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Data warehouse ⇄ Human resources

Databricks to Oracle Fusion Cloud HCM integration — real-time, two-way sync

Keep Databricks and Oracle Fusion Cloud HCM 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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Why teams connect Databricks and Oracle Fusion Cloud HCM

Land the people and organization records from Oracle Fusion Cloud HCM in Databricks as live tables for workforce reporting, without extract jobs, and write computed results back where Oracle Fusion Cloud HCM 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 Oracle Fusion Cloud HCM 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 Assignments, Jobs, Positions, Departments (Organizations) from Oracle Fusion Cloud HCM 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 Oracle Fusion Cloud HCM 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 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 Two-way sync Workers and Assignments with an operational Postgres so internal apps read reporting lines, departments, and locations in SQL while HR keeps mastering data in Oracle HCM.
  • 04 Provision identities and app access downstream when an Atom new-hire or termination feed event fires, joining Workers, Assignments, Departments, and Locations.

Common sync patterns

Fresh data instead of last night's load

Because changes stream continuously, reports query current workforce data rather than waiting for an overnight load window to finish.

Queryable history for planning and audit

A continuously synced copy in Databricks gives you a durable, queryable record of how Oracle Fusion Cloud HCM's records change over time, for headcount planning and audit questions.

Write-back of computed values

Segments, rollups, or risk flags computed in Databricks sync back onto the matching records in Oracle Fusion Cloud HCM, where the HR team sees them in the system they already use.

What you can sync between Databricks and Oracle Fusion Cloud HCM

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 Oracle Fusion Cloud HCM objects How this pairing syncs
Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. Departments (Organizations) Org-structure records that define cost centers and reporting lines; typically mastered in HCM and read into other systems. Catalogs is specific to Databricks and Departments (Organizations) to Oracle Fusion Cloud HCM — 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. Locations Physical work locations referenced by assignments; synced so downstream provisioning and directory tools resolve the same location codes. Schemas is specific to Databricks and Locations to Oracle Fusion Cloud HCM — 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. Grades and Salaries Grade, grade-rate, and worker compensation records; usually read out for compensation and headcount analytics, and written back for corrections. Delta Tables is specific to Databricks and Grades and Salaries to Oracle Fusion Cloud HCM — each maps to any object or custom field on the other side.
Views Curated read-only projections used as sync sources for downstream tools. Absences (workerAbsenceEntries) Time-off and leave entries; synced into scheduling or workforce-management databases for coverage planning. Views is specific to Databricks and Absences (workerAbsenceEntries) to Oracle Fusion Cloud HCM — 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. Workers (publicWorkers) Person master with personNumber, names, national IDs, and work relationships; synced two-way via the /workers resource, which replaced the desupported /emps resource. Materialized Views is specific to Databricks and Workers (publicWorkers) to Oracle Fusion Cloud HCM — each maps to any object or custom field on the other side.
Volumes Unity Catalog file storage used for staging bulk loads. Assignments Child of a worker's work relationship carrying job, position, department, location, grade, and manager; effective-dated, so writes go through PATCH with an effective date. Volumes is specific to Databricks and Assignments to Oracle Fusion Cloud HCM — each maps to any object or custom field on the other side.

How changes propagate between Databricks and Oracle Fusion Cloud HCM

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 Oracle Fusion Cloud HCM 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 Oracle Fusion Cloud HCM through its API, with automatic retries and rate-limit backoff.

Oracle Fusion Cloud HCM Databricks Sub-second propagation

DetectionChanges in Oracle Fusion Cloud HCM are captured at the source via change data capture — no polling loop against its API. Atom feeds expose changes-only feeds for key events (new hire, termination, assignment change).

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.
  • Oracle Fusion Cloud HCM: Oracle publishes no fixed per-tenant REST rate limit; pages are capped via limit/offset and Oracle recommends client-side throttling, backoff, and using HDL for mass loads rather than transactional REST.
What ships with Databricks ⇄ Oracle Fusion Cloud HCM

Connect Databricks and Oracle Fusion Cloud HCM for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Databricks–Oracle Fusion Cloud HCM connection.

Real-time

Two-way sync

Changes in Databricks or Oracle Fusion Cloud HCM instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Databricks or Oracle Fusion Cloud HCM 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 Oracle Fusion Cloud HCM record.

Observability

Monitoring

Track your Databricks ⇄ Oracle Fusion Cloud HCM 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 Oracle Fusion Cloud HCM.

How the Databricks and Oracle Fusion Cloud HCM 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

Oracle Fusion Cloud HCM

Integration surface
REST API (/hcmRestApi/resources), Atom feeds, and HCM Data Loader (HDL) for bulk
Authentication
OAuth 2.0 bearer tokens via Oracle Identity Cloud Service (IDCS/IAM); HTTP Basic and SAML/JWT bearer over SSL are also accepted, enforced by Oracle Web Services Manager (OWSM)
Change detection
Atom feeds expose changes-only feeds for key events (new hire, termination, assignment change); REST q= filters on LastUpdateDate and HCM Extract in incremental mode cover broader deltas
Capabilities
read · write · CDC
Rate limits
Oracle publishes no fixed per-tenant REST rate limit; pages are capped via limit/offset and Oracle recommends client-side throttling, backoff, and using HDL for mass loads rather than transactional REST.
How it works

How to connect Databricks to Oracle Fusion Cloud HCM — 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 Oracle Fusion Cloud HCM 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
    Oracle Fusion Cloud HCM connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Databricks and Oracle Fusion Cloud HCM 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 ⇄ Oracle Fusion Cloud HCM
    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 Oracle Fusion Cloud HCM
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Databricks and Oracle Fusion Cloud HCM 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.

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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 549 integrations available for Databricks and Oracle Fusion Cloud HCM.

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