Real-time
Two-way sync
Changes in BigQuery or Citus instantly reflect in both systems. No stale data, no manual imports.
Data warehouse ⇄ Database
BigQuery to Citus integration — real-time, two-way sync
Keep BigQuery 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.
- 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 Fivetran
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Migrated from Celigo
Why teams connect BigQuery and Citus
Connect Citus and BigQuery 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 BigQuery, 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 BigQuery in real time, and result tables in BigQuery sync back into Citus, with schema and type mapping between the two systems handled for you.
Common use cases
- Activate modeled BigQuery tables by syncing computed attributes back into sales and marketing tools
- Maintain a customer master table in BigQuery joined across CRM, billing, and support sources
- 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 BigQuery and keep Citus focused on its operational workload.
Operational data in the warehouse, minus the pipeline
Rows from Citus land in BigQuery as they change, replacing hand-built CDC and batch extract jobs.
What you can sync between BigQuery 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.
| BigQuery objects | Citus objects | |
|---|---|---|
| Tables The syncable unit: only tables can be synced per the Stacksync docs. | Local tables Coordinator-only tables that behave exactly like standard PostgreSQL tables. | |
| Partitioned tables Synced like regular tables; partition columns map to target fields. | Schemas Standard Postgres namespaces used to scope what a sync user can read and write. | |
| Clustered tables Supported; clustering is transparent to the sync. | Views Curated projections over distributed data, often used as read-only sync sources. | |
| Datasets Organizational container — you pick which dataset’s tables to sync. | Sequences Key generators that matter when external writes must not collide with application inserts. | |
| Projects Connection scope: the service account grants access per project. | Distributed tables Tables sharded across worker nodes by a distribution column; the main sync target for large datasets. |
What ships with BigQuery ⇄ Citus
Connect BigQuery and Citus for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every BigQuery–Citus connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever BigQuery 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 BigQuery or Citus record.
Observability
Monitoring
Track your BigQuery ⇄ Citus sync health, view errors, and replay failed events in one click.
Trading partners
EDI
Transform legacy EDI complexity into simple database interactions between BigQuery and Citus.
How the BigQuery and Citus connectors work
BigQuery
- Integration surface
- GoogleSQL via the BigQuery REST API, client libraries, JDBC/ODBC drivers, and the Storage Read/Write APIs
- Authentication
- Google Cloud service account: create a dedicated service account, grant roles (BigQuery Data Editor, BigQuery Job User, Cloud Functions Service Agent, Cloud Run Developer, Eventarc Event Receiver
- Change detection
- Real-time notification service deployed into your Google Cloud project: Eventarc ("a notification service that enables real-time updates to happen") with a Cloud Run "secure portal for real-time notification service in
- Capabilities
- read · write · CDC
- Rate limits
- Subject to Google Cloud quotas on queries, DML, and streaming; DML is supported but the platform favors append-heavy batch and streaming loads over row-at-a-time writes
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 BigQuery 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.
- 01
Connect your apps
Authenticate BigQuery 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.
BigQuery
connected
Citus
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the BigQuery 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.
objects · BigQuery ⇄ CitusCustomers 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.
BigQuery CitusCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
BigQuery and Citus integration FAQ
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Yes. Stacksync provides a managed, real-time two-way integration between BigQuery and Citus: authenticate both systems, choose the objects to sync (such as BigQuery's Tables and Partitioned tables), map fields visually, and changes propagate both ways in milliseconds — no code required.
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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 BigQuery and Citus: Fresh analytics without loading windows; Offload heavy reads; Operational data in the warehouse, minus the pipeline. Because changes stream continuously, analysts query current data instead of waiting for last night's load.
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BigQuery: GoogleSQL via the BigQuery REST API, client libraries, JDBC/ODBC drivers, and the Storage Read/Write APIs. Authentication: Google Cloud service account: create a dedicated service account, grant roles (BigQuery Data Editor, BigQuery Job User, Cloud Functions Service Agent, Cloud Run Developer, Eventarc Event Receiver. 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). Stacksync manages authentication, retries, and rate limits on both sides.
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BigQuery: The Storage Write API supports high-throughput streaming ingestion, which suits continuous sync loads better than legacy streaming inserts. Citus: Distributed tables are sharded by a declared distribution column, and reference tables are fully replicated to all nodes; the table type changes how writes and joins behave. Stacksync's field mapping accounts for these differences between BigQuery and Citus without custom code.
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Stacksync is SOC 2 Type II and ISO 27001 certified with HIPAA BAA support. Data is encrypted in transit, and a zero-persistent-storage architecture means BigQuery and Citus records are not retained after a sync operation.
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 BigQuery and Citus.
Popular · 8 of 386
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BigQuery ⇄ InterSystems IRIS -
BigQuery ⇄ MarkLogic -
BigQuery ⇄ Neo4j -
BigQuery ⇄ OCI Database (Oracle Cloud) -
Citus ⇄ Rockset -
Citus ⇄ Starburst Enterprise -
Citus ⇄ StarRocks -
Citus ⇄ Teradata Vantage
No integrations match .
