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Prerequisites

Unity Catalog Access

You need permissions to create and manage Unity Catalog resources:

  • CREATE_CATALOG: Permission to create catalogs (or use existing catalog)
  • CREATE_SCHEMA: Permission to create schemas within catalogs
  • CREATE_FUNCTION: Permission to register UDTFs
  • CREATE_TABLE: Permission to create Views

Secret Manager Access

You need permissions to create secret scopes and store secrets:

  • CREATE_SECRET_SCOPE: Permission to create secret scopes
  • WRITE_SECRET: Permission to store secrets in scopes

WorkspaceClient

Access to Databricks Workspace API is required. The WorkspaceClient automatically detects credentials in Databricks notebooks:

from databricks.sdk import WorkspaceClient

workspace_client = WorkspaceClient()  # Auto-detects credentials

CDF Credentials

Access to CDF credentials is required:

  • client_id: OAuth2 client ID
  • client_secret: OAuth2 client secret
  • tenant_id: Azure AD tenant ID
  • cdf_cluster: CDF cluster name (e.g., "westeurope-1")
  • project: CDF project name

These credentials are typically stored in a TOML file and then transferred to Databricks Secret Manager.

Example CDF config TOML (no secrets)

Use the following structure. Replace placeholders with your real values and do not commit the file with secrets. In Databricks, store it under a path like /Workspace/Users/<your-user>/config.toml.

# Example CDF configuration for cognite-databricks.
# Copy, replace placeholders, and do not commit secrets.

[cognite]
project = "your-cdf-project"
tenant_id = "your-azure-ad-tenant-id"
cdf_cluster = "westeurope-1"
client_id = "your-oauth2-client-id"
client_secret = "your-oauth2-client-secret"

An example file with this content is in the repo at docs/catalog_based/example_config.toml. Copy it and fill in your values.

CDF Data Model

A CDF Data Model with Views (for Data Model UDTFs) or Time Series (for Time Series UDTFs) is required.

Next Steps

Once prerequisites are met, proceed to Secret Manager Setup to configure secure credential storage.