Real-time
Two-way sync
Changes in BigQuery or DuckDB instantly reflect in both systems. No stale data, no manual imports.
Data warehouse ⇄ Database
BigQuery to DuckDB integration — real-time, two-way sync
Keep BigQuery and DuckDB 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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Why teams connect BigQuery and DuckDB
Connect DuckDB 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 DuckDB'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 DuckDB where the services that read from it get them at normal query latency.
Stacksync covers both directions with one connection. Tables or collections in DuckDB sync into BigQuery in real time, and result tables in BigQuery sync back into DuckDB, 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 Push aggregates computed in DuckDB out to a CRM or business tools so analysis results reach operational systems.
- 04 Use DuckDB as a transform step: read synced Parquet exports, aggregate with SQL, and write results back to an operational database.
Common sync patterns
Operational data in the warehouse, minus the pipeline
Rows from DuckDB 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 DuckDB, 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.
What you can sync between BigQuery and DuckDB
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 | DuckDB objects | How this pairing syncs | |
|---|---|---|---|
| Tables The syncable unit: only tables can be synced per the Stacksync docs. | Tables Columnar tables created via SQL; the destination for materialized sync data. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| Partitioned tables Synced like regular tables; partition columns map to target fields. | Attached databases Additional database files or external systems attached into one session for cross-source queries. | Partitioned tables is specific to BigQuery and Attached databases to DuckDB — each maps to any object or custom field on the other side. | |
| Clustered tables Supported; clustering is transparent to the sync. | Database files Single-file .duckdb databases that jobs read and write directly on disk or object storage. | Clustered tables is specific to BigQuery and Database files to DuckDB — each maps to any object or custom field on the other side. | |
| Datasets Organizational container — you pick which dataset’s tables to sync. | Schemas Namespaces within a database used to organize tables in sync outputs. | Datasets is specific to BigQuery and Schemas to DuckDB — each maps to any object or custom field on the other side. | |
| Projects Connection scope: the service account grants access per project. | Views SQL views used to shape or filter data for downstream consumers. | Projects is specific to BigQuery and Views to DuckDB — each maps to any object or custom field on the other side. |
How changes propagate between BigQuery and DuckDB
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 DuckDB 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 DuckDB as a row-level write, with types converted between the two schemas.
DuckDB BigQuery Interval-based propagation
DetectionStacksync polls DuckDB for changes on an incremental schedule, reading only records changed since the previous pass. Polling or full re-reads.
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.
- DuckDB: No API rate limits; throughput is bounded by local compute and I/O.
What ships with BigQuery ⇄ DuckDB
Connect BigQuery and DuckDB for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every BigQuery–DuckDB connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever BigQuery or DuckDB 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 DuckDB record.
Observability
Monitoring
Track your BigQuery ⇄ DuckDB 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 DuckDB.
How the BigQuery and DuckDB 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
DuckDB
- Integration surface
- In-process SQL engine via client libraries (Python, Node.js, JDBC, CLI); no server or network API by default
- Authentication
- None built in; access control is file-system level (MotherDuck adds token auth for its hosted service)
- Change detection
- Polling or full re-reads; no change feed or transaction log API
- Capabilities
- read · write
- Rate limits
- No API rate limits; throughput is bounded by local compute and I/O
How it works
How to connect BigQuery to DuckDB — 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 DuckDB 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
DuckDB
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the BigQuery and DuckDB 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 ⇄ DuckDBCustomers 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 DuckDBCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
BigQuery and DuckDB integration FAQ
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Yes. Stacksync provides a managed, real-time two-way integration between BigQuery and DuckDB: 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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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 DuckDB 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 DuckDB connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom BigQuery–DuckDB integration in-house.
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Yes — Stacksync ships production-grade connectors for both BigQuery and DuckDB. 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 DuckDB: Polling or full re-reads; no change feed or transaction log API. 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: Datasets, Projects, Tables, Partitioned tables, plus custom fields where BigQuery exposes them. On the DuckDB side: Tables, Views, External files (Parquet/CSV/JSON), Attached databases. 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
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
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