Real-time
Two-way sync
Changes in BigQuery or Neo4j instantly reflect in both systems. No stale data, no manual imports.
Data warehouse ⇄ Database
BigQuery to Neo4j integration — real-time, two-way sync
Keep BigQuery and Neo4j 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 Neo4j
Connect Neo4j 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 Neo4j'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 Neo4j where the services that read from it get them at normal query latency.
Stacksync covers both directions with one connection. Tables or collections in Neo4j sync into BigQuery in real time, and result tables in BigQuery sync back into Neo4j, 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 product catalog and order history into Neo4j to power recommendation queries.
- Feed identity and access data into a graph for entitlement and blast-radius analysis.
Offload heavy reads
Point analytical queries at the synced copy in BigQuery and keep Neo4j focused on its operational workload.
Operational data in the warehouse, minus the pipeline
Rows from Neo4j 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 Neo4j, where whatever reads from that database gets them without querying the warehouse.
What you can sync between BigQuery and Neo4j
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 | Neo4j objects | |
|---|---|---|
| Tables The syncable unit: only tables can be synced per the Stacksync docs. | Relationships Typed, directed edges that carry the connections syncs exist to model. | |
| Partitioned tables Synced like regular tables; partition columns map to target fields. | Properties Key-value attributes on both nodes and relationships, mapped from source fields. | |
| Clustered tables Supported; clustering is transparent to the sync. | Labels Node type markers used to map source tables or objects onto the graph. | |
| Datasets Organizational container — you pick which dataset’s tables to sync. | Indexes & Constraints Uniqueness constraints and indexes that make MERGE-based upserts reliable and fast. | |
| Projects Connection scope: the service account grants access per project. | Databases Named databases in a single instance that scope multi-tenant or multi-domain syncs. |
What ships with BigQuery ⇄ Neo4j
Connect BigQuery and Neo4j for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every BigQuery–Neo4j connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever BigQuery or Neo4j 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 Neo4j record.
Observability
Monitoring
Track your BigQuery ⇄ Neo4j 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 Neo4j.
How the BigQuery and Neo4j 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
Neo4j
- Integration surface
- Bolt binary protocol with Cypher via official drivers, plus an HTTP query API
- Authentication
- Username/password (basic auth); enterprise deployments add SSO options
- Change detection
- Neo4j Change Data Capture on Enterprise and Aura streams graph changes; otherwise Cypher polling on timestamp properties
- Capabilities
- read · write · CDC
How it works
How to connect BigQuery to Neo4j — 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 Neo4j 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
Neo4j
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the BigQuery and Neo4j 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 ⇄ Neo4jCustomers 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 Neo4jCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
BigQuery and Neo4j integration FAQ
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Yes. Stacksync provides a managed, real-time two-way integration between BigQuery and Neo4j: 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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Common patterns for BigQuery and Neo4j: Offload heavy reads; Operational data in the warehouse, minus the pipeline; Serve warehouse results at database speed. Point analytical queries at the synced copy in BigQuery and keep Neo4j focused on its operational workload.
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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. Neo4j: Bolt binary protocol with Cypher via official drivers, plus an HTTP query API. Authentication: Username/password (basic auth); enterprise deployments add SSO 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. Neo4j: Schema is optional, but uniqueness constraints and indexes are the standard way to make keyed syncs deterministic. Stacksync's field mapping accounts for these differences between BigQuery and Neo4j 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 Neo4j 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 Neo4j connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom BigQuery–Neo4j integration in-house.
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 Neo4j.
Popular · 8 of 386
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BigQuery ⇄ MongoDB -
BigQuery ⇄ CockroachDB -
BigQuery ⇄ Google AlloyDB -
BigQuery ⇄ Dynamo DB -
Neo4j ⇄ Databricks -
Neo4j ⇄ AWS S3 -
Neo4j ⇄ Actian Vector -
Neo4j ⇄ Amazon Redshift
No integrations match .
