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Developer tools ⇄ Data warehouse

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

Keep Jms and MotherDuck 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 Jms and MotherDuck

Close the gap between analytics and operations: MotherDuck holds the record while Jms runs the day-to-day work, and Stacksync keeps the two in step in real time, in both directions.

MotherDuck is the central store where teams keep Database Shares, Attached Local DuckDB Databases, Databases, Schemas for reporting and analysis; Jms runs the operational side of engineering work — tracking issues, moving messages and events, watching systems, and managing users and access. The two overlap wherever the same operational data matters to both: the Message headers and properties, Dead Letter Queue, Queue, Topic produced in Jms are exactly what analysts want to measure in MotherDuck, and the curated rows in MotherDuck are what should drive the next action in Jms. When that overlap is bridged by nightly ETL or hand-written scripts, dashboards lag a day behind reality and the tools that should react to warehouse signals never see them.

Stacksync syncs Database Shares, Attached Local DuckDB Databases, Databases, Schemas in MotherDuck with Message headers and properties, Dead Letter Queue, Queue, Topic in Jms field by field, in real time, and in both directions. You decide which system owns which fields; Stacksync matches records on a stable external key, keeps every copy consistent, and resolves conflicts by rules you set — so analytics and operations work from the same current data instead of two drifting copies.

Common use cases

  • 01 Share curated, synced datasets with other teams through read-only database shares
  • 02 Land CRM and operational database records in MotherDuck so a small team gets warehouse-style analytics without cluster management
  • 03 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.
  • 04 Publish records changed in a CRM or database as TextMessages onto a JMS Topic so subscribing Java services react in real time.

Common sync patterns

Keep user and access records aligned

Where Jms manages users, directory, or access data, those records stay current in MotherDuck — and can be provisioned back from it — so ownership and permissions match across both.

Operational data lands in MotherDuck for analytics

Records created in Jms — issues, events, messages, metrics, or user changes — replicate into MotherDuck tables as they happen, so reporting runs on current data instead of last night's export.

Warehouse signals reach Jms

A row scored, flagged, or enriched in MotherDuck creates or updates the matching record in Jms, so the operational tool acts on the same data the analysts already see.

What you can sync between Jms and MotherDuck

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 MotherDuck objects How this pairing syncs
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. Tables The main landing target for synced records and source for analysis. Durable Subscription is specific to Jms and Tables to MotherDuck — 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. Views Modeled projections used as outbound sync sources. Message headers and properties is specific to Jms and Views to MotherDuck — 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. Database Shares Read-only copies of a database shared with other users or teams. Dead Letter Queue is specific to Jms and Database Shares to MotherDuck — 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. Attached Local DuckDB Databases Local files attached alongside cloud databases for hybrid queries. Queue is specific to Jms and Attached Local DuckDB Databases to MotherDuck — 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. Databases Cloud-hosted DuckDB databases that scope a sync's reads and writes. Topic is specific to Jms and Databases to MotherDuck — each maps to any object or custom field on the other side.
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. Schemas Namespaces within a database used to organize synced tables. TextMessage is specific to Jms and Schemas to MotherDuck — each maps to any object or custom field on the other side.

How changes propagate between Jms and MotherDuck

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 MotherDuck 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 applied to MotherDuck as a row-level write, with types converted between the two schemas.

MotherDuck Jms Interval-based propagation

DetectionStacksync polls MotherDuck for changes on an incremental schedule, reading only records changed since the previous pass. Polling.

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.).
  • MotherDuck: Subject to the platform's compute and concurrency limits rather than per-request API rate limits.
What ships with Jms ⇄ MotherDuck

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Jms or MotherDuck 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 MotherDuck record.

Observability

Monitoring

Track your Jms ⇄ MotherDuck 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 MotherDuck.

How the Jms and MotherDuck 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.).

MotherDuck

Integration surface
SQL through DuckDB clients and drivers using a MotherDuck (md:) connection
Authentication
Access token created in MotherDuck (Settings > General > Create Token), pasted into Stacksync; database name and schema configurable if not using defaults
Change detection
Polling; no log-based CDC or webhook surface is exposed
Capabilities
read · write
Rate limits
Subject to the platform's compute and concurrency limits rather than per-request API rate limits
MotherDuck setup guide
How it works

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

    Choose tables

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

Jms and MotherDuck 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 328 integrations available for Jms and MotherDuck.

Popular · 8 of 328
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