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Data warehouse ⇄ Human resources

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

Keep Cloudera Data Platform and Greenhouse 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 Cloudera Data Platform and Greenhouse

Land the people and organization records from Greenhouse in Cloudera Data Platform as live tables for workforce reporting, without extract jobs, and write computed results back where Greenhouse 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 Greenhouse 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 Scheduled Interviews, Users, Departments and Offices, Candidates from Greenhouse 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 Greenhouse 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 Export Scorecards and Scheduled Interviews to a data warehouse to analyze interviewer load and interview outcomes.
  • 04 Two-way sync Candidates and Applications with Postgres so recruiting-ops apps read and update stage, status, and custom fields in SQL while recruiters stay in Greenhouse.

Common sync patterns

Headcount and cost joined with everything else

Analysts combine Greenhouse's workforce records with finance, product, or operational data already in Cloudera Data Platform 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.

Queryable history for planning and audit

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

What you can sync between Cloudera Data Platform and Greenhouse

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 Greenhouse objects How this pairing syncs
Object store / HDFS files Underlying Parquet or ORC files on HDFS or cloud storage backing the tables. Offers Offer records tied to an Application with status and custom offer fields; readable and patchable, commonly synced to HRIS on hire. Object store / HDFS files is specific to Cloudera Data Platform and Offers to Greenhouse — each maps to any object or custom field on the other side.
Databases Logical namespaces in the shared Hive Metastore that group tables for access control and syncs. Scorecards Interviewer feedback and ratings tied to interviews; read-only in Harvest, exported to a warehouse for interview analytics. Databases is specific to Cloudera Data Platform and Scorecards to Greenhouse — each maps to any object or custom field on the other side.
Hive tables Warehouse tables queried over JDBC/ODBC; classic managed tables are append-oriented. Scheduled Interviews Interview events with interviewers, times, and rooms; full create/update/delete support for calendar and reporting syncs. Hive tables is specific to Cloudera Data Platform and Scheduled Interviews to Greenhouse — 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. Users Greenhouse users (recruiters, hiring managers); read and written, and referenced by the On-Behalf-Of header on every write. Impala tables is specific to Cloudera Data Platform and Users to Greenhouse — 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. Departments and Offices Org structure attached to Jobs; read and written to keep reporting hierarchies aligned with an HRIS or warehouse. Kudu tables is specific to Cloudera Data Platform and Departments and Offices to Greenhouse — 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. Candidates Person records with contact details, tags, and custom fields; read and written via Harvest, often created from sourcing or enrichment pipelines. Iceberg tables is specific to Cloudera Data Platform and Candidates to Greenhouse — each maps to any object or custom field on the other side.

How changes propagate between Cloudera Data Platform and Greenhouse

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

Greenhouse Cloudera Data Platform Sub-second propagation

DetectionGreenhouse notifies Stacksync of record changes through webhook events. HMAC-SHA256 signed webhooks for candidate, application, job, and interview events, plus polling list endpoints with created_after / updated_after /.

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.
  • Greenhouse: Harvest enforces a per-integration limit over a rolling 10-second window (X-RateLimit-Limit, commonly 50 requests / 10s for approved integrations); responses carry X-RateLimit-Remaining and, on a 429, X-RateLimit-Reset and Retry-After.
What ships with Cloudera Data Platform ⇄ Greenhouse

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

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

Real-time

Two-way sync

Changes in Cloudera Data Platform or Greenhouse 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 Greenhouse 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 Greenhouse record.

Observability

Monitoring

Track your Cloudera Data Platform ⇄ Greenhouse 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 Greenhouse.

How the Cloudera Data Platform and Greenhouse 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

Greenhouse

Integration surface
Harvest REST API (plus read-only Job Board API and the Ingestion API for bulk candidate import)
Authentication
HTTP Basic Auth with a Harvest API key (key as username, blank password, colon appended then Base64-encoded); write calls require an On-Behalf-Of header naming the Greenhouse user
Change detection
HMAC-SHA256 signed webhooks for candidate, application, job, and interview events, plus polling list endpoints with created_after / updated_after / last_activity_after filters
Capabilities
read · write · webhooks
Rate limits
Harvest enforces a per-integration limit over a rolling 10-second window (X-RateLimit-Limit, commonly 50 requests / 10s for approved integrations); responses carry X-RateLimit-Remaining and, on a 429, X-RateLimit-Reset and Retry-After.
Greenhouse setup guide
How it works

How to connect Cloudera Data Platform to Greenhouse — 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 Greenhouse 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
    Greenhouse connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Cloudera Data Platform and Greenhouse 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 ⇄ Greenhouse
    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 Greenhouse
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Cloudera Data Platform and Greenhouse 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 Greenhouse.

Popular · 8 of 436
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