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Data warehouse ⇄ Database

Apache Druid to Citus integration — real-time, two-way sync

Keep Apache Druid and Citus 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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Adopted by fast-scaling companies moving mission-critical data in real time

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Why teams connect Apache Druid and Citus

Connect Citus and Apache Druid 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 Apache Druid, 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 Apache Druid in real time, and result tables in Apache Druid sync back into Citus, with schema and type mapping between the two systems handled for you.

Common use cases

  • Expose product telemetry stored in Druid to business tools without granting direct cluster access.
  • Query aggregated event metrics from Druid and sync them into CRM account fields for usage-based selling.
  • Sync high-volume event or tenant data from a Citus cluster into a warehouse for cross-tenant analytics.
  • Write CRM or billing records into reference tables so distributed queries can join operational context locally on every node.

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 Apache Druid and keep Citus focused on its operational workload.

Operational data in the warehouse, minus the pipeline

Rows from Citus land in Apache Druid as they change, replacing hand-built CDC and batch extract jobs.

What you can sync between Apache Druid and Citus

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.

Apache Druid objects Citus objects
Segments Time-partitioned immutable files that hold datasource data; ingestion produces them. Sequences Key generators that matter when external writes must not collide with application inserts.
Dimensions String and categorical columns used for filtering and grouping in synced queries. Distributed tables Tables sharded across worker nodes by a distribution column; the main sync target for large datasets.
Metrics Numeric columns, often pre-aggregated at ingestion via rollup. Reference tables Small lookup tables replicated to every node, synced like ordinary Postgres tables.
Ingestion Supervisors Long-running specs that pull from streams like Kafka; the write path into Druid. Local tables Coordinator-only tables that behave exactly like standard PostgreSQL tables.
Lookups Key-value mappings joined at query time, refreshable from external systems. Schemas Standard Postgres namespaces used to scope what a sync user can read and write.
Tasks Batch ingestion and compaction jobs monitored during data loads. Views Curated projections over distributed data, often used as read-only sync sources.
What ships with Apache Druid ⇄ Citus

Connect Apache Druid and Citus for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Apache Druid–Citus connection.

Real-time

Two-way sync

Changes in Apache Druid or Citus instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Apache Druid or Citus 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 Apache Druid or Citus record.

Observability

Monitoring

Track your Apache Druid ⇄ Citus sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Apache Druid and Citus.

How the Apache Druid and Citus connectors work

Apache Druid

Integration surface
REST API (SQL over HTTP and native JSON queries); JDBC via Avatica
Authentication
Deployment-dependent: basic authentication or an authenticator extension; often fronted by a proxy
Change detection
Not applicable for reads out (polling by time interval); data enters Druid through streaming or batch ingestion rather than row updates
Capabilities
read · write
Rate limits
No fixed API quotas; query concurrency is bounded by broker and historical node capacity

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
How it works

How to connect Apache Druid to Citus — 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 Apache Druid and Citus 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
    Apache Druid connected
    Citus connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Apache Druid and Citus 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 · Apache Druid ⇄ Citus
    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
    Apache Druid Citus
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Apache Druid and Citus integration FAQ

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.

Learn more about security
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:

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 386 integrations available for Apache Druid and Citus.

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