Real-time
Two-way sync
Changes in Elasticsearch or Neo4j instantly reflect in both systems. No stale data, no manual imports.
Database
Elasticsearch to Neo4j integration — real-time, two-way sync
Keep Elasticsearch 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
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Migrated from Celigo
Why teams connect Elasticsearch and Neo4j
Keep Elasticsearch 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 Elasticsearch 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
- Mirror support tickets into an index used for full-text search and agent-assist tooling.
- Feed enriched customer records into an index used for vector or hybrid search in AI applications.
- Write computed relationship scores (fraud, influence, similarity) back to operational systems.
- Keep a customer-360 graph continuously updated from ERP, CRM, and support sources.
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 Elasticsearch and Neo4j, so each workload runs on the engine that suits it.
What you can sync between Elasticsearch 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.
| Elasticsearch objects | Neo4j objects | |
|---|---|---|
| Index templates Reusable settings and mappings applied automatically to new indices a sync creates. | Users & Roles Security principals controlling what an integration credential can query or modify. | |
| Indices Target containers for synced records; each holds a table-like collection of JSON documents. | Nodes Entity records (customers, products, accounts) written from source systems as labeled nodes. | |
| Documents The unit of sync; JSON records created, updated, and deleted by _id. | Relationships Typed, directed edges that carry the connections syncs exist to model. | |
| Index mappings Field type definitions that determine how synced fields are indexed and queried. | Properties Key-value attributes on both nodes and relationships, mapped from source fields. | |
| Aliases Stable read/write names that let a sync cut over between index versions without downtime. | Labels Node type markers used to map source tables or objects onto the graph. | |
| Data streams Append-only targets for time-series or event data pushed from source systems. | Indexes & Constraints Uniqueness constraints and indexes that make MERGE-based upserts reliable and fast. |
What ships with Elasticsearch ⇄ Neo4j
Connect Elasticsearch and Neo4j for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Elasticsearch–Neo4j connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Elasticsearch 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 Elasticsearch or Neo4j record.
Observability
Monitoring
Track your Elasticsearch ⇄ Neo4j sync health, view errors, and replay failed events in one click.
Trading partners
EDI
Transform legacy EDI complexity into simple database interactions between Elasticsearch and Neo4j.
How the Elasticsearch and Neo4j connectors work
Elasticsearch
- Integration surface
- REST API (JSON over HTTP)
- Authentication
- API keys or basic authentication; Elastic Cloud also issues service account tokens
- Change detection
- Polling on timestamp or sequence fields; Elasticsearch does not expose a native change feed or webhooks
- Capabilities
- read · write
- Rate limits
- No fixed request quota; throughput is bounded by cluster sizing, thread pools, and bulk queue capacity
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 Elasticsearch 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 Elasticsearch 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.
Elasticsearch
connected
Neo4j
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Elasticsearch 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 · Elasticsearch ⇄ 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.
Elasticsearch Neo4jCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Elasticsearch and Neo4j integration FAQ
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Yes. Stacksync provides a managed, real-time two-way integration between Elasticsearch and Neo4j: authenticate both systems, choose the objects to sync (such as Elasticsearch's Index templates and Indices), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Elasticsearch: REST API (JSON over HTTP). Authentication: API keys or basic authentication; Elastic Cloud also issues service account tokens. 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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Elasticsearch: Writes are addressed by document _id, so upserts map directly onto the index API, and the _bulk endpoint batches many operations in a single request. Neo4j: Neo4j uses a property graph model in which nodes and relationships both carry key-value properties, so edges hold data rather than just linking rows. Stacksync's field mapping accounts for these differences between Elasticsearch 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 Elasticsearch 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 Elasticsearch and Neo4j connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Elasticsearch–Neo4j integration in-house.
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Yes — Stacksync ships production-grade connectors for both Elasticsearch and Neo4j. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
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 Elasticsearch and Neo4j.
Popular · 8 of 386
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Elasticsearch ⇄ RavenDB -
Elasticsearch ⇄ Redis Enterprise -
Elasticsearch ⇄ SAP ASE (Sybase) -
Elasticsearch ⇄ Scaleway Postgres -
Neo4j ⇄ InterSystems IRIS -
Neo4j ⇄ MarkLogic -
Neo4j ⇄ OCI Database (Oracle Cloud) -
Neo4j ⇄ OpenSearch
No integrations match .
