Real-time
Two-way sync
Changes in ClickHouse or Neo4j instantly reflect in both systems. No stale data, no manual imports.
Data warehouse ⇄ Database
ClickHouse to Neo4j integration — real-time, two-way sync
Keep ClickHouse 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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Why teams connect ClickHouse and Neo4j
Connect Neo4j and ClickHouse 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 ClickHouse, 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 ClickHouse in real time, and result tables in ClickHouse sync back into Neo4j, with schema and type mapping between the two systems handled for you.
Common use cases
- Land product event data alongside synced CRM accounts so analysts join usage and revenue in one place.
- Sync aggregated ClickHouse query results back into operational tools, such as account-level usage metrics into a CRM.
- Feed identity and access data into a graph for entitlement and blast-radius analysis.
- Write computed relationship scores (fraud, influence, similarity) back to operational systems.
Serve warehouse results at database speed
Aggregates or model outputs computed in ClickHouse sync into Neo4j, 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.
Offload heavy reads
Point analytical queries at the synced copy in ClickHouse and keep Neo4j focused on its operational workload.
What you can sync between ClickHouse 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.
| ClickHouse objects | Neo4j objects | |
|---|---|---|
| Distributed tables Query-routing tables over cluster shards in self-managed deployments. | Databases Named databases in a single instance that scope multi-tenant or multi-domain syncs. | |
| Dictionaries In-memory lookup structures refreshed from external sources, sometimes fed by syncs. | Users & Roles Security principals controlling what an integration credential can query or modify. | |
| Tables (MergeTree family) Columnar, append-optimized tables that serve as the destination for high-volume sync loads. | Nodes Entity records (customers, products, accounts) written from source systems as labeled nodes. | |
| Databases Namespaces that group tables and scope permissions for sync users. | Relationships Typed, directed edges that carry the connections syncs exist to model. | |
| Views Saved queries used as curated, read-only sync sources. | Properties Key-value attributes on both nodes and relationships, mapped from source fields. | |
| Materialized views Insert-time transformations that reshape incoming synced rows into aggregates. | Labels Node type markers used to map source tables or objects onto the graph. |
What ships with ClickHouse ⇄ Neo4j
Connect ClickHouse and Neo4j for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every ClickHouse–Neo4j connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever ClickHouse 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 ClickHouse or Neo4j record.
Observability
Monitoring
Track your ClickHouse ⇄ Neo4j sync health, view errors, and replay failed events in one click.
Trading partners
EDI
Transform legacy EDI complexity into simple database interactions between ClickHouse and Neo4j.
How the ClickHouse and Neo4j connectors work
ClickHouse
- Integration surface
- Native TCP protocol and HTTP interface; standard SQL dialect, with MySQL and PostgreSQL wire compatibility available
- Authentication
- Database credentials (username/password); ClickHouse Cloud issues per-service credentials over TLS
- Change detection
- No log-based CDC for consumers; incremental reads use polling on monotonic columns, and ClickHouse is usually the destination rather than the source
- Capabilities
- read · write
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 ClickHouse 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 ClickHouse 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.
ClickHouse
connected
Neo4j
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the ClickHouse 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 · ClickHouse ⇄ 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.
ClickHouse Neo4jCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
ClickHouse and Neo4j integration FAQ
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Yes. Stacksync provides a managed, real-time two-way integration between ClickHouse and Neo4j: authenticate both systems, choose the objects to sync (such as ClickHouse's Distributed tables and Dictionaries), map fields visually, and changes propagate both ways in milliseconds — no code required.
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On the ClickHouse side: Tables (MergeTree family), Databases, Views, Materialized views, plus custom fields where ClickHouse exposes them. On the Neo4j side: Relationships, Properties, Labels, Indexes & Constraints. 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.
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Common patterns for ClickHouse and Neo4j: Serve warehouse results at database speed; Fresh analytics without loading windows; Offload heavy reads. Aggregates or model outputs computed in ClickHouse sync into Neo4j, where whatever reads from that database gets them without querying the warehouse.
-
ClickHouse: Native TCP protocol and HTTP interface; standard SQL dialect, with MySQL and PostgreSQL wire compatibility available. Authentication: Database credentials (username/password); ClickHouse Cloud issues per-service credentials over TLS. 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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ClickHouse: It exposes both a native TCP protocol and an HTTP interface, and can additionally speak MySQL and PostgreSQL wire protocols for compatibility with existing drivers. Neo4j: Cypher is its declarative query language, and MERGE semantics give integrations a native upsert primitive for idempotent syncs. Stacksync's field mapping accounts for these differences between ClickHouse and Neo4j without custom code.
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 ClickHouse and Neo4j.
Popular · 8 of 386
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ClickHouse ⇄ AWS Aurora MySQL - ClickHouse ⇄ AWS Aurora PostgreSQL
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ClickHouse ⇄ MongoDB -
ClickHouse ⇄ CockroachDB -
Neo4j ⇄ Snowflake -
Neo4j ⇄ BigQuery -
Neo4j ⇄ MotherDuck -
Neo4j ⇄ Databricks
No integrations match .
