Real-time
Two-way sync
Changes in Dremio or Rabbitmq instantly reflect in both systems. No stale data, no manual imports.
Data warehouse ⇄ Developer tools
Dremio to Rabbitmq integration — real-time, two-way sync
Keep Dremio and Rabbitmq 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
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Migrated from MuleSoft
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Migrated from Fivetran
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Migrated from Celigo
Why teams connect Dremio and Rabbitmq
Close the gap between analytics and operations: Dremio holds the record while Rabbitmq 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 Spaces and folders, Reflections, Jobs, Sources for reporting and analysis; Rabbitmq 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 Bindings, Messages, Virtual Hosts, Consumers produced in Rabbitmq are exactly what analysts want to measure in Dremio, and the curated rows in Dremio are what should drive the next action in Rabbitmq. 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 Spaces and folders, Reflections, Jobs, Sources in Dremio with Bindings, Messages, Virtual Hosts, Consumers in Rabbitmq 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 a single event out to multiple systems by binding several Queues to a fanout Exchange, each feeding a different Stacksync sync.
- 04 Route Messages by routing key through a topic Exchange so only Bindings matching a pattern (e.g. region or entity type) trigger a sync.
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 Rabbitmq 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.
Operational data lands in Dremio for analytics
Records created in Rabbitmq — issues, events, messages, metrics, or user changes — replicate into Dremio tables as they happen, so reporting runs on current data instead of last night's export.
What you can sync between Dremio and Rabbitmq
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 | Rabbitmq objects | How this pairing syncs | |
|---|---|---|---|
| Sources Connected storage and database systems (S3, ADLS, relational databases) Dremio queries in place. | Bindings Rules linking an exchange to a queue by routing key or pattern; they determine which messages reach which queue and can be declared during setup. | Sources is specific to Dremio and Bindings to Rabbitmq — 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. | Messages The payload plus headers and properties; consumed (read) via basic.consume and published (write) via basic.publish, then mapped to rows or records. | Physical datasets is specific to Dremio and Messages to Rabbitmq — 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. | Virtual Hosts Isolated namespaces (vhosts) that separate environments or tenants; a connection targets one vhost and permissions are scoped to it. | Virtual datasets (views) is specific to Dremio and Virtual Hosts to Rabbitmq — 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. | Consumers Subscriptions registered on a queue for push delivery; RabbitMQ pushes each enqueued message down the consumer's open AMQP connection in real time. | Apache Iceberg tables is specific to Dremio and Consumers to Rabbitmq — each maps to any object or custom field on the other side. | |
| Spaces and folders Namespaces that organize virtual datasets and govern access. | Connections and Channels Client sessions and their multiplexed channels; listable through the Management HTTP API for monitoring but not a sync payload themselves. | Spaces and folders is specific to Dremio and Connections and Channels to Rabbitmq — each maps to any object or custom field on the other side. | |
| Reflections Materialized accelerations that make repeated extraction queries cheaper. | Users and Permissions Auth principals and per-vhost configure/write/read access rules; managed over the HTTP API, usually read-only in a data sync. | Reflections is specific to Dremio and Users and Permissions to Rabbitmq — each maps to any object or custom field on the other side. |
How changes propagate between Dremio and Rabbitmq
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 Rabbitmq 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 Rabbitmq through its API, with automatic retries and rate-limit backoff.
Rabbitmq Dremio Interval-based propagation
DetectionStacksync polls Rabbitmq for changes on an incremental schedule, reading only records changed since the previous pass. Push delivery — a consumer subscribes to a queue (AMQP basic.consume) and RabbitMQ pushes each enqueued message down the open AMQP connection in real.
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.
- Rabbitmq: No fixed API request quota; the broker applies TCP back-pressure (flow control), per-consumer prefetch (QoS) limits, and blocks publishers when memory or disk alarms trip.
What ships with Dremio ⇄ Rabbitmq
Connect Dremio and Rabbitmq for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Dremio–Rabbitmq connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Dremio or Rabbitmq 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 Rabbitmq record.
Observability
Monitoring
Track your Dremio ⇄ Rabbitmq 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 Rabbitmq.
How the Dremio and Rabbitmq 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
Rabbitmq
- Integration surface
- AMQP 0-9-1 for publish/consume (AMQP 1.0 is native in RabbitMQ 4.0+; MQTT and STOMP via plugins) plus the Management HTTP REST API on port 15672
- Authentication
- AMQP username/password (SASL PLAIN) over TLS, plus x509 client certificates and OAuth 2.0 (JWT) via auth-backend plugins; the Management HTTP API uses HTTP Basic auth, or Bearer tokens when OAuth 2.0 is enabled
- Change detection
- Push delivery — a consumer subscribes to a queue (AMQP basic.consume) and RabbitMQ pushes each enqueued message down the open AMQP connection in real time; there is no modified-date polling, and the Management HTTP API is stats-only and poll-based
- Capabilities
- read · write
- Rate limits
- No fixed API request quota; the broker applies TCP back-pressure (flow control), per-consumer prefetch (QoS) limits, and blocks publishers when memory or disk alarms trip.
How it works
How to connect Dremio to Rabbitmq — 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.
- 01
Connect your apps
Authenticate Dremio and Rabbitmq with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Dremio
connected
Rabbitmq
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Dremio and Rabbitmq 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.
objects · Dremio ⇄ RabbitmqCustomers 12,480Sales Orders 8,213Invoices 5,902Items 1,344 - 03
Map fields
Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.
Dremio RabbitmqCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Dremio and Rabbitmq integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Dremio and Rabbitmq: authenticate both systems, choose the objects to sync (such as Dremio's Sources and Physical datasets), map fields visually, and changes propagate both ways in milliseconds — no code required.
-
Stacksync pricing is usage-based and starts at $1,000/month, including the managed Dremio and Rabbitmq connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Dremio–Rabbitmq integration in-house.
-
Yes — Stacksync ships production-grade connectors for both Dremio and Rabbitmq. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
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Change detection on Dremio: Polling via SQL; Iceberg table snapshots can anchor incremental reads; no consumer-facing change feed. On Rabbitmq: Push delivery — a consumer subscribes to a queue (AMQP basic.consume) and RabbitMQ pushes each enqueued message down the open AMQP connection in real time; there is no modified-date polling, and the Management HTTP API is stats-only and poll-based. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
-
On the Dremio side: Spaces and folders, Reflections, Jobs, Sources, plus custom fields where Dremio exposes them. On the Rabbitmq side: Bindings, Messages, Virtual Hosts, Consumers. Stacksync auto-detects both schemas and converts types between the two systems.
-
Yes. Each object mapping can be bidirectional or restricted to a single direction (both systems accept writes). Read-only mirrors, one-way pushes, and full two-way sync can be mixed in the same integration.
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.
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:
Related integrations
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