databricks-enterprise-rbac

Configure Databricks enterprise SSO, Unity Catalog RBAC, and organization management. Use when implementing SSO integration, configuring role-based permissions, or setting up organization-level controls with Unity Catalog. Trigger with phrases like "databricks SSO", "databricks RBAC", "databricks enterprise", "unity catalog permissions", "databricks SCIM".

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name	databricks-enterprise-rbac
description	Configure Databricks enterprise SSO, Unity Catalog RBAC, and organization management. Use when implementing SSO integration, configuring role-based permissions, or setting up organization-level controls with Unity Catalog. Trigger with phrases like "databricks SSO", "databricks RBAC", "databricks enterprise", "unity catalog permissions", "databricks SCIM".
allowed-tools	Read, Write, Edit, Bash(databricks:*)
version	1.0.0
license	MIT
author	Jeremy Longshore <jeremy@intentsolutions.io>
compatible-with	claude-code, codex, openclaw
tags	["saas","databricks","rbac"]

Databricks Enterprise RBAC

Overview

Implement enterprise access control using Unity Catalog privileges, SCIM-provisioned groups, workspace entitlements, cluster policies, and audit logging. Unity Catalog uses a three-level namespace (catalog.schema.object) with privilege inheritance: granting USAGE on a catalog cascades to schemas. Account-level SCIM syncs groups from your IdP (Okta, Azure AD, Google Workspace).

Prerequisites

Databricks Premium or Enterprise with Unity Catalog enabled
Account admin access for SCIM and group management
Identity Provider supporting SAML 2.0 and SCIM 2.0

Instructions

Step 1: Provision Groups via SCIM API

Sync groups from your IdP at the account level. Max 10,000 users + service principals and 5,000 groups per account.

# Create account-level groups that map to IdP teams
databricks account groups create --json '{
  "displayName": "data-engineers",
  "entitlements": [
    {"value": "workspace-access"},
    {"value": "databricks-sql-access"}
  ]
}'

databricks account groups create --json '{
  "displayName": "data-analysts",
  "entitlements": [
    {"value": "workspace-access"},
    {"value": "databricks-sql-access"}
  ]
}'

databricks account groups create --json '{
  "displayName": "ml-engineers",
  "entitlements": [
    {"value": "workspace-access"},
    {"value": "databricks-sql-access"},
    {"value": "allow-cluster-create"}
  ]
}'

# Assign groups to workspaces
from databricks.sdk import AccountClient

acct = AccountClient()

# Get workspace ID
workspaces = list(acct.workspaces.list())
prod_ws = next(ws for ws in workspaces if ws.workspace_name == "production")

# Assign group to workspace with permissions
acct.workspace_assignment.update(
    workspace_id=prod_ws.workspace_id,
    principal_id=group_id,
    permissions=["USER"],
)

Step 2: Unity Catalog Privilege Hierarchy

-- Privilege model: CATALOG > SCHEMA > TABLE/VIEW/FUNCTION
-- USAGE grants must cascade from catalog to schema

-- Data Engineers: full ETL access
GRANT USAGE ON CATALOG analytics TO `data-engineers`;
GRANT CREATE SCHEMA ON CATALOG analytics TO `data-engineers`;
GRANT CREATE, MODIFY, SELECT ON SCHEMA analytics.bronze TO `data-engineers`;
GRANT CREATE, MODIFY, SELECT ON SCHEMA analytics.silver TO `data-engineers`;
GRANT SELECT ON SCHEMA analytics.gold TO `data-engineers`;

-- Data Analysts: read-only curated data
GRANT USAGE ON CATALOG analytics TO `data-analysts`;
GRANT SELECT ON SCHEMA analytics.gold TO `data-analysts`;

-- ML Engineers: full ML lifecycle
GRANT USAGE ON CATALOG analytics TO `ml-engineers`;
GRANT SELECT ON SCHEMA analytics.gold TO `ml-engineers`;
GRANT ALL PRIVILEGES ON SCHEMA analytics.ml_features TO `ml-engineers`;
GRANT ALL PRIVILEGES ON SCHEMA analytics.ml_models TO `ml-engineers`;

-- Service Principal: CI/CD automation
GRANT USAGE ON CATALOG analytics TO `cicd-service-principal`;
GRANT ALL PRIVILEGES ON CATALOG analytics TO `cicd-service-principal`;

from databricks.sdk import WorkspaceClient

w = WorkspaceClient()

# Analyst policy: restrict to SQL warehouses and small clusters
analyst_policy = w.cluster_policies.create(
    name="analyst-compute-policy",
    definition="""{
        "cluster_type": {
            "type": "allowlist",
            "values": ["all-purpose"],
            "hidden": false
        },
        "autotermination_minutes": {
            "type": "range",
            "minValue": 10,
            "maxValue": 30,
            "defaultValue": 15
        },
        "num_workers": {
            "type": "range",
            "minValue": 0,
            "maxValue": 4
        },
        "node_type_id": {
            "type": "allowlist",
            "values": ["m5.xlarge", "m5.2xlarge"]
        },
        "spark_conf.spark.databricks.cluster.profile": {
            "type": "fixed",
            "value": "singleNode"
        }
    }""",
)

# Assign to analysts group
w.cluster_policies.set_permissions(
    cluster_policy_id=analyst_policy.policy_id,
    access_control_list=[{
        "group_name": "data-analysts",
        "all_permissions": [{"permission_level": "CAN_USE"}],
    }],
)

# Grant warehouse access by group
databricks permissions update sql/warehouses/$WAREHOUSE_ID --json '[
  {"group_name": "data-analysts", "permission_level": "CAN_USE"},
  {"group_name": "data-engineers", "permission_level": "CAN_MANAGE"},
  {"group_name": "ml-engineers", "permission_level": "CAN_USE"}
]'

-- Row filter: analysts only see their department's data
CREATE OR REPLACE FUNCTION analytics.gold.dept_filter(dept STRING)
  RETURN IF(IS_ACCOUNT_GROUP_MEMBER('data-admins'), true,
            dept = current_user_department());

ALTER TABLE analytics.gold.sales
  SET ROW FILTER analytics.gold.dept_filter ON (department);

-- Column mask: hide email from non-engineers
CREATE OR REPLACE FUNCTION analytics.gold.mask_email(email STRING)
  RETURN IF(IS_ACCOUNT_GROUP_MEMBER('data-engineers'), email,
            REGEXP_REPLACE(email, '(.).*@', '$1***@'));

ALTER TABLE analytics.gold.customers
  ALTER COLUMN email SET MASK analytics.gold.mask_email;

from databricks.sdk import AccountClient

acct = AccountClient()

# Create service principal
sp = acct.service_principals.create(
    display_name="cicd-pipeline",
    active=True,
)

# Generate OAuth secret
secret = acct.service_principal_secrets.create(
    service_principal_id=sp.id,
)
print(f"Client ID: {sp.application_id}")
print(f"Secret: {secret.secret}")  # Store securely — shown only once

-- Who accessed what in the last 7 days
SELECT event_time, user_identity.email AS actor,
       action_name, request_params
FROM system.access.audit
WHERE action_name LIKE '%Grant%' OR action_name LIKE '%Revoke%'
  AND event_date > current_date() - INTERVAL 7 DAYS
ORDER BY event_time DESC;

-- Excessive privilege detection
SELECT user_identity.email, action_name, COUNT(*) AS access_count
FROM system.access.audit
WHERE event_date > current_date() - INTERVAL 30 DAYS
  AND service_name = 'unityCatalog'
GROUP BY user_identity.email, action_name
HAVING COUNT(*) > 100
ORDER BY access_count DESC;

Issue	Cause	Solution
`PERMISSION_DENIED` on table	Missing `USAGE` on parent catalog/schema	Grant `USAGE` at each namespace level
SCIM sync fails	Expired bearer token	Regenerate account-level PAT or use OAuth
Can't create cluster	No matching cluster policy	Assign a policy to the user's group
Can't see SQL warehouse	Missing `CAN_USE` grant	Add warehouse permission for the group
Row filter too slow	Complex subquery in filter function	Materialize permissions in a small lookup table

SHOW GRANTS ON CATALOG analytics;
SHOW GRANTS `data-analysts` ON SCHEMA analytics.gold;
SHOW GRANTS ON TABLE analytics.gold.sales;

Role	Bronze	Silver	Gold	ML	Clusters	Warehouses
Data Engineer	Read/Write	Read/Write	Read	-	Create (policy)	Use/Manage
Data Analyst	-	-	Read	-	Single-node (policy)	Use
ML Engineer	-	Read	Read	Read/Write	Create (policy)	Use
Admin	Full	Full	Full	Full	Unrestricted	Manage
CI/CD SP	Full	Full	Full	Full	Manage	-