Real-time
Two-way sync
Changes in Databricks or Hibob instantly reflect in both systems. No stale data, no manual imports.
Data warehouse ⇄ Human resources
Databricks to Hibob integration — real-time, two-way sync
Keep Databricks and Hibob in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.
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Adopted by fast-scaling companies moving mission-critical data in real time
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Why teams connect Databricks and Hibob
Land the people and organization records from Hibob in Databricks as live tables for workforce reporting, without extract jobs, and write computed results back where Hibob can use them.
Workforce data is some of the most requested data in the warehouse and some of the most awkward to move: the record types are many, the fields carry sensitive personal information, the APIs are strict, and hand-built extract jobs go stale or break quietly. Whether Hibob is the system of record for employees and payroll, for candidates and applications, or for learners and course completions, the reporting belongs in Databricks next to everything else the company measures.
Stacksync syncs Lifecycle, Documents, Named lists, People (Employees) from Hibob into tables in Databricks continuously, handling API limits and schema drift as they come. The connection is bi-directional, so values computed in Databricks, such as headcount rollups, cost allocations, or attrition risk flags, can be written back to fields in Hibob where the HR team acts on them. You configure which records and fields cross over, and in which direction, instead of maintaining pipeline code.
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 Sync Time off requests and who's-out data into a scheduling or workforce-planning database for coverage and capacity reporting.
- 04 Two-way sync People (employee) profiles and Work records with a Postgres database so internal apps and directories read profiles in SQL while HR corrections flow back into Bob.
Common sync patterns
Write-back of computed values
Segments, rollups, or risk flags computed in Databricks sync back onto the matching records in Hibob, where the HR team sees them in the system they already use.
HR data in the warehouse, minus the pipeline
People and organization records from Hibob arrive in Databricks as queryable tables, current within seconds instead of a day behind, replacing hand-built extract jobs.
Headcount and cost joined with everything else
Analysts combine Hibob's workforce records with finance, product, or operational data already in Databricks for reporting the HR system cannot produce on its own.
What you can sync between Databricks and Hibob
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 | Hibob objects | How this pairing syncs | |
|---|---|---|---|
| Views Curated read-only projections used as sync sources for downstream tools. | Lifecycle Hire, termination, and leave status changes at /people/{id}/lifecycle; read-only and the source of lifecycle webhook events. | Views is specific to Databricks and Lifecycle to Hibob — 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. | Documents Employee documents accessed under the Docs API; gated behind the Documents permission granted per service user. | Materialized Views is specific to Databricks and Documents to Hibob — each maps to any object or custom field on the other side. | |
| Volumes Unity Catalog file storage used for staging bulk loads. | Named lists Dropdown option lists such as departments and sites used to resolve field values when mapping People records during sync. | Volumes is specific to Databricks and Named lists to Hibob — each maps to any object or custom field on the other side. | |
| SQL Warehouses The compute endpoint a sync connects to for query execution. | People (Employees) Core employee records with out-of-the-box and custom fields; read via POST /people/search, created and updated through the /people endpoints. | SQL Warehouses is specific to Databricks and People (Employees) to Hibob — each maps to any object or custom field on the other side. | |
| Change Data Feed Row-level change records on Delta tables that drive incremental reads. | Employment Historical table of job terms, contract, and working pattern under /people/{id}/employment; full CRUD, entries keyed by effectiveDate. | Change Data Feed is specific to Databricks and Employment to Hibob — 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. | Work Reporting line, department, site, and title under /people/{id}/work; drives the org chart and is synced two-way to directories. | Catalogs is specific to Databricks and Work to Hibob — each maps to any object or custom field on the other side. |
How changes propagate between Databricks and Hibob
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 Hibob 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 Hibob through its API, with automatic retries and rate-limit backoff.
Hibob Databricks Sub-second propagation
DetectionHibob notifies Stacksync of record changes through webhook events. Webhooks fire for employee created/updated/deleted, table-entry created/updated, time off, and lifecycle events.
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.
- Hibob: Rate limits are enforced per service user; violations return HTTP 429 with X-RateLimit-Limit, X-RateLimit-Remaining, and X-RateLimit-Reset response headers. The docs list POST /people/search at 50 requests per minute, and bulk /people/search is preferred over per-record reads.
What ships with Databricks ⇄ Hibob
Connect Databricks and Hibob for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Databricks–Hibob connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Databricks or Hibob 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 Hibob record.
Observability
Monitoring
Track your Databricks ⇄ Hibob 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 Hibob.
How the Databricks and Hibob 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
Hibob
- Integration surface
- REST API (the Bob API)
- Authentication
- Service User credentials over HTTP Basic auth (Base64-encoded serviceUserId:serviceUserToken); permissions granted per data category (People, Payroll, Documents) in Bob admin
- Change detection
- Webhooks fire for employee created/updated/deleted, table-entry created/updated, time off, and lifecycle events; the employee.updated payload flags which fields changed and Bob recommends an API call for the full record. There is no CDC stream, and deleting a table entry fires no webhook (Bob has no table-entry deletion event).
- Capabilities
- read · write · webhooks
- Rate limits
- Rate limits are enforced per service user; violations return HTTP 429 with X-RateLimit-Limit, X-RateLimit-Remaining, and X-RateLimit-Reset response headers. The docs list POST /people/search at 50 requests per minute, and bulk /people/search is preferred over per-record reads.
How it works
How to connect Databricks to Hibob — 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 Databricks and Hibob with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Databricks
connected
Hibob
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Databricks and Hibob 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 · Databricks ⇄ HibobCustomers 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.
Databricks HibobCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Databricks and Hibob integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Databricks and Hibob: authenticate both systems, choose the objects to sync (such as Databricks's Views and Materialized Views), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Change detection on Databricks: Delta Lake Change Data Feed for row-level changes; otherwise incremental polling on watermark columns. On Hibob: Webhooks fire for employee created/updated/deleted, table-entry created/updated, time off, and lifecycle events; the employee.updated payload flags which fields changed and Bob recommends an API call for the full record. There is no CDC stream, and deleting a table entry fires no webhook (Bob has no table-entry deletion event). 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: SQL Warehouses, Change Data Feed, Catalogs, Schemas, plus custom fields where Databricks exposes them. On the Hibob side: Lifecycle, Documents, Named lists, People (Employees). Stacksync auto-detects both schemas and converts types between the two systems.
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Yes. Each object mapping can be bidirectional or restricted to a single direction (both systems accept writes). Read-only mirrors, one-way pushes, and full two-way sync can be mixed in the same integration.
-
Common patterns for Databricks and Hibob: Write-back of computed values; HR data in the warehouse, minus the pipeline; Headcount and cost joined with everything else. Segments, rollups, or risk flags computed in Databricks sync back onto the matching records in Hibob, where the HR team sees them in the system they already use.
-
Databricks: 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. Hibob: REST API (the Bob API). Authentication: Service User credentials over HTTP Basic auth (Base64-encoded serviceUserId:serviceUserToken); permissions granted per data category (People, Payroll, Documents) in Bob admin. Stacksync manages authentication, retries, and rate limits on both sides.
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 550 integrations available for Databricks and Hibob.
Popular · 7 of 550
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Databricks ⇄ Greenhouse -
Databricks ⇄ Jobvite -
Databricks ⇄ Lever -
Databricks ⇄ Namely -
Hibob ⇄ MotherDuck -
Hibob ⇄ AWS S3 -
Hibob ⇄ Amazon Redshift
No integrations match .
