Real-time
Two-way sync
Changes in BigQuery or Scaleway Postgres instantly reflect in both systems. No stale data, no manual imports.
Data warehouse ⇄ Database
BigQuery to Scaleway Postgres integration — real-time, two-way sync
Keep BigQuery and Scaleway Postgres 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 Celigo
Why teams connect BigQuery and Scaleway Postgres
Connect Scaleway Postgres 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 Scaleway Postgres'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 Scaleway Postgres where the services that read from it get them at normal query latency.
Stacksync covers both directions with one connection. Tables or collections in Scaleway Postgres sync into BigQuery in real time, and result tables in BigQuery sync back into Scaleway Postgres, with schema and type mapping between the two systems handled for you.
Common use cases
- 01 Feed ML feature tables in BigQuery from operational systems on a continuous schedule
- 02 Land CRM and ERP records in BigQuery continuously so dashboards reflect business systems without nightly batch jobs
- 03 Back internal tools with the same Postgres instance while Stacksync keeps it consistent with external systems
- 04 Keep an EU-hosted Postgres as the system of record while distributing data to US-hosted SaaS applications
Common sync patterns
Offload heavy reads
Point analytical queries at the synced copy in BigQuery and keep Scaleway Postgres focused on its operational workload.
Operational data in the warehouse, minus the pipeline
Rows from Scaleway Postgres land in BigQuery as they change, replacing hand-built CDC and batch extract jobs.
Serve warehouse results at database speed
Aggregates or model outputs computed in BigQuery sync into Scaleway Postgres, where whatever reads from that database gets them without querying the warehouse.
What you can sync between BigQuery and Scaleway Postgres
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 | Scaleway Postgres objects | How this pairing syncs | |
|---|---|---|---|
| Tables The syncable unit: only tables can be synced per the Stacksync docs. | Tables Primary sync unit; each table maps to an object or table on the other side of the sync. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| Clustered tables Supported; clustering is transparent to the sync. | Views Read-only sources for shaping data before it leaves the database. | Clustered tables is specific to BigQuery and Views to Scaleway Postgres — each maps to any object or custom field on the other side. | |
| Datasets Organizational container — you pick which dataset’s tables to sync. | Materialized views Precomputed result sets that can be read on a schedule for downstream syncs. | Datasets is specific to BigQuery and Materialized views to Scaleway Postgres — each maps to any object or custom field on the other side. | |
| Projects Connection scope: the service account grants access per project. | Schemas Namespace tables so multiple applications or environments can be synced selectively. | Projects is specific to BigQuery and Schemas to Scaleway Postgres — each maps to any object or custom field on the other side. | |
| Partitioned tables Synced like regular tables; partition columns map to target fields. | Sequences Generate primary keys; sync tooling must respect them when writing rows. | Partitioned tables is specific to BigQuery and Sequences to Scaleway Postgres — each maps to any object or custom field on the other side. |
How changes propagate between BigQuery and Scaleway Postgres
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.
BigQuery Scaleway Postgres Sub-second propagation
DetectionChanges in BigQuery are captured at the source via change data capture — no polling loop against its API. Real-time notification service deployed into your Google Cloud project: Eventarc ("a notification service that enables real-time updates to happen").
DeliveryEach detected change is applied to Scaleway Postgres as a row-level write, with types converted between the two schemas.
Scaleway Postgres BigQuery Sub-second propagation
DetectionChanges in Scaleway Postgres are captured at the source via change data capture — no polling loop against its API. Log-based CDC via PostgreSQL logical replication where the managed instance permits it.
DeliveryEach detected change is applied to BigQuery as a row-level write, with types converted between the two schemas.
Rate-limit considerations
- BigQuery: 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.
- Scaleway Postgres: Throughput is bounded by instance size and connection limits rather than API quotas.
What ships with BigQuery ⇄ Scaleway Postgres
Connect BigQuery and Scaleway Postgres for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every BigQuery–Scaleway Postgres connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever BigQuery or Scaleway Postgres 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 Scaleway Postgres record.
Observability
Monitoring
Track your BigQuery ⇄ Scaleway Postgres 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 Scaleway Postgres.
How the BigQuery and Scaleway Postgres 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
Scaleway Postgres
- Integration surface
- SQL wire protocol (PostgreSQL)
- Authentication
- Database credentials (username/password over TLS)
- Change detection
- Log-based CDC via PostgreSQL logical replication where the managed instance permits it; otherwise timestamp or query-based polling
- Capabilities
- read · write · CDC
- Rate limits
- Throughput is bounded by instance size and connection limits rather than API quotas
How it works
How to connect BigQuery to Scaleway Postgres — 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 Scaleway Postgres 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
Scaleway Postgres
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the BigQuery and Scaleway Postgres 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 ⇄ Scaleway PostgresCustomers 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 Scaleway PostgresCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
BigQuery and Scaleway Postgres integration FAQ
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Yes. Stacksync provides a managed, real-time two-way integration between BigQuery and Scaleway Postgres: authenticate both systems, choose the objects to sync (such as BigQuery's Tables and Clustered tables), map fields visually, and changes propagate both ways in milliseconds — no code required.
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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 Scaleway Postgres records are not retained after a sync operation.
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Stacksync pricing is usage-based and starts at $1,000/month, including the managed BigQuery and Scaleway Postgres connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom BigQuery–Scaleway Postgres integration in-house.
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Yes — Stacksync ships production-grade connectors for both BigQuery and Scaleway Postgres. 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 BigQuery: 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. On Scaleway Postgres: Log-based CDC via PostgreSQL logical replication where the managed instance permits it; otherwise timestamp or query-based polling. 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 BigQuery side: Tables, Partitioned tables, Clustered tables, Datasets, plus custom fields where BigQuery exposes them. On the Scaleway Postgres side: Materialized views, Schemas, Sequences, Columns. Stacksync auto-detects both schemas and converts types between the two systems.
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
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 479 integrations available for BigQuery and Scaleway Postgres.
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