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Human resources ⇄ Data warehouse

BambooHR to Databricks integration — real-time, two-way sync

Keep BambooHR and Databricks 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 BambooHR and Databricks

Land the people and organization records from BambooHR in Databricks as live tables for workforce reporting, without extract jobs, and write computed results back where BambooHR 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 BambooHR 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 Custom Tables, Reports, Employees, Job Information from BambooHR 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 BambooHR 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 Read Job Information and Departments into an ERP or planning tool so headcount, cost centers, and org structure stay current.
  • 04 Push approved Time Off into capacity and scheduling systems so project plans reflect real availability.

Common sync patterns

Queryable history for planning and audit

A continuously synced copy in Databricks gives you a durable, queryable record of how BambooHR's records change over time, for headcount planning and audit questions.

Write-back of computed values

Segments, rollups, or risk flags computed in Databricks sync back onto the matching records in BambooHR, 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 BambooHR arrive in Databricks as queryable tables, current within seconds instead of a day behind, replacing hand-built extract jobs.

What you can sync between BambooHR and Databricks

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.

BambooHR objects Databricks objects How this pairing syncs
Departments and Divisions Org-structure list values; synced to keep cost centers and team groupings consistent across ERP, identity, and analytics systems. SQL Warehouses The compute endpoint a sync connects to for query execution. Departments and Divisions is specific to BambooHR and SQL Warehouses to Databricks — each maps to any object or custom field on the other side.
Custom Tables Company-specific effective-dated tables (assets, training, certifications); rows read and written so custom HR data isn't trapped in the HRIS. Change Data Feed Row-level change records on Delta tables that drive incremental reads. Custom Tables is specific to BambooHR and Change Data Feed to Databricks — each maps to any object or custom field on the other side.
Reports Saved company reports retrievable via API; read out on a schedule to feed a warehouse without rebuilding field-by-field queries. Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. Reports is specific to BambooHR and Catalogs to Databricks — each maps to any object or custom field on the other side.
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. Schemas Group tables and views; syncs typically target a dedicated schema per source system. Employees is specific to BambooHR and Schemas to Databricks — each maps to any object or custom field on the other side.
Job Information Effective-dated job title, department, division, location, and reporting line; synced so org charts and provisioning systems track internal moves. Delta Tables The primary read and write target; operational data lands here as managed or external tables. Job Information is specific to BambooHR and Delta Tables to Databricks — each maps to any object or custom field on the other side.
Compensation Effective-dated pay rate, schedule, and change reason; read into planning and payroll systems with access restricted to authorized fields. Views Curated read-only projections used as sync sources for downstream tools. Compensation is specific to BambooHR and Views to Databricks — each maps to any object or custom field on the other side.

How changes propagate between BambooHR and Databricks

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.

BambooHR Databricks 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 Databricks as a row-level write, with types converted between the two schemas.

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

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.
  • Databricks: Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits.
What ships with BambooHR ⇄ Databricks

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever BambooHR or Databricks 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 BambooHR or Databricks record.

Observability

Monitoring

Track your BambooHR ⇄ Databricks sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between BambooHR and Databricks.

How the BambooHR and Databricks connectors work

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.

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
How it works

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

    Choose tables

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

BambooHR and Databricks 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 458 integrations available for BambooHR and Databricks.

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