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Data warehouse ⇄ Developer tools

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

Keep Dremio 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 Dremio and Jms

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

Dremio is the central store where teams keep Sources, Physical datasets, Virtual datasets (views), Apache Iceberg 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 Dead Letter Queue, Queue, Topic, TextMessage produced in Jms are exactly what analysts want to measure in Dremio, and the curated rows in Dremio 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 Sources, Physical datasets, Virtual datasets (views), Apache Iceberg tables in Dremio with Dead Letter Queue, Queue, Topic, TextMessage 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 Publish operational database tables into Iceberg via Dremio so the lakehouse reflects current application state.
  • 02 Consolidate data from multiple lake sources through one Dremio semantic layer into a single warehouse target.
  • 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

No batch jobs to babysit

New and changed records move field by field the moment they change, replacing scheduled ETL and one-off scripts that fail quietly and leave stale rows behind.

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 Dremio — and can be provisioned back from it — so ownership and permissions match across both.

What you can sync between Dremio 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.

Dremio objects Jms objects How this pairing syncs
Sources Connected storage and database systems (S3, ADLS, relational databases) Dremio queries in place. 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. Sources is specific to Dremio and TextMessage to Jms — each maps to any object or custom field on the other side.
Physical datasets Tables and files promoted from sources; the raw data a sync ultimately reads. MapMessage Body of typed name/value pairs that maps directly onto record fields, so no custom body parsing is needed on read or write. Physical datasets is specific to Dremio and MapMessage to Jms — each maps to any object or custom field on the other side.
Virtual datasets (views) SQL views layering semantics over physical data; the preferred sync target for curated extracts. BytesMessage Raw binary body for files, protobuf, or opaque payloads; passed through as a byte stream when structured field mapping is not required. Virtual datasets (views) is specific to Dremio and BytesMessage to Jms — each maps to any object or custom field on the other side.
Apache Iceberg tables Lakehouse tables supporting DML and snapshot metadata usable for 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. Apache Iceberg tables is specific to Dremio and Durable Subscription to Jms — each maps to any object or custom field on the other side.
Spaces and folders Namespaces that organize virtual datasets and govern access. Message headers and properties JMSCorrelationID, JMSReplyTo, JMSTimestamp, JMSType plus JMSX/application-defined properties; used for correlation, routing, and selector-based filtering. Spaces and folders is specific to Dremio and Message headers and properties to Jms — each maps to any object or custom field on the other side.
Reflections Materialized accelerations that make repeated extraction queries cheaper. 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. Reflections is specific to Dremio and Dead Letter Queue to Jms — each maps to any object or custom field on the other side.

How changes propagate between Dremio 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.

Dremio Jms Interval-based propagation

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

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

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

Rate-limit considerations

  • Dremio: Bounded by engine capacity and workload management rather than API rate limits.
  • 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 Dremio ⇄ Jms

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Dremio 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 Dremio or Jms record.

Observability

Monitoring

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

Trading partners

EDI

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

How the Dremio and Jms connectors work

Dremio

Integration surface
Arrow Flight SQL, JDBC/ODBC, and a REST API
Authentication
Personal access tokens or username/password; OAuth-based SSO on Dremio Cloud
Change detection
Polling via SQL; Iceberg table snapshots can anchor incremental reads; no consumer-facing change feed
Capabilities
read · write
Rate limits
Bounded by engine capacity and workload management rather than API rate limits

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

    Choose tables

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

Dremio 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 313 integrations available for Dremio and Jms.

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