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Data warehouse ⇄ Human resources

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

Keep Apache Impala 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 Impala and Greenhouse

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

Stacksync syncs Scheduled Interviews, Users, Departments and Offices, Candidates from Greenhouse into tables in Apache Impala continuously, handling API limits and schema drift as they come. The connection is bi-directional, so values computed in Apache Impala, 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 Sync mutable reference data into Kudu tables via Impala so row-level updates are possible on the Hadoop side.
  • 02 Read new partitions incrementally from Parquet tables and land them in a cloud warehouse during migration.
  • 03 Stream Jobs, Departments, and Offices into a reporting database for recruiting funnel and time-to-fill dashboards.
  • 04 Export Scorecards and Scheduled Interviews to a data warehouse to analyze interviewer load and interview outcomes.

Common sync patterns

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 Apache Impala gives you a durable, queryable record of how Greenhouse's records change over time, for headcount planning and audit questions.

Write-back of computed values

Segments, rollups, or risk flags computed in Apache Impala sync back onto the matching records in Greenhouse, where the HR team sees them in the system they already use.

What you can sync between Apache Impala 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 Impala objects Greenhouse objects How this pairing syncs
Kudu Tables Kudu-backed tables that support row-level insert, update, upsert, and delete. Candidates Person records with contact details, tags, and custom fields; read and written via Harvest, often created from sourcing or enrichment pipelines. Kudu Tables is specific to Apache Impala and Candidates to Greenhouse — each maps to any object or custom field on the other side.
External Tables Tables over files loaded by other tools, queryable without data movement. Applications Links a Candidate to a Job; carries current stage, status, and source. Movable, rejectable, and hireable through Harvest write calls. External Tables is specific to Apache Impala and Applications to Greenhouse — each maps to any object or custom field on the other side.
Users and Roles Principals (often via Ranger/Sentry) used to grant scoped read access. Jobs Requisitions with openings, hiring team, departments, and offices; created and patched via Harvest, read out for funnel and time-to-fill reporting. Users and Roles is specific to Apache Impala and Jobs to Greenhouse — each maps to any object or custom field on the other side.
Databases Namespaces shared with the Hive Metastore that scope tables. Offers Offer records tied to an Application with status and custom offer fields; readable and patchable, commonly synced to HRIS on hire. Databases is specific to Apache Impala and Offers to Greenhouse — each maps to any object or custom field on the other side.
Tables HDFS or object-storage backed tables (commonly Parquet) read at interactive speed. Scorecards Interviewer feedback and ratings tied to interviews; read-only in Harvest, exported to a warehouse for interview analytics. Tables is specific to Apache Impala and Scorecards to Greenhouse — each maps to any object or custom field on the other side.
Partitions Partition values used to limit scans and drive incremental reads. Scheduled Interviews Interview events with interviewers, times, and rooms; full create/update/delete support for calendar and reporting syncs. Partitions is specific to Apache Impala and Scheduled Interviews to Greenhouse — each maps to any object or custom field on the other side.

How changes propagate between Apache Impala 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 Impala Greenhouse Interval-based propagation

DetectionStacksync polls Apache Impala for changes on an incremental schedule, reading only records changed since the previous pass. Polling on partition or timestamp columns.

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

Greenhouse Apache Impala 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 Impala as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • Apache Impala: No API quotas; concurrency is bounded by cluster resources and admission control settings.
  • 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 Impala ⇄ Greenhouse

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Apache Impala 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 Impala or Greenhouse record.

Observability

Monitoring

Track your Apache Impala ⇄ 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 Impala and Greenhouse.

How the Apache Impala and Greenhouse connectors work

Apache Impala

Integration surface
SQL over JDBC/ODBC (HiveServer2-compatible protocol)
Authentication
Deployment-dependent: Kerberos, LDAP, or username/password
Change detection
Polling on partition or timestamp columns; no change log exposed for external consumers
Capabilities
read · write
Rate limits
No API quotas; concurrency is bounded by cluster resources and admission control settings

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

    Choose tables

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

Apache Impala and Greenhouse integration FAQ

SECURITY

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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 435 integrations available for Apache Impala and Greenhouse.

Popular · 8 of 435
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