Skip to content
Data warehouse ⇄ Security and identity

Databricks to Okta integration — real-time, two-way sync

Keep Databricks and Okta 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
Case study
Migrated from Celigo
Migrated from Heroku Connect
Migrated from Matillion
Case study
Migrated from Fivetran
Case study
Migrated from Celigo
Why teams connect Databricks and Okta

Land the users, groups, and access events from Okta in Databricks continuously for security analytics, and write computed results back — without building or maintaining a pipeline.

Okta 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 Okta 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 Getting them there usually means a brittle export that runs overnight and hands auditors a snapshot that is already out of date.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 Devices, Users, Groups, Applications from Okta 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 Okta 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 Land CRM and ERP records in Delta tables continuously so lakehouse models work from current operational data.
  • 02 Use Change Data Feed to propagate only changed rows to downstream apps instead of full-table scans.
  • 03 Read Application assignments and Factors into a database to report on who can reach which app and which users have strong MFA enrolled.
  • 04 Drive Group Rules and policy assignments from HR attributes so access changes the moment a title, department, or location changes.

Common sync patterns

Access and sign-in analytics

Sign-in and access events from Okta 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 Okta'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 Databricks and Okta

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.

Databricks objects Okta objects How this pairing syncs
Change Data Feed Row-level change records on Delta tables that drive incremental reads. Group Rules Dynamic rules that add users to groups from profile attributes; read and written to automate access based on department, title, or location. Change Data Feed is specific to Databricks and Group Rules to Okta — each maps to any object or custom field on the other side.
Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. Factors (Authenticators) MFA enrollments per user (Okta Verify, WebAuthn, SMS, email); read for security-posture reporting and reset when a device is lost or rotated. Catalogs is specific to Databricks and Factors (Authenticators) to Okta — each maps to any object or custom field on the other side.
Schemas Group tables and views; syncs typically target a dedicated schema per source system. Policies Sign-on, password, and authenticator-enrollment policies with their rules; read to audit access controls or updated to manage them centrally. Schemas is specific to Databricks and Policies to Okta — each maps to any object or custom field on the other side.
Delta Tables The primary read and write target; operational data lands here as managed or external tables. System Log Immutable audit event stream of logins, admin changes, and lifecycle events; read-only, polled by timestamp and used as the change feed and SIEM source. Delta Tables is specific to Databricks and System Log to Okta — each maps to any object or custom field on the other side.
Views Curated read-only projections used as sync sources for downstream tools. Devices Registered and managed device records tied to users; read to correlate access with device posture and to feed inventory or conditional-access reporting. Views is specific to Databricks and Devices to Okta — each maps to any object or custom field on the other side.
Materialized Views Precomputed results read on a schedule for reverse-ETL style syncs. Users Universal Directory user records with profile attributes, credentials, and lifecycle status (staged, active, suspended, deprovisioned); full CRUD, so users are created, updated, activated, and deactivated to match an HR or identity source. Materialized Views is specific to Databricks and Users to Okta — each maps to any object or custom field on the other side.

How changes propagate between Databricks and Okta

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.

Databricks Okta 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 Okta through its API, with automatic retries and rate-limit backoff.

Okta Databricks Sub-second propagation

DetectionOkta notifies Stacksync of record changes through webhook events. Event Hooks send an HTTPS POST to a subscribed endpoint on events such as user.lifecycle.create and group.user_membership.add (after a one-time.

DeliveryEach detected change is applied to Databricks as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • Databricks: Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits.
  • Okta: Rate limits are enforced per org, per endpoint group, per minute and vary by Okta edition; responses carry X-Rate-Limit-Limit, X-Rate-Limit-Remaining, and X-Rate-Limit-Reset headers and exceeding one returns HTTP 429. The System Log is more tightly limited (about 120 requests/minute) and a separate concurrent-request cap (default 75 in-flight transactions) also applies.
What ships with Databricks ⇄ Okta

Connect Databricks and Okta for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Databricks–Okta connection.

Real-time

Two-way sync

Changes in Databricks or Okta instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Databricks or Okta 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 Databricks or Okta record.

Observability

Monitoring

Track your Databricks ⇄ Okta sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Databricks and Okta.

How the Databricks and Okta connectors work

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

Okta

Integration surface
REST API (Okta Management API, /api/v1) with Event Hooks for outbound events and the System Log API as the change feed
Authentication
SSWS API tokens (Authorization: SSWS {token}) scoped to the creating admin's permissions, or OAuth 2.0 for Okta service apps using a private-key-JWT client-credentials grant with granular scopes (DPoP supported)
Change detection
Event Hooks send an HTTPS POST to a subscribed endpoint on events such as user.lifecycle.create and group.user_membership.add (after a one-time verification handshake); the System Log API (/api/v1/logs) is polled by the since parameter and the next Link header for a near-real-time change feed
Capabilities
read · write · webhooks
Rate limits
Rate limits are enforced per org, per endpoint group, per minute and vary by Okta edition; responses carry X-Rate-Limit-Limit, X-Rate-Limit-Remaining, and X-Rate-Limit-Reset headers and exceeding one returns HTTP 429. The System Log is more tightly limited (about 120 requests/minute) and a separate concurrent-request cap (default 75 in-flight transactions) also applies.
How it works

How to connect Databricks to Okta — 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.

  1. 01

    Connect your apps

    Authenticate Databricks and Okta with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.

    • OAuth 2.0
    • SSH tunnel
    • VPC peering
    Databricks connected
    Okta connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Databricks and Okta 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.

    • Standard objects
    • Custom objects
    • Auto-schema
    objects · Databricks ⇄ Okta
    Customers 12,480
    Sales Orders 8,213
    Invoices 5,902
    Items 1,344
  3. 03

    Map fields

    Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.

    • Auto-map
    • Type casting
    • Transforms
    Databricks Okta
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
Try interactive demo
FAQ

Databricks and Okta integration FAQ

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.

Learn more about security
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:

OAuth 2 SSH Tunnelling SSL certificates IP Whitelisting VPN gateway VPC peering and more

Related integrations

Every pair below is a real-time, two-way sync. Search all 548 integrations available for Databricks and Okta.

Popular · 7 of 548
All Databricks integrations All Okta integrations Browse all integrations
Coworkers laughing in front of a laptop in a casual office setting

Your last integration took months.
Your next one takes a prompt.

Book a demo Tour the platform on your own