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Database ⇄ Human resources

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

Keep AWS Aurora MySQL and BambooHR 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 AWS Aurora MySQL and BambooHR

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

BambooHR 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: Employment Status, Departments and Divisions, Custom Tables, Reports in BambooHR need to exist as queryable Tables, Rows, Columns, Primary keys and indexes 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 Tables, Rows, Columns, Primary keys and indexes in AWS Aurora MySQL with Employment Status, Departments and Divisions, Custom Tables, Reports in BambooHR field by field, in real time. You decide which system owns which fields — BambooHR 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 Give backend services read and write access to ERP or billing data by syncing it into Aurora tables the application already queries.
  • 02 Stream row changes from Aurora into SaaS tools via binlog CDC instead of scheduled batch exports.
  • 03 Sync Employees and Employment Status to identity and IT systems so new hires are provisioned and departures deactivated automatically.
  • 04 Read Job Information and Departments into an ERP or planning tool so headcount, cost centers, and org structure stay current.

Common sync patterns

Mirror people records into the database

Records maintained in BambooHR land as queryable Tables, Rows, Columns, Primary keys and indexes 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.

Reporting and analytics on current data

Employment Status, Departments and Divisions, Custom Tables, Reports replicate into AWS Aurora MySQL where they join operational tables, so headcount, roles, and status reports run on the live state without manual pulls.

What you can sync between AWS Aurora MySQL and BambooHR

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 BambooHR 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. Employment Status Effective-dated hire, leave, and termination status; drives automated provisioning and deprovisioning in downstream identity and license systems. Tables is specific to AWS Aurora MySQL and Employment Status to BambooHR — 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. Departments and Divisions Org-structure list values; synced to keep cost centers and team groupings consistent across ERP, identity, and analytics systems. Rows is specific to AWS Aurora MySQL and Departments and Divisions to BambooHR — 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. Custom Tables Company-specific effective-dated tables (assets, training, certifications); rows read and written so custom HR data isn't trapped in the HRIS. Columns is specific to AWS Aurora MySQL and Custom Tables to BambooHR — 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. Reports Saved company reports retrievable via API; read out on a schedule to feed a warehouse without rebuilding field-by-field queries. Primary keys and indexes is specific to AWS Aurora MySQL and Reports to BambooHR — 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. Employees The core person record with personal and work fields; read out to identity, payroll, and IT systems, and written back from recruiting or onboarding tools. Views is specific to AWS Aurora MySQL and Employees to BambooHR — 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. Job Information Effective-dated job title, department, division, location, and reporting line; synced so org charts and provisioning systems track internal moves. Foreign keys is specific to AWS Aurora MySQL and Job Information to BambooHR — each maps to any object or custom field on the other side.

How changes propagate between AWS Aurora MySQL and BambooHR

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

BambooHR AWS Aurora MySQL Sub-second propagation

DetectionBambooHR notifies Stacksync of record changes through webhook events. The Get Updated Employee IDs endpoint (last-changed timestamps) returns employees inserted, updated, or deleted since a cursor for efficient polling.

DeliveryEach detected change is applied to AWS Aurora MySQL as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • BambooHR: Requests are rate-limited per API key with standard REST throttling (429 on excess); bulk reads are best done through saved Reports or the updated-IDs delta endpoint rather than per-employee calls.
What ships with AWS Aurora MySQL ⇄ BambooHR

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

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

Real-time

Two-way sync

Changes in AWS Aurora MySQL or BambooHR 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 BambooHR 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 BambooHR record.

Observability

Monitoring

Track your AWS Aurora MySQL ⇄ BambooHR 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 BambooHR.

How the AWS Aurora MySQL and BambooHR 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

BambooHR

Integration surface
BambooHR API (REST, per-company subdomain)
Authentication
API key per user over HTTP Basic auth (key as username), scoped to that user's permission level in BambooHR; OAuth/OpenID available for SSO-enabled apps
Change detection
The Get Updated Employee IDs endpoint (last-changed timestamps) returns employees inserted, updated, or deleted since a cursor for efficient polling; webhooks can fire on monitored field changes
Capabilities
read · write · webhooks
Rate limits
Requests are rate-limited per API key with standard REST throttling (429 on excess); bulk reads are best done through saved Reports or the updated-IDs delta endpoint rather than per-employee calls.
How it works

How to connect AWS Aurora MySQL to BambooHR — 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 BambooHR 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
    BambooHR connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the AWS Aurora MySQL and BambooHR 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 ⇄ BambooHR
    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 BambooHR
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

AWS Aurora MySQL and BambooHR 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 358 integrations available for AWS Aurora MySQL and BambooHR.

Popular · 7 of 358
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