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Jms to Neo4j integration — real-time, two-way sync

Keep Jms and Neo4j 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 Jms and Neo4j

Keep Neo4j and Jms in step: the rows in your database and the MapMessage, BytesMessage, Durable Subscription, Message headers and properties your engineering tools track stay consistent in real time, in both directions.

Neo4j is where your application's durable data lives; Jms is where engineering and operations teams track the issues, events, messages, or identities that run alongside it. The two overlap constantly, a row should open a ticket, an alert should land as a record, a user in one should exist in the other, but bridging them today means per-tool integration code: auth, webhooks, pagination, rate limits, and retries, built and maintained separately for every tool.

Stacksync syncs Users & Roles, Nodes, Relationships, Properties in Neo4j 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 keeps every copy consistent and resolves conflicts by rules you set, so the database and the tooling around it never drift apart.

Common use cases

  • 01 Keep a customer-360 graph continuously updated from ERP, CRM, and support sources.
  • 02 Mirror CRM accounts and contacts into a graph to model buying-group and referral relationships.
  • 03 Bridge a legacy IBM MQ or ActiveMQ Queue to a SaaS system of record by consuming each message and writing the record through the SaaS API.
  • 04 Fan out inventory or pricing updates onto a Topic with durable subscriptions so multiple services stay aligned even after downtime.

Common sync patterns

Land tool activity as queryable rows

Records and events from Jms arrive in Neo4j as rows, so tickets, alerts, messages, or identity changes become joinable data your services and reports read directly.

One integration pattern instead of per-tool API code

Read and write the synced tables in Neo4j and Stacksync keeps Jms current, replacing the auth, webhooks, rate limits, and retry logic you would otherwise maintain for each tool.

React to changes on either side in near real time

Updates in Jms arrive as row changes in Neo4j, and writes to Neo4j propagate to Jms within seconds, so triggers, jobs, and alerts fire without polling.

What you can sync between Jms and Neo4j

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.

Jms objects Neo4j objects How this pairing syncs
BytesMessage Raw binary body for files, protobuf, or opaque payloads; passed through as a byte stream when structured field mapping is not required. Users & Roles Security principals controlling what an integration credential can query or modify. BytesMessage is specific to Jms and Users & Roles to Neo4j — each maps to any object or custom field on the other side.
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. Nodes Entity records (customers, products, accounts) written from source systems as labeled nodes. Durable Subscription is specific to Jms and Nodes to Neo4j — each maps to any object or custom field on the other side.
Message headers and properties JMSCorrelationID, JMSReplyTo, JMSTimestamp, JMSType plus JMSX/application-defined properties; used for correlation, routing, and selector-based filtering. Relationships Typed, directed edges that carry the connections syncs exist to model. Message headers and properties is specific to Jms and Relationships to Neo4j — each maps to any object or custom field on the other side.
Dead Letter Queue Provider-managed destination (e.g. ActiveMQ.DLQ, IBM MQ dead-letter queue) where messages exceeding redelivery limits land; read to reconcile failed deliveries. Properties Key-value attributes on both nodes and relationships, mapped from source fields. Dead Letter Queue is specific to Jms and Properties to Neo4j — each maps to any object or custom field on the other side.
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. Labels Node type markers used to map source tables or objects onto the graph. Queue is specific to Jms and Labels to Neo4j — each maps to any object or custom field on the other side.
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. Indexes & Constraints Uniqueness constraints and indexes that make MERGE-based upserts reliable and fast. Topic is specific to Jms and Indexes & Constraints to Neo4j — each maps to any object or custom field on the other side.

How changes propagate between Jms and Neo4j

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.

Jms Neo4j 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 Neo4j through its API, with automatic retries and rate-limit backoff.

Neo4j Jms Sub-second propagation

DetectionChanges in Neo4j are captured at the source via change data capture — no polling loop against its API. Neo4j Change Data Capture on Enterprise and Aura streams graph changes.

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

Rate-limit considerations

  • 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 Jms ⇄ Neo4j

Connect Jms and Neo4j for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Jms–Neo4j connection.

Real-time

Two-way sync

Changes in Jms or Neo4j instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Jms or Neo4j 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 Jms or Neo4j record.

Observability

Monitoring

Track your Jms ⇄ Neo4j sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Jms and Neo4j.

How the Jms and Neo4j connectors work

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.).

Neo4j

Integration surface
Bolt binary protocol with Cypher via official drivers, plus an HTTP query API
Authentication
Username/password (basic auth); enterprise deployments add SSO options
Change detection
Neo4j Change Data Capture on Enterprise and Aura streams graph changes; otherwise Cypher polling on timestamp properties
Capabilities
read · write · CDC
How it works

How to connect Jms to Neo4j — 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 Jms and Neo4j 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
    Jms connected
    Neo4j connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Jms and Neo4j 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 · Jms ⇄ Neo4j
    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
    Jms Neo4j
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Jms and Neo4j 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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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 396 integrations available for Jms and Neo4j.

Popular · 8 of 396
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