricks Volume",
      "label": "",
      "style": "arrow",
      "type": {
        "id": "00000000-0000-0000-0000-000000007044"
      },
      "roleDirection": true
    },
    {
      "from": "Databricks Volume",
      "to": "Directory",
      "label": "",
      "style": "arrow",
      "type": {
        "id": "00000000-0000-0000-0001-002600000001"
      },
      "roleDirection": true
    },
    {
      "from": "Directory",
      "to": "Directory",
      "label": "",
      "style": "arrow",
      "type": {
        "id": "00000000-0000-0000-0001-002600000003"
      },
      "roleDirection": true
    },
    {
      "from": "Directory",
      "to": "File",
      "label": "",
      "style": "arrow",
      "type": {
        "id": "00000000-0000-0000-0000-000000007060"
      },
      "roleDirection": true
    },
    {
      "from": "AI Base Model",
      "to": "Databricks AI Model Version",
      "label": "",
      "style": "arrow",
      "type": {
        "id": "00000000-0000-0000-0000-000000077000"
      },
      "roleDirection": true
    },
    {
      "from": "Databricks AI Model Version",
      "to": "AI Model Deployment",
      "label": "",
      "style": "arrow",
      "type": {
        "id": "00000000-0000-0000-0000-000000077001"
      },
      "roleDirection": true
    },
    {
      "from": "AI Model Deployment",
      "to": "AI Endpoint",
      "label": "",
      "style": "arrow",
      "type": {
        "id": "00000000-0000-0000-0000-000000077006"
      },
      "roleDirection": true
    },
    {
      "from": "AI Model Deployment",
      "to": "AI Monitor",
      "label": "",
      "style": "arrow",
      "type": {
        "id": "00000000-0000-0000-0000-000000077007"
      },
      "roleDirection": true
    },
    {
      "from": "Databricks AI Model Version",
      "to": "Table",
      "label": "trained by",
      "style": "arrow",
      "type": {
        "id": "00000000-0000-0000-0000-000000007102"
      },
      "roleDirection": true
    },
    {
      "from": "AI Model Deployment",
      "to": "Table",
      "label": "output",
      "style": "arrow",
      "type": {
        "id": "00000000-0000-0000-0000-000000077004"
      },
      "roleDirection": true
    },
    {
      "from": "AI Agent",
      "to": "Databricks AI Model Version",
      "label": "uses",
      "style": "arrow",
      "type": {
        "id": "00000000-0000-0000-0000-000000007108"
      },
      "roleDirection": true
    },
    {
      "from": "AI Agent",
      "to": "AI Agent Version",
      "label": "",
      "style": "arrow",
      "type": {
        "id": "00000000-0000-0000-0000-000000007206"
      },
      "roleDirection": true
    },
    {
      "from": "AI Agent Version",
      "to": "AI Agent Tool",
      "label": "",
      "style": "arrow",
      "type": {
        "id": "00000000-0000-0000-0000-000000007207"
      },
      "roleDirection": true
    },
    {
      "from": "AI Agent Tool",
      "to": "Databricks Volume",
      "label": "reads inference data",
      "style": "arrow",
      "type": {
        "id": "00000000-0000-0000-0000-000000007211"
      },
      "roleDirection": true
    },
    {
      "from": "AI Agent Version",
      "to": "AI Model Deployment",
      "label": "deployed by",
      "style": "arrow",
      "type": {
        "id": "00000000-0000-0000-0000-000000007213"
      },
      "roleDirection": true
    }
  ],
  "showOverview": false,
  "enableFilters": true,
  "showLabels": true,
  "showFields": true,
  "showLegend": true,
  "showPreview": true,
  "visitStrategy": "directed",
  "layout": "HierarchyTopBottom",
  "maxNodeLabelLength": 50,
  "maxEdgeLabelLength": 30,
  "layoutOptions": {
    "compactGroups": false,
    "componentArrangementPolicy": "topmost",
    "edgeBends": true,
    "edgeBundling": true,
    "edgeToEdgeDistance": 5,
    "minimumLayerDistance": "auto",
    "nodeToEdgeDistance": 5,
    "orthogonalRouting": true,
    "preciseNodeHeightCalculation": true,
    "recursiveGroupLayering": true,
    "separateLayers": true,
    "webWorkers": true,
    "nodePlacer": {
      "barycenterMode": true,
      "breakLongSegments": true,
      "groupCompactionStrategy": "none",
      "nodeCompaction": false,
      "straightenEdges": true
    }
  }
}