Real-time
Two-way sync
Changes in AWS Aurora PostgreSQL or Neo4j instantly reflect in both systems. No stale data, no manual imports.
Database
AWS Aurora PostgreSQL to Neo4j integration — real-time, two-way sync
Keep AWS Aurora PostgreSQL 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
Case study
Migrated from Mulesoft
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Migrated from Matillion
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Migrated from Fivetran
Case study
Migrated from Celigo
Why teams connect AWS Aurora PostgreSQL and Neo4j
Keep AWS Aurora PostgreSQL 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 AWS Aurora PostgreSQL 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
- Capture row-level changes with logical replication and propagate them to SaaS tools without batch jobs.
- Sync JSONB-heavy application data into structured objects in downstream business systems.
- Write computed relationship scores (fraud, influence, similarity) back to operational systems.
- Keep a customer-360 graph continuously updated from ERP, CRM, and support sources.
Migration with zero-downtime cutover
When one database is replacing the other, sync both directions during the transition and switch traffic when ready, without a freeze window.
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.
What you can sync between AWS Aurora PostgreSQL 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.
| AWS Aurora PostgreSQL objects | Neo4j objects | |
|---|---|---|
| Rows Inserted, updated, and deleted in both directions during bi-directional syncs. | Databases Named databases in a single instance that scope multi-tenant or multi-domain syncs. | |
| Columns Rich Postgres types including JSONB and arrays are mapped to the paired system's fields. | Users & Roles Security principals controlling what an integration credential can query or modify. | |
| Primary keys and constraints Identify rows for upserts and enforce integrity on sync writes. | Nodes Entity records (customers, products, accounts) written from source systems as labeled nodes. | |
| Views and materialized views Usable as read-only sources for filtered or precomputed sync datasets. | Relationships Typed, directed edges that carry the connections syncs exist to model. | |
| Foreign keys Relationship metadata that syncs can translate into object references elsewhere. | Properties Key-value attributes on both nodes and relationships, mapped from source fields. | |
| Replication slots and publications The logical replication objects that power log-based CDC. | Labels Node type markers used to map source tables or objects onto the graph. |
What ships with AWS Aurora PostgreSQL ⇄ Neo4j
Connect AWS Aurora PostgreSQL and Neo4j for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every AWS Aurora PostgreSQL–Neo4j connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever AWS Aurora PostgreSQL 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 AWS Aurora PostgreSQL or Neo4j record.
Observability
Monitoring
Track your AWS Aurora PostgreSQL ⇄ Neo4j sync health, view errors, and replay failed events in one click.
Trading partners
EDI
Transform legacy EDI complexity into simple database interactions between AWS Aurora PostgreSQL and Neo4j.
How the AWS Aurora PostgreSQL and Neo4j connectors work
AWS Aurora PostgreSQL
- Integration surface
- SQL wire protocol (PostgreSQL-compatible), standard Postgres drivers and JDBC
- Authentication
- Database credentials, optionally AWS IAM database authentication, over TLS
- Change detection
- Log-based CDC via PostgreSQL logical replication (WAL decoding through replication slots), with timestamp polling as a fallback
- Capabilities
- read · write · CDC
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 AWS Aurora PostgreSQL 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 AWS Aurora PostgreSQL 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.
AWS Aurora PostgreSQL
connected
Neo4j
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the AWS Aurora PostgreSQL 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 · AWS Aurora PostgreSQL ⇄ 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.
AWS Aurora PostgreSQL Neo4jCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
AWS Aurora PostgreSQL and Neo4j integration FAQ
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Yes. Stacksync provides a managed, real-time two-way integration between AWS Aurora PostgreSQL and Neo4j: authenticate both systems, choose the objects to sync (such as AWS Aurora PostgreSQL's Rows and Columns), map fields visually, and changes propagate both ways in milliseconds — no code required.
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AWS Aurora PostgreSQL: Logical replication uses publications and replication slots, so CDC reads changes from the write-ahead log without polling production tables. Neo4j: Client drivers connect over the Bolt binary protocol rather than HTTP for query workloads. Stacksync's field mapping accounts for these differences between AWS Aurora PostgreSQL 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 AWS Aurora PostgreSQL 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 AWS Aurora PostgreSQL and Neo4j connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom AWS Aurora PostgreSQL–Neo4j integration in-house.
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Yes — Stacksync ships production-grade connectors for both AWS Aurora PostgreSQL and Neo4j. 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 AWS Aurora PostgreSQL: Log-based CDC via PostgreSQL logical replication (WAL decoding through replication slots), with timestamp polling as a fallback. 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.
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 AWS Aurora PostgreSQL and Neo4j.
Popular · 8 of 386
- AWS Aurora PostgreSQL ⇄ AWS Aurora MySQL
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AWS Aurora PostgreSQL ⇄ MongoDB -
AWS Aurora PostgreSQL ⇄ CockroachDB -
AWS Aurora PostgreSQL ⇄ Google AlloyDB -
Neo4j ⇄ Oracle DB -
Neo4j ⇄ RavenDB -
Neo4j ⇄ Redis Enterprise -
Neo4j ⇄ SAP ASE (Sybase)
No integrations match .
