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Data warehouse ⇄ Human resources

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

Keep Apache Hive 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 Apache Hive and Greenhouse

Land the people and organization records from Greenhouse in Apache Hive 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 Apache Hive next to everything else the company measures.

Stacksync syncs Candidates, Applications, Jobs, Offers from Greenhouse 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 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 Load records from CRMs and databases into partitioned Hive tables for long-term analytical storage.
  • 02 Sync new date partitions incrementally instead of rescanning full tables.
  • 03 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.
  • 04 Write enriched or sourced Candidates from external tools into Greenhouse and keep contact fields refreshed as data changes.

Common sync patterns

HR data in the warehouse, minus the pipeline

People and organization records from Greenhouse 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 Greenhouse'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 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.

Apache Hive objects Greenhouse objects How this pairing syncs
Views Logical views readable as modeled sources. Users Greenhouse users (recruiters, hiring managers); read and written, and referenced by the On-Behalf-Of header on every write. Views is specific to Apache Hive and Users to Greenhouse — each maps to any object or custom field on the other side.
Materialized Views Precomputed results available in newer Hive versions for faster reads. Departments and Offices Org structure attached to Jobs; read and written to keep reporting hierarchies aligned with an HRIS or warehouse. Materialized Views is specific to Apache Hive and Departments and Offices to Greenhouse — 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. Candidates Person records with contact details, tags, and custom fields; read and written via Harvest, often created from sourcing or enrichment pipelines. ACID Tables is specific to Apache Hive and Candidates to Greenhouse — each maps to any object or custom field on the other side.
Metastore Catalog The schema registry other engines (Spark, Presto, Impala) also read. Applications Links a Candidate to a Job; carries current stage, status, and source. Movable, rejectable, and hireable through Harvest write calls. Metastore Catalog is specific to Apache Hive and Applications to Greenhouse — each maps to any object or custom field on the other side.
Databases Metastore namespaces that scope tables and grants. Jobs Requisitions with openings, hiring team, departments, and offices; created and patched via Harvest, read out for funnel and time-to-fill reporting. Databases is specific to Apache Hive and Jobs to Greenhouse — each maps to any object or custom field on the other side.
Managed Tables Tables whose data lifecycle Hive controls, used as warehouse destinations. Offers Offer records tied to an Application with status and custom offer fields; readable and patchable, commonly synced to HRIS on hire. Managed Tables is specific to Apache Hive and Offers to Greenhouse — each maps to any object or custom field on the other side.

How changes propagate between Apache Hive 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.

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

Greenhouse Apache Hive 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 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.
  • 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 Apache Hive ⇄ Greenhouse

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Apache Hive 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 Apache Hive or Greenhouse record.

Observability

Monitoring

Track your Apache Hive ⇄ Greenhouse 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 Greenhouse.

How the Apache Hive and Greenhouse 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

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

    Choose tables

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

Apache Hive and Greenhouse 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

Every pair below is a real-time, two-way sync. Search all 440 integrations available for Apache Hive and Greenhouse.

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