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Database ⇄ Human resources

AWS Aurora MySQL to Success Factors integration — real-time, two-way sync

Keep AWS Aurora MySQL 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 AWS Aurora MySQL and Success Factors

Put your workforce data where your apps can reach it: AWS Aurora MySQL and Success Factors share the same people, positions, and org structure in real time.

Success Factors is the system of record for the people side of the business — employees, candidates, roles, and the org structure around them. AWS Aurora MySQL is where internal tools, dashboards, provisioning jobs, and analytics actually read and store records. The overlap is the workforce itself: Foundation Objects (FODepartment, FOLocation, FOCostCenter), Position, PerEmail and PerPhone, User in Success Factors need to exist as queryable Stored procedures and triggers, Databases (schemas), Tables, Rows in AWS Aurora MySQL before an app can act on them. When that bridge is a nightly export or a hand-run CSV, every downstream system spends the day working from a roster that has already moved on.

Stacksync syncs Stored procedures and triggers, Databases (schemas), Tables, Rows in AWS Aurora MySQL with Foundation Objects (FODepartment, FOLocation, FOCostCenter), Position, PerEmail and PerPhone, User in Success Factors field by field, in real time. You decide which system owns which fields — Success Factors typically owns identity and org attributes, while operational or computed values can flow back the other way — and Stacksync keeps every copy consistent, matching records on a stable key and resolving conflicts by rules you set.

The result is one live picture of the workforce on both sides: HR keeps its source of truth, and the database keeps a current mirror that internal apps, reports, and access controls can trust without a batch window in between.

Common use cases

  • 01 Let operations teams edit records in a spreadsheet-style tool with changes written back to Aurora safely.
  • 02 Give backend services read and write access to ERP or billing data by syncing it into Aurora tables the application already queries.
  • 03 Write updated PerEmail, PerPhone, and User attributes back into SuccessFactors from an identity or directory system so contact data stays current.
  • 04 Two-way sync of User and EmpJob (position, department, manager, cost center) into Postgres so provisioning, internal apps, and analytics read current org data in SQL while HR edits in SuccessFactors.

Common sync patterns

Computed and operational fields flow back

Values assembled or corrected in AWS Aurora MySQL write onto the matching record in Success Factors where those fields are writable, keeping the people system enriched.

Mirror people records into the database

Records maintained in Success Factors land as queryable Stored procedures and triggers, Databases (schemas), Tables, Rows in AWS Aurora MySQL, so internal apps and dashboards read live data instead of a periodic export.

One directory of record

When a person record is added, changed, or deactivated in either system, the matching row in the other stays current, ending dual maintenance.

What you can sync between AWS Aurora MySQL 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.

AWS Aurora MySQL objects Success Factors objects How this pairing syncs
Tables The primary sync unit; each table maps one-to-one to a table or object in the paired system. 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. Tables is specific to AWS Aurora MySQL and User to Success Factors — each maps to any object or custom field on the other side.
Rows Inserted, updated, and deleted individually or in bulk during two-way syncs. PerPerson Person-level container in Employee Central holding biographical data; effective-dated child entities (PerPersonal, PerEmail, PerPhone) hang off it. Rows is specific to AWS Aurora MySQL and PerPerson to Success Factors — each maps to any object or custom field on the other side.
Columns MySQL data types are mapped to the paired system's field types during schema setup. EmpEmployment Employment record tying a person to an employment period (hire, service dates); carries lastModifiedDateTime used for incremental polling. Columns is specific to AWS Aurora MySQL and EmpEmployment to Success Factors — each maps to any object or custom field on the other side.
Primary keys and indexes Used to match rows across systems and keep incremental syncs efficient. EmpJob Effective-dated job info: position, department, manager, FTE, pay grade, cost center; the most-synced record for downstream HR and provisioning. Primary keys and indexes is specific to AWS Aurora MySQL and EmpJob to Success Factors — each maps to any object or custom field on the other side.
Views Can serve as read-only sync sources for derived or filtered datasets. EmpCompensation Effective-dated pay and compensation; usually read into a warehouse for reporting, writable for comp updates as new dated slices. Views is specific to AWS Aurora MySQL and EmpCompensation to Success Factors — each maps to any object or custom field on the other side.
Foreign keys Express relationships that syncs preserve when mapping to related objects elsewhere. 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. Foreign keys is specific to AWS Aurora MySQL and Foundation Objects (FODepartment, FOLocation, FOCostCenter) to Success Factors — each maps to any object or custom field on the other side.

How changes propagate between AWS Aurora MySQL 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.

AWS Aurora MySQL Success Factors Sub-second propagation

DetectionChanges in AWS Aurora MySQL are captured at the source via change data capture — no polling loop against its API. Log-based CDC via the MySQL binary log (binlog), with polling on timestamp columns as a fallback.

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

Success Factors AWS Aurora MySQL 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 AWS Aurora MySQL as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • 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 AWS Aurora MySQL ⇄ Success Factors

Connect AWS Aurora MySQL and Success Factors for flexible, real-time data sync.

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever AWS Aurora MySQL 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 AWS Aurora MySQL or Success Factors record.

Observability

Monitoring

Track your AWS Aurora MySQL ⇄ 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 AWS Aurora MySQL and Success Factors.

How the AWS Aurora MySQL and Success Factors connectors work

AWS Aurora MySQL

Integration surface
SQL wire protocol (MySQL-compatible), standard MySQL drivers and JDBC
Authentication
Database credentials, optionally AWS IAM database authentication, over TLS
Change detection
Log-based CDC via the MySQL binary log (binlog), with polling on timestamp columns as a fallback
Capabilities
read · write · CDC

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 AWS Aurora MySQL 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 AWS Aurora MySQL 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
    AWS Aurora MySQL connected
    Success Factors connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the AWS Aurora MySQL 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 · AWS Aurora MySQL ⇄ 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
    AWS Aurora MySQL Success Factors
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

AWS Aurora MySQL 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 450 integrations available for AWS Aurora MySQL and Success Factors.

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