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Data warehouse ⇄ Human resources

Databricks to Success Factors integration — real-time, two-way sync

Keep Databricks and Success Factors 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 Success Factors

Land the people and organization records from Success Factors in Databricks as live tables for workforce reporting, without extract jobs, and write computed results back where Success Factors 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 Success Factors 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 EmpCompensation, Foundation Objects (FODepartment, FOLocation, FOCostCenter), Position, PerEmail and PerPhone from Success Factors 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 Success Factors 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 Use Change Data Feed to propagate only changed rows to downstream apps instead of full-table scans.
  • 02 Serve ML feature outputs computed in Databricks to production apps through a synced operational store.
  • 03 Push new hires (PerPerson, EmpEmployment, EmpJob) into an ERP or payroll system so account, cost-center, and payroll setup fire on hire instead of manual Integration Center exports.
  • 04 Replicate EmpCompensation and EmpJob effective-dated history into a warehouse for headcount, attrition, and compensation reporting without CSV pulls.

Common sync patterns

Write-back of computed values

Segments, rollups, or risk flags computed in Databricks sync back onto the matching records in Success Factors, 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 Success Factors 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 Success Factors'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 Success Factors

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 Success Factors objects How this pairing syncs
Views Curated read-only projections used as sync sources for downstream tools. PerPerson Person-level container in Employee Central holding biographical data; effective-dated child entities (PerPersonal, PerEmail, PerPhone) hang off it. Views is specific to Databricks and PerPerson to Success Factors — 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. EmpEmployment Employment record tying a person to an employment period (hire, service dates); carries lastModifiedDateTime used for incremental polling. Materialized Views is specific to Databricks and EmpEmployment to Success Factors — each maps to any object or custom field on the other side.
Volumes Unity Catalog file storage used for staging bulk loads. EmpJob Effective-dated job info: position, department, manager, FTE, pay grade, cost center; the most-synced record for downstream HR and provisioning. Volumes is specific to Databricks and EmpJob to Success Factors — each maps to any object or custom field on the other side.
SQL Warehouses The compute endpoint a sync connects to for query execution. EmpCompensation Effective-dated pay and compensation; usually read into a warehouse for reporting, writable for comp updates as new dated slices. SQL Warehouses is specific to Databricks and EmpCompensation to Success Factors — 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. Foundation Objects (FODepartment, FOLocation, FOCostCenter) Org-structure master data (department, location, cost center, division); mastered elsewhere and written in, or read out to build org charts. Change Data Feed is specific to Databricks and Foundation Objects (FODepartment, FOLocation, FOCostCenter) to Success Factors — 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. Position Position Management records for headcount and requisition planning; synced with an ATS or ERP to keep positions and reqs aligned. Catalogs is specific to Databricks and Position to Success Factors — each maps to any object or custom field on the other side.

How changes propagate between Databricks and Success Factors

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 Success Factors 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 Success Factors through its API, with automatic retries and rate-limit backoff.

Success Factors Databricks Sub-second propagation

DetectionSuccess Factors notifies Stacksync of record changes through webhook events. Polling on each entity's lastModifiedDateTime / lastModifiedOn (effective-dated entities require date-range handling).

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.
  • Success Factors: OData calls are throttled at the tenant level (Access Limits for OData V2); large reads must be paginated (default page size 1000) via $top/$skip or paging cursors.
What ships with Databricks ⇄ Success Factors

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Databricks or Success Factors 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 Success Factors record.

Observability

Monitoring

Track your Databricks ⇄ Success Factors 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 Success Factors.

How the Databricks and Success Factors 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

Success Factors

Integration surface
OData V2 and V4 REST APIs (plus legacy SFAPI / Compound Employee SOAP API)
Authentication
OAuth 2.0 SAML Bearer Assertion — register an OAuth client for an API key (used as client_id), then exchange a signed SAML assertion for a short-lived access token; legacy HTTP Basic auth is being retired
Change detection
Polling on each entity's lastModifiedDateTime / lastModifiedOn (effective-dated entities require date-range handling); Intelligent Services can also push a fixed set of standard business events (e.g. Employee Hire) to a REST endpoint
Capabilities
read · write · webhooks
Rate limits
OData calls are throttled at the tenant level (Access Limits for OData V2); large reads must be paginated (default page size 1000) via $top/$skip or paging cursors.
How it works

How to connect Databricks to Success Factors — 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 Success Factors 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
    Success Factors connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Databricks and Success Factors 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 ⇄ Success Factors
    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 Success Factors
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Databricks and Success Factors 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 550 integrations available for Databricks and Success Factors.

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