Skip to content
Database ⇄ Developer tools

Google Cloud Spanner to Jms integration — real-time, two-way sync

Keep Google Cloud Spanner 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.

  • SOC 2 and 6 other compliance frameworks
  • POC with real engineers in minutes

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

Case study
Migrated from MuleSoft
Case study
Migrated from Celigo
Migrated from Heroku Connect
Migrated from Matillion
Case study
Migrated from Fivetran
Case study
Migrated from Celigo
Why teams connect Google Cloud Spanner and Jms

Keep Google Cloud Spanner and Jms in step: the rows in your database and the BytesMessage, Durable Subscription, Message headers and properties, Dead Letter Queue your engineering tools track stay consistent in real time, in both directions.

Google Cloud Spanner 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 Change streams, Views, Databases, Tables in Google Cloud Spanner with BytesMessage, Durable Subscription, Message headers and properties, Dead Letter Queue 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 Use change streams to feed near-real-time copies of operational tables into an analytics warehouse.
  • 02 Run a two-way sync between Spanner and a SaaS tool so edits made by ops teams land back in the application database.
  • 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

React to changes on either side in near real time

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

Where Jms manages users or groups: keep identity aligned

Directory and identity records in Jms stay matched to the users or owners table in Google Cloud Spanner, so provisioning and de-provisioning flow from one source.

Turn rows into the records your tools track

A new or changed row in Google Cloud Spanner creates or updates the matching record in Jms, whether that is an issue, an event, a message, or a user, so the tool reflects the database without a custom API job.

What you can sync between Google Cloud Spanner 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.

Google Cloud Spanner objects Jms objects How this pairing syncs
Tables Relational tables mapped one-to-one to sync targets. Message headers and properties JMSCorrelationID, JMSReplyTo, JMSTimestamp, JMSType plus JMSX/application-defined properties; used for correlation, routing, and selector-based filtering. Tables is specific to Google Cloud Spanner and Message headers and properties to Jms — each maps to any object or custom field on the other side.
Rows The unit of read and write in each sync cycle, keyed by primary key. 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. Rows is specific to Google Cloud Spanner and Dead Letter Queue to Jms — each maps to any object or custom field on the other side.
Interleaved tables Child rows physically co-located with parents; synced as related records. 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. Interleaved tables is specific to Google Cloud Spanner and Queue to Jms — each maps to any object or custom field on the other side.
Secondary indexes Used to make incremental read queries efficient on non-key columns. 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. Secondary indexes is specific to Google Cloud Spanner and Topic to Jms — each maps to any object or custom field on the other side.
Change streams Capture inserts, updates, and deletes for log-style change data capture. 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. Change streams is specific to Google Cloud Spanner and TextMessage to Jms — each maps to any object or custom field on the other side.
Views Read-only projections useful for shaping data before it leaves Spanner. MapMessage Body of typed name/value pairs that maps directly onto record fields, so no custom body parsing is needed on read or write. Views is specific to Google Cloud Spanner and MapMessage to Jms — each maps to any object or custom field on the other side.

How changes propagate between Google Cloud Spanner 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.

Google Cloud Spanner Jms Sub-second propagation

DetectionChanges in Google Cloud Spanner are captured at the source via change data capture — no polling loop against its API. Change streams (log-style CDC), or timestamp-based polling queries.

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

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

Rate-limit considerations

  • Google Cloud Spanner: Throughput is bounded by the instance's provisioned compute capacity rather than a fixed 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 Google Cloud Spanner ⇄ Jms

Connect Google Cloud Spanner and Jms for flexible, real-time data sync.

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Google Cloud Spanner 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 Google Cloud Spanner or Jms record.

Observability

Monitoring

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

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Google Cloud Spanner and Jms.

How the Google Cloud Spanner and Jms connectors work

Google Cloud Spanner

Integration surface
gRPC/REST client API with SQL query surface (GoogleSQL and PostgreSQL-interface dialects)
Authentication
Google Cloud IAM (service accounts)
Change detection
Change streams (log-style CDC), or timestamp-based polling queries
Capabilities
read · write · CDC
Rate limits
Throughput is bounded by the instance's provisioned compute capacity rather than a fixed 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 Google Cloud Spanner 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 Google Cloud Spanner 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
    Google Cloud Spanner connected
    Jms connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Google Cloud Spanner 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 · Google Cloud Spanner ⇄ 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
    Google Cloud Spanner Jms
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
Try interactive demo
FAQ

Google Cloud Spanner 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 306 integrations available for Google Cloud Spanner and Jms.

Popular · 8 of 306
All Google Cloud Spanner integrations All Jms integrations Browse all integrations
Coworkers laughing in front of a laptop in a casual office setting

Your last integration took months.
Your next one takes a prompt.

Book a demo Tour the platform on your own