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Data warehouse ⇄ Database

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

Keep Apache Pinot 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 Pinot and Citus

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

Common use cases

  • 01 Serve user-facing analytics from Pinot while syncing daily rollups to finance and ops tools.
  • 02 Keep upsert-enabled real-time tables aligned with mutable operational records streamed from source systems.
  • 03 Sync high-volume event or tenant data from a Citus cluster into a warehouse for cross-tenant analytics.
  • 04 Write CRM or billing records into reference tables so distributed queries can join operational context locally on every node.

Common sync patterns

Serve warehouse results at database speed

Aggregates or model outputs computed in Apache Pinot 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 Apache Pinot and keep Citus focused on its operational workload.

What you can sync between Apache Pinot 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 Pinot objects Citus objects How this pairing syncs
Schemas Column definitions (dimensions, metrics, time columns) mapped during integration setup. Schemas Standard Postgres namespaces used to scope what a sync user can read and write. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Tables The queryable unit, defined as offline, real-time, or hybrid; the main read target. Local tables Coordinator-only tables that behave exactly like standard PostgreSQL tables. Tables is specific to Apache Pinot and Local tables to Citus — each maps to any object or custom field on the other side.
Segments Immutable data files that batch ingestion uploads and the cluster serves. Views Curated projections over distributed data, often used as read-only sync sources. Segments is specific to Apache Pinot and Views to Citus — each maps to any object or custom field on the other side.
Real-time Tables Tables fed continuously from streams like Kafka, including upsert-enabled tables. Sequences Key generators that matter when external writes must not collide with application inserts. Real-time Tables is specific to Apache Pinot and Sequences to Citus — each maps to any object or custom field on the other side.
Offline Tables Batch-loaded tables merged with real-time data at query time. Distributed tables Tables sharded across worker nodes by a distribution column; the main sync target for large datasets. Offline Tables is specific to Apache Pinot and Distributed tables to Citus — each maps to any object or custom field on the other side.
Indexes Inverted, range, and star-tree indexes that determine which sync queries run at low latency. Reference tables Small lookup tables replicated to every node, synced like ordinary Postgres tables. Indexes is specific to Apache Pinot and Reference tables to Citus — each maps to any object or custom field on the other side.

How changes propagate between Apache Pinot and Citus

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.

Apache Pinot Citus Interval-based propagation

DetectionStacksync polls Apache Pinot for changes on an incremental schedule, reading only records changed since the previous pass. Data enters Pinot via streaming ingestion or segment upload, not row-level writes.

DeliveryEach detected change is applied to Citus as a row-level write, with types converted between the two schemas.

Citus Apache Pinot Sub-second propagation

DetectionChanges in Citus are captured at the source via change data capture — no polling loop against its API. PostgreSQL logical decoding / CDC, with caveats: changes to distributed tables occur on worker shards, so CDC setup differs from single-node Postgres.

DeliveryEach detected change is applied to Apache Pinot as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • Apache Pinot: No fixed API quotas; query throughput depends on broker and server sizing.
What ships with Apache Pinot ⇄ Citus

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Apache Pinot 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 Pinot or Citus record.

Observability

Monitoring

Track your Apache Pinot ⇄ 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 Pinot and Citus.

How the Apache Pinot and Citus connectors work

Apache Pinot

Integration surface
REST API (SQL queries via the broker; administration via the controller); JDBC client available
Authentication
Deployment-dependent: HTTP basic authentication or token-based auth where enabled
Change detection
Not applicable for reads out (polling by time column); data enters Pinot via streaming ingestion or segment upload, not row-level writes
Capabilities
read · write
Rate limits
No fixed API quotas; query throughput depends on broker and server sizing

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 Pinot 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 Pinot 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 Pinot connected
    Citus connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

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

Apache Pinot and Citus 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 365 integrations available for Apache Pinot and Citus.

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