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Data warehouse ⇄ Developer tools

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

Keep Greenplum 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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Why teams connect Greenplum and Jms

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

Greenplum is the central store where teams keep Tables, Partitions, Views, External tables 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 Durable Subscription, Message headers and properties, Dead Letter Queue, Queue produced in Jms are exactly what analysts want to measure in Greenplum, and the curated rows in Greenplum 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 Tables, Partitions, Views, External tables in Greenplum with Durable Subscription, Message headers and properties, Dead Letter Queue, Queue 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 Keep Greenplum and a cloud warehouse in sync during a migration so both platforms serve consistent data.
  • 02 Feed BI reporting by syncing curated Greenplum views to downstream tools on a schedule.
  • 03 Fan out inventory or pricing updates onto a Topic with durable subscriptions so multiple services stay aligned even after downtime.
  • 04 Route messages by JMSType or application property using a message selector, landing high-priority messages in one table and the rest in another.

Common sync patterns

One shared record, kept consistent

Where both systems track the same entity, a change on either side propagates to the other, ending the manual reconciliation between the operational copy and the warehouse copy.

Keep user and access records aligned

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

Operational data lands in Greenplum for analytics

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

What you can sync between Greenplum 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.

Greenplum objects Jms objects How this pairing syncs
Partitions Large tables are commonly partitioned by date, which shapes incremental reads. 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. Partitions is specific to Greenplum and Durable Subscription to Jms — each maps to any object or custom field on the other side.
Views Read-only projections used to shape data before syncing it out. Message headers and properties JMSCorrelationID, JMSReplyTo, JMSTimestamp, JMSType plus JMSX/application-defined properties; used for correlation, routing, and selector-based filtering. Views is specific to Greenplum and Message headers and properties to Jms — each maps to any object or custom field on the other side.
External tables Reference external files for bulk load paths alongside row-level syncs. 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. External tables is specific to Greenplum and Dead Letter Queue to Jms — each maps to any object or custom field on the other side.
Rows Read and written by key; distribution keys determine where rows live. 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. Rows is specific to Greenplum and Queue to Jms — each maps to any object or custom field on the other side.
Databases Top-level containers that scope a sync connection. 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 is specific to Greenplum and Topic to Jms — each maps to any object or custom field on the other side.
Schemas Namespace tables and control which objects a sync can see. 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 is specific to Greenplum and TextMessage to Jms — each maps to any object or custom field on the other side.

How changes propagate between Greenplum 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.

Greenplum Jms Interval-based propagation

DetectionStacksync polls Greenplum for changes on an incremental schedule, reading only records changed since the previous pass. Polling with timestamp or key-based cursors.

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

Jms Greenplum 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 Greenplum as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • Greenplum: Bounded by cluster resources and concurrency settings rather than an API quota.
  • 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 Greenplum ⇄ Jms

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Greenplum 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 Greenplum or Jms record.

Observability

Monitoring

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

Trading partners

EDI

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

How the Greenplum and Jms connectors work

Greenplum

Integration surface
PostgreSQL wire protocol (libpq), plus JDBC/ODBC drivers
Authentication
Database credentials
Change detection
Polling with timestamp or key-based cursors; Greenplum does not expose logical-decoding CDC
Capabilities
read · write
Rate limits
Bounded by cluster resources and concurrency settings rather than an API quota.

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 Greenplum 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 Greenplum 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
    Greenplum connected
    Jms connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

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

Greenplum 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
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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 317 integrations available for Greenplum and Jms.

Popular · 7 of 317
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