Real-time
Two-way sync
Changes in Citus or Dremio instantly reflect in both systems. No stale data, no manual imports.
Database ⇄ Data warehouse
Citus to Dremio integration — real-time, two-way sync
Keep Citus and Dremio 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 Citus and Dremio
Connect Citus and Dremio with one live, two-way sync: operational rows flow into the warehouse, and computed results flow back where systems can read them fast.
Operational databases and analytical warehouses want the same data at different moments. Analysts want Citus's rows in Dremio, current and joinable, without a change-data-capture pipeline to maintain. Engineers want the outputs of warehouse work, such as aggregates, features, and segments, available in Citus where the services that read from it get them at normal query latency.
Stacksync covers both directions with one connection. Tables or collections in Citus sync into Dremio in real time, and result tables in Dremio sync back into Citus, with schema and type mapping between the two systems handled for you.
Common use cases
- Reverse-ETL aggregates computed over lake data out to CRMs and finance tools for business users.
- Publish operational database tables into Iceberg via Dremio so the lakehouse reflects current application state.
- Use a Citus cluster as the scalable operational store behind a customer-facing app while syncing summaries back to internal tools.
- Consolidate per-tenant rows from distributed tables into per-customer reporting databases.
Serve warehouse results at database speed
Aggregates or model outputs computed in Dremio sync into Citus, where whatever reads from that database gets them without querying the warehouse.
Fresh analytics without loading windows
Because changes stream continuously, analysts query current data instead of waiting for last night's load.
Offload heavy reads
Point analytical queries at the synced copy in Dremio and keep Citus focused on its operational workload.
What you can sync between Citus and Dremio
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.
| Citus objects | Dremio objects | |
|---|---|---|
| Views Curated projections over distributed data, often used as read-only sync sources. | Virtual datasets (views) SQL views layering semantics over physical data; the preferred sync target for curated extracts. | |
| Sequences Key generators that matter when external writes must not collide with application inserts. | Apache Iceberg tables Lakehouse tables supporting DML and snapshot metadata usable for incremental reads. | |
| Distributed tables Tables sharded across worker nodes by a distribution column; the main sync target for large datasets. | Spaces and folders Namespaces that organize virtual datasets and govern access. | |
| Reference tables Small lookup tables replicated to every node, synced like ordinary Postgres tables. | Reflections Materialized accelerations that make repeated extraction queries cheaper. | |
| Local tables Coordinator-only tables that behave exactly like standard PostgreSQL tables. | Jobs Query execution records useful for monitoring sync workloads. | |
| Schemas Standard Postgres namespaces used to scope what a sync user can read and write. | Sources Connected storage and database systems (S3, ADLS, relational databases) Dremio queries in place. |
What ships with Citus ⇄ Dremio
Connect Citus and Dremio for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Citus–Dremio connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Citus or Dremio 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 Citus or Dremio record.
Observability
Monitoring
Track your Citus ⇄ Dremio sync health, view errors, and replay failed events in one click.
Trading partners
EDI
Transform legacy EDI complexity into simple database interactions between Citus and Dremio.
How the Citus and Dremio connectors work
Citus
- Integration surface
- PostgreSQL wire protocol; any standard Postgres driver connects to the coordinator node
- Authentication
- Database credentials (standard PostgreSQL authentication; managed deployments add cloud IAM options)
- Change detection
- PostgreSQL logical decoding / CDC, with caveats: changes to distributed tables occur on worker shards, so CDC setup differs from single-node Postgres
- Capabilities
- read · write · CDC
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
How it works
How to connect Citus to Dremio — 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 Citus and Dremio with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Citus
connected
Dremio
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Citus and Dremio 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 · Citus ⇄ DremioCustomers 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.
Citus DremioCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Citus and Dremio integration FAQ
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Yes. Stacksync provides a managed, real-time two-way integration between Citus and Dremio: authenticate both systems, choose the objects to sync (such as Citus's Views and Sequences), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Change detection on Citus: PostgreSQL logical decoding / CDC, with caveats: changes to distributed tables occur on worker shards, so CDC setup differs from single-node Postgres. On Dremio: Polling via SQL; Iceberg table snapshots can anchor incremental reads; no consumer-facing change feed. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
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On the Dremio side: Sources, Physical datasets, Virtual datasets (views), Apache Iceberg tables, plus custom fields where Dremio exposes them. On the Citus side: Reference tables, Local tables, Schemas, Views. Stacksync auto-detects both schemas and converts types between the two systems.
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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.
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Common patterns for Citus and Dremio: Serve warehouse results at database speed; Fresh analytics without loading windows; Offload heavy reads. Aggregates or model outputs computed in Dremio sync into Citus, where whatever reads from that database gets them without querying the warehouse.
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Citus: PostgreSQL wire protocol; any standard Postgres driver connects to the coordinator node. Authentication: Database credentials (standard PostgreSQL authentication; managed deployments add cloud IAM options). Dremio: Arrow Flight SQL, JDBC/ODBC, and a REST API. Authentication: Personal access tokens or username/password; OAuth-based SSO on Dremio Cloud. Stacksync manages authentication, retries, and rate limits on both sides.
SECURITY
Security teams love 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
CSA STAR
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
Every pair below is a real-time, two-way sync. Search all 386 integrations available for Citus and Dremio.
Popular · 8 of 386
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Citus ⇄ Yellowbrick -
Citus ⇄ Snowflake -
Citus ⇄ BigQuery -
Citus ⇄ MotherDuck -
Dremio ⇄ IBM Db2 -
Dremio ⇄ Firebase -
Dremio ⇄ SingleStore -
Dremio ⇄ Amazon Aurora
No integrations match .
