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Data warehouse ⇄ Human resources

Cloudera Data Platform to Success Factors integration — real-time, two-way sync

Keep Cloudera Data Platform 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 Cloudera Data Platform and Success Factors

Land the people and organization records from Success Factors in Cloudera Data Platform 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 Cloudera Data Platform next to everything else the company measures.

Stacksync syncs Position, PerEmail and PerPhone, User, PerPerson from Success Factors into tables in Cloudera Data Platform continuously, handling API limits and schema drift as they come. The connection is bi-directional, so values computed in Cloudera Data Platform, 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 Publish CRM or ERP records into CDP so enterprise analytics runs alongside existing data lake workloads.
  • 02 Consolidate tables from on-prem and cloud CDP environments into a single cloud warehouse target.
  • 03 Replicate EmpCompensation and EmpJob effective-dated history into a warehouse for headcount, attrition, and compensation reporting without CSV pulls.
  • 04 Keep Foundation Objects (department, location, cost center) aligned between SuccessFactors and an ERP so cost-center and org hierarchies match.

Common sync patterns

Queryable history for planning and audit

A continuously synced copy in Cloudera Data Platform gives you a durable, queryable record of how Success Factors's records change over time, for headcount planning and audit questions.

Write-back of computed values

Segments, rollups, or risk flags computed in Cloudera Data Platform 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 Cloudera Data Platform as queryable tables, current within seconds instead of a day behind, replacing hand-built extract jobs.

What you can sync between Cloudera Data Platform 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.

Cloudera Data Platform objects Success Factors objects How this pairing syncs
Hive tables Warehouse tables queried over JDBC/ODBC; classic managed tables are append-oriented. EmpCompensation Effective-dated pay and compensation; usually read into a warehouse for reporting, writable for comp updates as new dated slices. Hive tables is specific to Cloudera Data Platform and EmpCompensation to Success Factors — each maps to any object or custom field on the other side.
Impala tables The same metastore tables served through Impala for lower-latency SQL 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. Impala tables is specific to Cloudera Data Platform and Foundation Objects (FODepartment, FOLocation, FOCostCenter) to Success Factors — each maps to any object or custom field on the other side.
Kudu tables Storage engine tables that support row-level inserts, updates, and deletes. Position Position Management records for headcount and requisition planning; synced with an ATS or ERP to keep positions and reqs aligned. Kudu tables is specific to Cloudera Data Platform and Position to Success Factors — each maps to any object or custom field on the other side.
Iceberg tables Open table format tables in newer CDP versions, with snapshot metadata usable for incremental reads. PerEmail and PerPhone Contact sub-entities under PerPerson; synced two-way with a directory or comms system to keep worker contact data current. Iceberg tables is specific to Cloudera Data Platform and PerEmail and PerPhone to Success Factors — each maps to any object or custom field on the other side.
Views SQL views that can present curated, sync-ready projections of raw lake data. User Core identity/User entity behind role-based permissions; a model distinct from Employee Central, synced two-way with a directory or IdP and downstream apps. Views is specific to Cloudera Data Platform and User to Success Factors — each maps to any object or custom field on the other side.
Partitions Table partitions (often by date) that incremental extraction jobs use to scope reads. PerPerson Person-level container in Employee Central holding biographical data; effective-dated child entities (PerPersonal, PerEmail, PerPhone) hang off it. Partitions is specific to Cloudera Data Platform and PerPerson to Success Factors — each maps to any object or custom field on the other side.

How changes propagate between Cloudera Data Platform 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.

Cloudera Data Platform Success Factors Interval-based propagation

DetectionStacksync polls Cloudera Data Platform for changes on an incremental schedule, reading only records changed since the previous pass. Polling via SQL on timestamp or partition columns.

DeliveryEach detected change is written to Success Factors through its API, with automatic retries and rate-limit backoff.

Success Factors Cloudera Data Platform 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 Cloudera Data Platform as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • Cloudera Data Platform: Constrained by cluster capacity and admission control rather than API 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 Cloudera Data Platform ⇄ Success Factors

Connect Cloudera Data Platform and Success Factors for flexible, real-time data sync.

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Cloudera Data Platform 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 Cloudera Data Platform or Success Factors record.

Observability

Monitoring

Track your Cloudera Data Platform ⇄ 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 Cloudera Data Platform and Success Factors.

How the Cloudera Data Platform and Success Factors connectors work

Cloudera Data Platform

Integration surface
JDBC/ODBC over Hive and Impala SQL endpoints, plus REST management APIs
Authentication
Kerberos, LDAP, or workload user credentials, often brokered through the Knox gateway
Change detection
Polling via SQL on timestamp or partition columns; no consumer-facing change feed
Capabilities
read · write
Rate limits
Constrained by cluster capacity and admission control rather than API 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 Cloudera Data Platform 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 Cloudera Data Platform 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
    Cloudera Data Platform connected
    Success Factors connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Cloudera Data Platform 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 · Cloudera Data Platform ⇄ 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
    Cloudera Data Platform Success Factors
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Cloudera Data Platform 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 436 integrations available for Cloudera Data Platform and Success Factors.

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