Real-time
Two-way sync
Changes in Azure Active Directory or Databricks instantly reflect in both systems. No stale data, no manual imports.
Security and identity ⇄ Data warehouse
Azure Active Directory to Databricks integration — real-time, two-way sync
Keep Azure Active Directory and Databricks in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.
- SOC 2 and 6 other compliance frameworks
- POC with real engineers in minutes
Adopted by fast-scaling companies moving mission-critical data in real time
Case study
Migrated from MuleSoft
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Migrated from Fivetran
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Migrated from Celigo
Why teams connect Azure Active Directory and Databricks
Land the users, groups, and access events from Azure Active Directory in Databricks continuously for security analytics, and write computed results back — without building or maintaining a pipeline.
Azure Active Directory is the record of who exists and what they can reach; Databricks is where the business measures everything else. The two overlap on people and their access — the same users, groups, roles, and events that Azure Active Directory governs are what security, compliance, and analytics teams want to query in Databricks. Getting them there usually means a brittle export that runs overnight and hands auditors a snapshot that is already out of date.
Stacksync syncs Users, Groups, Group memberships, Applications from Azure Active Directory into tables in Databricks in real time, and the connection works in both directions: values computed in Databricks, such as risk scores or access-review decisions, can be written back to attributes in Azure Active Directory where the identity team acts on them. Schema changes are handled, API limits are managed, and the sync is something you configure rather than a pipeline you keep alive.
Common use cases
- 01 Serve ML feature outputs computed in Databricks to production apps through a synced operational store.
- 02 Land CRM and ERP records in Delta tables continuously so lakehouse models work from current operational data.
- 03 Replicate Groups and their Members into a Postgres warehouse for access reviews and entitlement reporting.
- 04 Mirror Applications and Service principals into a database or CMDB to track app ownership and credential expiry.
Common sync patterns
Access and sign-in analytics
Sign-in and access events from Azure Active Directory land in Databricks, so security and compliance teams can query who reached what, and when, alongside the rest of the business's data.
Access governance on joined data
Join Azure Active Directory's users and group memberships with HR, product, and usage data already in Databricks to surface who holds access they no longer need.
Queryable history for audit and compliance
A continuously synced copy in Databricks gives you a durable, queryable record of identity and access state for access reviews, SOC 2, and audit questions.
What you can sync between Azure Active Directory and Databricks
Representative objects on each side — any object or custom field can map to any target. Schemas are auto-detected; types are converted between the two systems.
| Azure Active Directory objects | Databricks objects | How this pairing syncs | |
|---|---|---|---|
| Groups /groups covering security and Microsoft 365 groups; created, updated, and deleted through Graph, and read into warehouses for entitlement reporting. | Volumes Unity Catalog file storage used for staging bulk loads. | Groups is specific to Azure Active Directory and Volumes to Databricks — each maps to any object or custom field on the other side. | |
| Group memberships Member and owner relationships on /groups/{id}/members; added and removed via the $ref endpoint and tracked for changes with delta query on $select=members. | SQL Warehouses The compute endpoint a sync connects to for query execution. | Group memberships is specific to Azure Active Directory and SQL Warehouses to Databricks — each maps to any object or custom field on the other side. | |
| Applications App registrations under /applications; usually read into a database or CMDB for app ownership and credential-expiry tracking. | Change Data Feed Row-level change records on Delta tables that drive incremental reads. | Applications is specific to Azure Active Directory and Change Data Feed to Databricks — each maps to any object or custom field on the other side. | |
| Service principals /servicePrincipals (enterprise apps) plus appRoleAssignments; read for app inventory and access-posture reporting. | Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. | Service principals is specific to Azure Active Directory and Catalogs to Databricks — each maps to any object or custom field on the other side. | |
| Directory roles /directoryRoles and roleManagement assignments; read for privileged-access reviews, with role-assignment writes where the granted scopes permit. | Schemas Group tables and views; syncs typically target a dedicated schema per source system. | Directory roles is specific to Azure Active Directory and Schemas to Databricks — each maps to any object or custom field on the other side. | |
| Devices /devices registered or joined to the tenant; typically read-only into asset and security databases. | Delta Tables The primary read and write target; operational data lands here as managed or external tables. | Devices is specific to Azure Active Directory and Delta Tables to Databricks — each maps to any object or custom field on the other side. |
How changes propagate between Azure Active Directory and Databricks
Each direction of the sync is driven by what the source system can signal and what the destination accepts — detection, delivery, and expected latency below.
Azure Active Directory Databricks Sub-second propagation
DetectionAzure Active Directory notifies Stacksync of record changes through webhook events. Microsoft Graph delta query (deltaLink tokens returning only changed users and groups, with @removed deletions) paired with change-notification.
DeliveryEach detected change is applied to Databricks as a row-level write, with types converted between the two schemas.
Databricks Azure Active Directory Sub-second propagation
DetectionChanges in Databricks are captured at the source via change data capture — no polling loop against its API. Delta Lake Change Data Feed for row-level changes.
DeliveryEach detected change is written to Azure Active Directory through its API, with automatic retries and rate-limit backoff.
Rate-limit considerations
- Azure Active Directory: Resource-unit throttling: roughly 8,000 ResourceUnits per 10 seconds per app for reads on tenants over 500 users, writes near 3,000 requests per 2.5 minutes; 429 responses include a Retry-After header.
- Databricks: Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits.
What ships with Azure Active Directory ⇄ Databricks
Connect Azure Active Directory and Databricks for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Azure Active Directory–Databricks connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Azure Active Directory or Databricks data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.
At scale
Event queues
Handle millions of events per minute without losing a single Azure Active Directory or Databricks record.
Observability
Monitoring
Track your Azure Active Directory ⇄ Databricks sync health, view errors, and replay failed events in one click.
Trading partners
EDI
Transform legacy EDI complexity into simple database interactions between Azure Active Directory and Databricks.
How the Azure Active Directory and Databricks connectors work
Azure Active Directory
- Integration surface
- Microsoft Graph REST API (v1.0)
- Authentication
- OAuth 2.0 via the Microsoft identity platform using an Entra ID app registration; app-only (client credentials) or delegated flows, with directory scopes such as User.ReadWrite.All and Group.ReadWrite.All requiring tenant admin consent
- Change detection
- Microsoft Graph delta query (deltaLink tokens returning only changed users and groups, with @removed deletions) paired with change-notification webhook subscriptions for near-real-time push
- Capabilities
- read · write · webhooks
- Rate limits
- Resource-unit throttling: roughly 8,000 ResourceUnits per 10 seconds per app for reads on tenants over 500 users, writes near 3,000 requests per 2.5 minutes; 429 responses include a Retry-After header
Databricks
- Integration surface
- SQL over JDBC/ODBC via SQL warehouses, plus a REST API including statement execution
- Authentication
- Personal access tokens or OAuth machine-to-machine credentials for service principals
- Change detection
- Delta Lake Change Data Feed for row-level changes; otherwise incremental polling on watermark columns
- Capabilities
- read · write · CDC
- Rate limits
- Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits
How it works
How to connect Azure Active Directory to Databricks — three steps, no code
Configure and sync within minutes, no code. Whether you sync 50k or 100M+ records, Stacksync handles the queues, infra, and plumbing. Integrations are non-invasive and need zero setup on your systems.
- 01
Connect your apps
Authenticate Azure Active Directory and Databricks with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Azure Active Directory
connected
Databricks
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Azure Active Directory and Databricks objects to sync — Stacksync auto-detects both schemas, including custom fields where the platform exposes them. Sync to existing tables, or let Stacksync create new ones with ideal data types.
objects · Azure Active Directory ⇄ DatabricksCustomers 12,480Sales Orders 8,213Invoices 5,902Items 1,344 - 03
Map fields
Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.
Azure Active Directory DatabricksCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Azure Active Directory and Databricks integration FAQ
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Yes. Stacksync provides a managed, real-time two-way integration between Azure Active Directory and Databricks: authenticate both systems, choose the objects to sync (such as Azure Active Directory's Groups and Group memberships), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Stacksync is SOC 2 Type II and ISO 27001 certified with HIPAA BAA support. Data is encrypted in transit, and a zero-persistent-storage architecture means Azure Active Directory and Databricks records are not retained after a sync operation.
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Stacksync pricing is usage-based and starts at $1,000/month, including the managed Azure Active Directory and Databricks connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Azure Active Directory–Databricks integration in-house.
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Yes — Stacksync ships production-grade connectors for both Azure Active Directory and Databricks. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
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Change detection on Azure Active Directory: Microsoft Graph delta query (deltaLink tokens returning only changed users and groups, with @removed deletions) paired with change-notification webhook subscriptions for near-real-time push. On Databricks: Delta Lake Change Data Feed for row-level changes; otherwise incremental polling on watermark columns. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
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On the Databricks side: Materialized Views, Volumes, SQL Warehouses, Change Data Feed, plus custom fields where Databricks exposes them. On the Azure Active Directory side: Users, Groups, Group memberships, Applications. Stacksync auto-detects both schemas and converts types between the two systems.
SECURITY
Security teams trust Stacksync
As a data company, we understand the importance of keeping your data secure. Stacksync is built with security best practices to keep your data safe at every layer, and is DPF-certified for US, EU, UK and CH data transfers.
SOC 2 Type II
ISO 27001
HIPAA BAA
GDPR
CCPA
DPF US-EU-UK-CH
→ SECURITY WITH BENEFITS
SSO & SCIM
Let your users access Stacksync from your centralized user management systems. Works with Okta, Azure, Google SSO and more.
Alerts
Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.
Secure connection options
Securely connects to your systems with:
Related integrations
Every pair below is a real-time, two-way sync. Search all 548 integrations available for Azure Active Directory and Databricks.
Popular · 8 of 548
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Azure Active Directory ⇄ Snowflake -
Azure Active Directory ⇄ BigQuery -
Azure Active Directory ⇄ MotherDuck -
Azure Active Directory ⇄ AWS S3 -
Databricks ⇄ Onelogin -
Databricks ⇄ Auth0 -
Databricks ⇄ Jumpcloud - Databricks ⇄ Active Directory
No integrations match .
