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Data warehouse ⇄ Human resources

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

Keep Apache Hive 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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Why teams connect Apache Hive and Success Factors

Land the people and organization records from Success Factors in Apache Hive 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 Apache Hive 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 Apache Hive continuously, handling API limits and schema drift as they come. The connection is bi-directional, so values computed in Apache Hive, 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 Sync new date partitions incrementally instead of rescanning full tables.
  • 02 Publish Hive aggregate tables to a faster serving database for dashboards.
  • 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

HR data in the warehouse, minus the pipeline

People and organization records from Success Factors arrive in Apache Hive 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 Apache Hive for reporting the HR system cannot produce on its own.

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.

What you can sync between Apache Hive 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.

Apache Hive objects Success Factors objects How this pairing syncs
Materialized Views Precomputed results available in newer Hive versions for faster reads. Position Position Management records for headcount and requisition planning; synced with an ATS or ERP to keep positions and reqs aligned. Materialized Views is specific to Apache Hive and Position to Success Factors — each maps to any object or custom field on the other side.
ACID Tables ORC-backed transactional tables that support row-level insert, update, and delete. PerEmail and PerPhone Contact sub-entities under PerPerson; synced two-way with a directory or comms system to keep worker contact data current. ACID Tables is specific to Apache Hive and PerEmail and PerPhone to Success Factors — each maps to any object or custom field on the other side.
Metastore Catalog The schema registry other engines (Spark, Presto, Impala) also read. 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. Metastore Catalog is specific to Apache Hive and User to Success Factors — each maps to any object or custom field on the other side.
Databases Metastore namespaces that scope tables and grants. PerPerson Person-level container in Employee Central holding biographical data; effective-dated child entities (PerPersonal, PerEmail, PerPhone) hang off it. Databases is specific to Apache Hive and PerPerson to Success Factors — each maps to any object or custom field on the other side.
Managed Tables Tables whose data lifecycle Hive controls, used as warehouse destinations. EmpEmployment Employment record tying a person to an employment period (hire, service dates); carries lastModifiedDateTime used for incremental polling. Managed Tables is specific to Apache Hive and EmpEmployment to Success Factors — each maps to any object or custom field on the other side.
External Tables Tables over existing files in HDFS or object storage, read without moving data. EmpJob Effective-dated job info: position, department, manager, FTE, pay grade, cost center; the most-synced record for downstream HR and provisioning. External Tables is specific to Apache Hive and EmpJob to Success Factors — each maps to any object or custom field on the other side.

How changes propagate between Apache Hive 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.

Apache Hive Success Factors Interval-based propagation

DetectionStacksync polls Apache Hive for changes on an incremental schedule, reading only records changed since the previous pass. Polling on partition values or timestamp columns.

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

Success Factors Apache Hive 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 Apache Hive as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • Apache Hive: No API quotas; query latency reflects the batch-oriented execution engine underneath.
  • 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 Apache Hive ⇄ Success Factors

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Apache Hive 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 Apache Hive or Success Factors record.

Observability

Monitoring

Track your Apache Hive ⇄ 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 Apache Hive and Success Factors.

How the Apache Hive and Success Factors connectors work

Apache Hive

Integration surface
SQL (HiveQL) over JDBC/ODBC via HiveServer2 (Thrift)
Authentication
Deployment-dependent: Kerberos, LDAP, or username/password
Change detection
Polling on partition values or timestamp columns; no general-purpose change log for external consumers
Capabilities
read · write
Rate limits
No API quotas; query latency reflects the batch-oriented execution engine underneath

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 Apache Hive 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 Apache Hive 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
    Apache Hive connected
    Success Factors connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Apache Hive 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 · Apache Hive ⇄ 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
    Apache Hive Success Factors
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Apache Hive and Success Factors integration FAQ

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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

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