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Business productivity ⇄ Developer tools

Gatekeeper to Jms integration — real-time, two-way sync

Keep Gatekeeper and Jms 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 Gatekeeper and Jms

Connect the tickets, records, and events in Gatekeeper to the systems engineering runs in Jms, kept current in real time and in both directions.

Gatekeeper is where customer-facing and operational work happens: the tickets, conversations, contacts, and records a team touches every day. Jms is where engineering runs the systems behind that work — the issue trackers, message brokers, directories, and monitors that keep services moving. The same items, people, and events matter to both, and when the only link between them is a manual hand-off or an overnight export, each side acts on a stale copy of what the other already knows.

Stacksync syncs Vendors (Suppliers), Files, Workflow form data, Custom data groups in Gatekeeper with MapMessage, BytesMessage, Durable Subscription, Message headers and properties in Jms field by field, in real time, and in both directions. You decide which system owns which fields; Stacksync maps the overlap, resolves conflicts by rules you set, and keeps every copy current, with no middleware to build and no API limits to babysit.

Common use cases

  • 01 Stream workflow-form data - intake requests, vendor onboarding, risk assessments - into an operational database for cycle-time and compliance reporting.
  • 02 Write contract or vendor records into Gatekeeper when a deal closes in the CRM to kick off downstream procurement and legal workflows.
  • 03 Route messages by JMSType or application property using a message selector, landing high-priority messages in one table and the rest in another.
  • 04 Consume order or event messages from a JMS Queue and upsert them as rows into Postgres or a warehouse so downstream apps read them in plain SQL.

Common sync patterns

Where Jms watches operations: live volumes for visibility

Open items, throughput, and error counts from Gatekeeper replicate into Jms, so the operational view reflects what is actually happening in the business.

One shared record across both systems

Where both systems keep records for the same contacts, items, or users, a correction in either updates the other, ending dual maintenance and keeping IDs aligned for every other flow.

Where Jms tracks engineering work: requests become issues

A ticket or request raised in Gatekeeper opens or updates a matching issue in Jms, and status, comments, and resolution flow back, so support and engineering work the same item instead of retyping it.

What you can sync between Gatekeeper and Jms

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.

Gatekeeper objects Jms objects How this pairing syncs
Vendors (Suppliers) Company records for counterparties and suppliers with onboarding status, compliance, risk, contacts, and spend; read and written to keep vendor master data aligned with a CRM or ERP. Queue Point-to-point destination where each message is delivered to exactly one consumer. Stacksync consumes messages to load into a database, or publishes messages for a downstream Java service to process. Vendors (Suppliers) is specific to Gatekeeper and Queue to Jms — each maps to any object or custom field on the other side.
Files Document files attached to contracts and vendors - executed PDFs, certificates, and compliance evidence; read to pull signed files and evidence out, or written to push generated documents in. Topic Publish/subscribe destination that fans each message out to every active subscriber. Stacksync subscribes to event streams or publishes records so multiple services react. Files is specific to Gatekeeper and Topic to Jms — each maps to any object or custom field on the other side.
Workflow form data The structured data captured on Gatekeeper workflow forms (intake requests, vendor onboarding, risk assessments); exposed by the API since 2025 so form results sync into an operational database, not only contract and vendor records. TextMessage Most common body type, carrying a String that is usually JSON or XML. Deserialized into rows/records on read and serialized from source records on write. Workflow form data is specific to Gatekeeper and TextMessage to Jms — each maps to any object or custom field on the other side.
Custom data groups Customer-configured custom fields and data groups; because the JSON:API and its docs are dynamic, any custom data added in Configuration exposes the same read/write endpoints as the standard objects and syncs the same way. MapMessage Body of typed name/value pairs that maps directly onto record fields, so no custom body parsing is needed on read or write. Custom data groups is specific to Gatekeeper and MapMessage to Jms — each maps to any object or custom field on the other side.
Users Gatekeeper user and team records governed by role-based access; read to map contract and vendor owners, approvers, and internal contacts to CRM or HR records. BytesMessage Raw binary body for files, protobuf, or opaque payloads; passed through as a byte stream when structured field mapping is not required. Users is specific to Gatekeeper and BytesMessage to Jms — each maps to any object or custom field on the other side.
Categories The classification taxonomy applied to contracts and vendors (type, department, business unit); synced so categorization stays consistent between Gatekeeper and downstream reporting or ERP dimensions. Durable Subscription Named topic subscription that retains messages while the consumer is offline, so a sync that disconnects does not miss events published in the meantime. Categories is specific to Gatekeeper and Durable Subscription to Jms — each maps to any object or custom field on the other side.

How changes propagate between Gatekeeper and Jms

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.

Gatekeeper Jms Interval-based propagation

DetectionStacksync polls Gatekeeper for changes on an incremental schedule, reading only records changed since the previous pass. No native developer webhook subscription API and no database change-data-capture log.

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

Jms Gatekeeper Sub-second propagation

DetectionJms notifies Stacksync of record changes through webhook events. Asynchronous push — the broker delivers messages to registered consumers (MessageListener.onMessage) in real time, with no polling.

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

Rate-limit considerations

  • Gatekeeper: Gatekeeper publishes no fixed public per-minute request quota; throughput is governed per key by its endpoint permissions, and every call is recorded (parameters, payload, response) under API Logs for monitoring. Pace bulk writes and use JSON:API pagination on list endpoints.
  • Jms: JMS defines no protocol-level rate limits; throughput ceilings, consumer prefetch, and producer flow control are configured on the broker (ActiveMQ, IBM MQ, Solace, TIBCO EMS, etc.).
What ships with Gatekeeper ⇄ Jms

Connect Gatekeeper and Jms for flexible, real-time data sync.

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

Real-time

Two-way sync

Changes in Gatekeeper or Jms instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Gatekeeper or Jms 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 Gatekeeper or Jms record.

Observability

Monitoring

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

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Gatekeeper and Jms.

How the Gatekeeper and Jms connectors work

Gatekeeper

Integration surface
RESTful API following the JSON:API specification, tenant-scoped with interactive docs at {tenant}.gatekeeperhq.com/api_docs and a published Postman collection. The API is dynamic: it exposes the standard Contract and Vendor objects plus any custom data groups and workflow-form data configured in the tenant.
Authentication
API keys created and managed under Configuration > API Keys and passed as a token; each key carries granular per-endpoint permissions set to read-only or write, so access is scoped per object. Multiple keys can be issued and revoked independently.
Change detection
No native developer webhook subscription API and no database change-data-capture log; detect changes by polling the JSON:API list endpoints filtered and sorted on updated-at timestamps. Gatekeeper's own event automation - Workflow Engine phase transitions and Interconnect process orchestration - runs inside the platform rather than as a subscribable webhook stream.
Capabilities
read · write
Rate limits
Gatekeeper publishes no fixed public per-minute request quota; throughput is governed per key by its endpoint permissions, and every call is recorded (parameters, payload, response) under API Logs for monitoring. Pace bulk writes and use JSON:API pagination on list endpoints.

Jms

Integration surface
JMS / Jakarta Messaging API (classic API and simplified JMSContext) over provider transports such as OpenWire, AMQP, IBM MQ, or STOMP
Authentication
Username/password credentials passed to ConnectionFactory.createConnection(); ConnectionFactory and Destinations resolved via JNDI. Transport security (TLS) and stronger auth (SASL, JAAS, client certificates) are broker-implementation-specific.
Change detection
Asynchronous push — the broker delivers messages to registered consumers (MessageListener.onMessage) in real time, with no polling. Message selectors (an SQL-92 subset over headers/properties) filter delivery. There is no modified-date polling or CDC replay, and queue consumption is destructive.
Capabilities
read · write · webhooks
Rate limits
JMS defines no protocol-level rate limits; throughput ceilings, consumer prefetch, and producer flow control are configured on the broker (ActiveMQ, IBM MQ, Solace, TIBCO EMS, etc.).
How it works

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

    Choose tables

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

Gatekeeper and Jms 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.

Learn more about security
SOC 2 Type II
ISO 27001
HIPAA BAA
GDPR
CCPA
DPF US-EU-UK-CH
→ SECURITY WITH BENEFITS

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 286 integrations available for Gatekeeper and Jms.

Popular · 7 of 286
All Gatekeeper integrations All Jms integrations Browse all integrations
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