Real-time
Two-way sync
Changes in Jdbc or Neo4j instantly reflect in both systems. No stale data, no manual imports.
Database
Jdbc to Neo4j integration — real-time, two-way sync
Keep Jdbc 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 Fivetran
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Migrated from Celigo
Why teams connect Jdbc and Neo4j
Keep Jdbc and Neo4j synchronized in real time, across engines, regions, or services, in one or both directions.
Two databases that must agree is one of the oldest problems in engineering: different engines for different workloads, separate services with overlapping reference data, a migration in flight, or regional instances that share a subset of records. Hand-rolled replication across systems means change capture, conflict handling, and type mapping, all built and maintained by your team.
Stacksync syncs tables or collections between Jdbc and Neo4j continuously and bi-directionally, translating types between the two engines and resolving conflicts by rules you configure. Rows written on either side appear on the other within seconds.
Common use cases
- 01 Incrementally sync a high-volume table by polling an updated_at or auto-increment column, keeping a downstream store fresh without full reloads.
- 02 Consolidate several databases from different vendors into one relational store by syncing each through its own JDBC driver.
- 03 Mirror CRM accounts and contacts into a graph to model buying-group and referral relationships.
- 04 Sync product catalog and order history into Neo4j to power recommendation queries.
Common sync patterns
Shared reference data between services
Services that own separate databases stay consistent on the records they share, without a custom replication layer.
Regional or environment copies
Mirror selected tables to another region or environment continuously, filtered to just the rows that should travel.
Cross-engine sync
Keep the same dataset live in both Jdbc and Neo4j, so each workload runs on the engine that suits it.
What you can sync between Jdbc 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.
| Jdbc objects | Neo4j objects | How this pairing syncs | |
|---|---|---|---|
| Sequences Server-generated identity values; relevant when writing rows into tables whose keys are assigned by the database rather than the source system. | Users & Roles Security principals controlling what an integration credential can query or modify. | Sequences is specific to Jdbc and Users & Roles to Neo4j — each maps to any object or custom field on the other side. | |
| Tables The base relational tables in the target database; synced two-way as rows over SQL, with each table's primary key driving upserts and row-level updates. | Nodes Entity records (customers, products, accounts) written from source systems as labeled nodes. | Tables is specific to Jdbc and Nodes to Neo4j — each maps to any object or custom field on the other side. | |
| Views Stored SELECT queries exposed like tables; read-only projections synced outbound when raw base tables should not be exposed downstream. | Relationships Typed, directed edges that carry the connections syncs exist to model. | Views is specific to Jdbc and Relationships to Neo4j — each maps to any object or custom field on the other side. | |
| Columns Per-table fields with data types, nullability, and defaults; enumerated via DatabaseMetaData.getColumns to auto-generate and type-check field mappings. | Properties Key-value attributes on both nodes and relationships, mapped from source fields. | Columns is specific to Jdbc and Properties to Neo4j — each maps to any object or custom field on the other side. | |
| Primary keys & indexes Key and index definitions read via DatabaseMetaData; the primary key is required for reliable upserts, and indexes on the cursor column keep incremental polling fast. | Labels Node type markers used to map source tables or objects onto the graph. | Primary keys & indexes is specific to Jdbc and Labels to Neo4j — each maps to any object or custom field on the other side. | |
| Schemas & catalogs Namespaces that group tables and views; the connector targets a schema/catalog and lists its objects from the JDBC metadata to build the sync. | Indexes & Constraints Uniqueness constraints and indexes that make MERGE-based upserts reliable and fast. | Schemas & catalogs is specific to Jdbc and Indexes & Constraints to Neo4j — each maps to any object or custom field on the other side. |
How changes propagate between Jdbc and Neo4j
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.
Jdbc Neo4j Interval-based propagation
DetectionStacksync polls Jdbc for changes on an incremental schedule, reading only records changed since the previous pass. No native change feed.
DeliveryEach detected change is written to Neo4j through its API, with automatic retries and rate-limit backoff.
Neo4j Jdbc Sub-second propagation
DetectionChanges in Neo4j are captured at the source via change data capture — no polling loop against its API. Neo4j Change Data Capture on Enterprise and Aura streams graph changes.
DeliveryEach detected change is applied to Jdbc as a row-level write, with types converted between the two schemas.
Rate-limit considerations
- Jdbc: No SaaS-style request quota. Throughput is bounded by the target database's max connections and connection-pool size, plus the CPU and I/O it shares with production queries, so heavy syncs can contend with live workloads.
What ships with Jdbc ⇄ Neo4j
Connect Jdbc and Neo4j for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Jdbc–Neo4j connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Jdbc 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 Jdbc or Neo4j record.
Observability
Monitoring
Track your Jdbc ⇄ Neo4j sync health, view errors, and replay failed events in one click.
Trading partners
EDI
Transform legacy EDI complexity into simple database interactions between Jdbc and Neo4j.
How the Jdbc and Neo4j connectors work
Jdbc
- Integration surface
- JDBC API (java.sql / javax.sql) executing SQL through a JDBC driver, typically a pure-Java Type 4 driver; reaches any relational database with a driver - PostgreSQL, MySQL, SQL Server, Oracle, IBM DB2, and others - via a JDBC URL such as jdbc:postgresql://host:5432/db.
- Authentication
- A database user's username and password supplied in the JDBC connection (DriverManager or a DataSource), typically over a TLS/SSL-encrypted connection. Some drivers add Kerberos, integrated Windows auth, or cloud IAM-token auth, but the available methods depend on the target database and its driver.
- Change detection
- No native change feed. Incremental sync polls a cursor column - an updated_at timestamp or an auto-incrementing key - to pull new and changed rows; detecting deletes needs soft-delete flags or database triggers writing to a shadow table. No webhooks.
- Capabilities
- read · write
- Rate limits
- No SaaS-style request quota. Throughput is bounded by the target database's max connections and connection-pool size, plus the CPU and I/O it shares with production queries, so heavy syncs can contend with live workloads.
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 Jdbc 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 Jdbc 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.
Jdbc connected
Neo4j
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Jdbc 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 · Jdbc ⇄ 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.
Jdbc Neo4jCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Jdbc and Neo4j integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Jdbc and Neo4j: authenticate both systems, choose the objects to sync (such as Jdbc's Sequences and Tables), map fields visually, and changes propagate both ways in milliseconds — no code required.
-
Stacksync pricing is usage-based and starts at $1,000/month, including the managed Jdbc and Neo4j connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Jdbc–Neo4j integration in-house.
-
Yes — Stacksync ships production-grade connectors for both Jdbc and Neo4j. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
-
Change detection on Jdbc: No native change feed. Incremental sync polls a cursor column - an updated_at timestamp or an auto-incrementing key - to pull new and changed rows; detecting deletes needs soft-delete flags or database triggers writing to a shadow table. No webhooks. On Neo4j: Neo4j Change Data Capture on Enterprise and Aura streams graph changes; otherwise Cypher polling on timestamp properties. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
-
On the Jdbc side: Views, Columns, Primary keys & indexes, Schemas & catalogs, plus custom fields where Jdbc exposes them. On the Neo4j side: Labels, Indexes & Constraints, Databases, Users & Roles. Stacksync auto-detects both schemas and converts types between the two systems.
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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.
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
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