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Database ⇄ Human resources

Amazon DynamoDB to Greenhouse integration — real-time, two-way sync

Keep Amazon DynamoDB 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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Adopted by fast-scaling companies moving mission-critical data in real time

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Why teams connect Amazon DynamoDB and Greenhouse

Put your workforce data where your apps can reach it: Amazon DynamoDB and Greenhouse share the same people, positions, and org structure in real time.

Greenhouse is the system of record for the people side of the business — employees, candidates, roles, and the org structure around them. Amazon DynamoDB is where internal tools, dashboards, provisioning jobs, and analytics actually read and store records. The overlap is the workforce itself: Applications, Jobs, Offers, Scorecards in Greenhouse need to exist as queryable Global secondary indexes (GSIs), Local secondary indexes (LSIs), DynamoDB Streams, Global Tables in Amazon DynamoDB 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 Global secondary indexes (GSIs), Local secondary indexes (LSIs), DynamoDB Streams, Global Tables in Amazon DynamoDB with Applications, Jobs, Offers, Scorecards in Greenhouse field by field, in real time. You decide which system owns which fields — Greenhouse 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 Mirror a high-write DynamoDB table into a relational database so teams can join NoSQL application data against relational tables for reporting.
  • 02 Use DynamoDB Streams as a change-data-capture source to push item INSERT, MODIFY, and REMOVE events into a CRM, search index, or operational database in near-real-time.
  • 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

Org and structure stay aligned

Groups, departments, managers, and reporting lines from Greenhouse stay consistent in Amazon DynamoDB, so hierarchy-driven logic and permissions don't drift.

Computed and operational fields flow back

Values assembled or corrected in Amazon DynamoDB write onto the matching record in Greenhouse where those fields are writable, keeping the people system enriched.

Mirror people records into the database

Records maintained in Greenhouse land as queryable Global secondary indexes (GSIs), Local secondary indexes (LSIs), DynamoDB Streams, Global Tables in Amazon DynamoDB, so internal apps and dashboards read live data instead of a periodic export.

What you can sync between Amazon DynamoDB 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.

Amazon DynamoDB objects Greenhouse objects How this pairing syncs
Global secondary indexes (GSIs) Alternate key projections that let you Query by non-key attributes without a full table Scan; read-only views maintained automatically by DynamoDB. Offers Offer records tied to an Application with status and custom offer fields; readable and patchable, commonly synced to HRIS on hire. Global secondary indexes (GSIs) is specific to Amazon DynamoDB and Offers to Greenhouse — each maps to any object or custom field on the other side.
Local secondary indexes (LSIs) Extra sort keys within the same partition key, defined at table creation; queried like the base table for alternate access patterns. Scorecards Interviewer feedback and ratings tied to interviews; read-only in Harvest, exported to a warehouse for interview analytics. Local secondary indexes (LSIs) is specific to Amazon DynamoDB and Scorecards to Greenhouse — each maps to any object or custom field on the other side.
DynamoDB Streams Ordered item-level change records (INSERT, MODIFY, REMOVE) with old/new image views and 24-hour retention; the native change-data-capture source Stacksync reads for near-real-time sync. Scheduled Interviews Interview events with interviewers, times, and rooms; full create/update/delete support for calendar and reporting syncs. DynamoDB Streams is specific to Amazon DynamoDB and Scheduled Interviews to Greenhouse — each maps to any object or custom field on the other side.
Global Tables Multi-region, active-active replicas of a table kept in sync by DynamoDB; each region is read and written locally with last-writer-wins conflict resolution. Users Greenhouse users (recruiters, hiring managers); read and written, and referenced by the On-Behalf-Of header on every write. Global Tables is specific to Amazon DynamoDB and Users to Greenhouse — each maps to any object or custom field on the other side.
Time to Live (TTL) Per-item expiry timestamps; DynamoDB deletes expired items in the background and emits a Streams REMOVE record for each deletion. Departments and Offices Org structure attached to Jobs; read and written to keep reporting hierarchies aligned with an HRIS or warehouse. Time to Live (TTL) is specific to Amazon DynamoDB and Departments and Offices to Greenhouse — each maps to any object or custom field on the other side.
Tables Top-level containers, each with a partition key and optional sort key; Stacksync syncs a table as a stream of items with full read and write via PutItem, UpdateItem, and DeleteItem. Candidates Person records with contact details, tags, and custom fields; read and written via Harvest, often created from sourcing or enrichment pipelines. Tables is specific to Amazon DynamoDB and Candidates to Greenhouse — each maps to any object or custom field on the other side.

How changes propagate between Amazon DynamoDB 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.

Amazon DynamoDB Greenhouse Sub-second propagation

DetectionChanges in Amazon DynamoDB are captured at the source via change data capture — no polling loop against its API. DynamoDB Streams emit ordered item-level change records (INSERT, MODIFY, REMOVE) with KEYS_ONLY, NEW_IMAGE, OLD_IMAGE, or NEW_AND_OLD_IMAGES views.

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

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

Rate-limit considerations

  • Amazon DynamoDB: Throughput is metered in read/write capacity units (provisioned or on-demand): 1 WCU = one 1 KB write per second, 1 RCU = one strongly-consistent 4 KB read per second. Exceeding capacity or the ~3,000 RCU / 1,000 WCU per-partition ceiling returns ProvisionedThroughputExceededException with throttling.
  • 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 Amazon DynamoDB ⇄ Greenhouse

Connect Amazon DynamoDB and Greenhouse for flexible, real-time data sync.

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Amazon DynamoDB 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 Amazon DynamoDB or Greenhouse record.

Observability

Monitoring

Track your Amazon DynamoDB ⇄ Greenhouse sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Amazon DynamoDB and Greenhouse.

How the Amazon DynamoDB and Greenhouse connectors work

Amazon DynamoDB

Integration surface
AWS SDK / low-level HTTPS JSON API at dynamodb.<region>.amazonaws.com (PutItem, GetItem, UpdateItem, DeleteItem, Query, Scan, BatchWriteItem, TransactWriteItems), plus PartiQL (ExecuteStatement) for SQL-style access and DynamoDB Streams for change capture.
Authentication
AWS Signature Version 4 (SigV4) signed requests using an IAM access key ID and secret key, or temporary STS credentials from an assumed IAM role; IAM policies scope access down to table and item level.
Change detection
DynamoDB Streams emit ordered item-level change records (INSERT, MODIFY, REMOVE) with KEYS_ONLY, NEW_IMAGE, OLD_IMAGE, or NEW_AND_OLD_IMAGES views and 24-hour retention, read via shard iterators (or Kinesis Data Streams for longer retention). No native HTTP webhooks.
Capabilities
read · write · CDC
Rate limits
Throughput is metered in read/write capacity units (provisioned or on-demand): 1 WCU = one 1 KB write per second, 1 RCU = one strongly-consistent 4 KB read per second. Exceeding capacity or the ~3,000 RCU / 1,000 WCU per-partition ceiling returns ProvisionedThroughputExceededException with throttling.

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

    Choose tables

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

Amazon DynamoDB 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 538 integrations available for Amazon DynamoDB and Greenhouse.

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