Real-time
Two-way sync
Changes in Cloudera Data Platform or Neo4j instantly reflect in both systems. No stale data, no manual imports.
Data warehouse ⇄ Database
Cloudera Data Platform to Neo4j integration — real-time, two-way sync
Keep Cloudera Data Platform 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
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Adopted by fast-scaling companies moving mission-critical data in real time
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Why teams connect Cloudera Data Platform and Neo4j
Connect Neo4j and Cloudera Data Platform 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 Cloudera Data Platform, 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 Cloudera Data Platform in real time, and result tables in Cloudera Data Platform sync back into Neo4j, with schema and type mapping between the two systems handled for you.
Common use cases
- 01 Consolidate tables from on-prem and cloud CDP environments into a single cloud warehouse target.
- 02 Sync curated CDP tables into an operational Postgres so applications query a low-latency copy instead of hitting the cluster.
- 03 Keep a customer-360 graph continuously updated from ERP, CRM, and support sources.
- 04 Mirror CRM accounts and contacts into a graph to model buying-group and referral relationships.
Common sync patterns
Offload heavy reads
Point analytical queries at the synced copy in Cloudera Data Platform and keep Neo4j focused on its operational workload.
Operational data in the warehouse, minus the pipeline
Rows from Neo4j land in Cloudera Data Platform as they change, replacing hand-built CDC and batch extract jobs.
Serve warehouse results at database speed
Aggregates or model outputs computed in Cloudera Data Platform sync into Neo4j, where whatever reads from that database gets them without querying the warehouse.
What you can sync between Cloudera Data Platform 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.
| Cloudera Data Platform objects | Neo4j objects | How this pairing syncs | |
|---|---|---|---|
| Databases Logical namespaces in the shared Hive Metastore that group tables for access control and syncs. | Databases Named databases in a single instance that scope multi-tenant or multi-domain syncs. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| Iceberg tables Open table format tables in newer CDP versions, with snapshot metadata usable for incremental reads. | Labels Node type markers used to map source tables or objects onto the graph. | Iceberg tables is specific to Cloudera Data Platform and Labels to Neo4j — each maps to any object or custom field on the other side. | |
| Views SQL views that can present curated, sync-ready projections of raw lake data. | Indexes & Constraints Uniqueness constraints and indexes that make MERGE-based upserts reliable and fast. | Views is specific to Cloudera Data Platform and Indexes & Constraints to Neo4j — each maps to any object or custom field on the other side. | |
| Partitions Table partitions (often by date) that incremental extraction jobs use to scope reads. | Users & Roles Security principals controlling what an integration credential can query or modify. | Partitions is specific to Cloudera Data Platform and Users & Roles to Neo4j — each maps to any object or custom field on the other side. | |
| Object store / HDFS files Underlying Parquet or ORC files on HDFS or cloud storage backing the tables. | Nodes Entity records (customers, products, accounts) written from source systems as labeled nodes. | Object store / HDFS files is specific to Cloudera Data Platform and Nodes to Neo4j — each maps to any object or custom field on the other side. | |
| Hive tables Warehouse tables queried over JDBC/ODBC; classic managed tables are append-oriented. | Relationships Typed, directed edges that carry the connections syncs exist to model. | Hive tables is specific to Cloudera Data Platform and Relationships to Neo4j — each maps to any object or custom field on the other side. |
How changes propagate between Cloudera Data Platform 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.
Cloudera Data Platform Neo4j Interval-based propagation
DetectionStacksync polls Cloudera Data Platform for changes on an incremental schedule, reading only records changed since the previous pass. Polling via SQL on timestamp or partition columns.
DeliveryEach detected change is written to Neo4j through its API, with automatic retries and rate-limit backoff.
Neo4j Cloudera Data Platform 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 Cloudera Data Platform as a row-level write, with types converted between the two schemas.
Rate-limit considerations
- Cloudera Data Platform: Constrained by cluster capacity and admission control rather than API rate limits.
What ships with Cloudera Data Platform ⇄ Neo4j
Connect Cloudera Data Platform and Neo4j for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Cloudera Data Platform–Neo4j connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Cloudera Data Platform 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 Cloudera Data Platform or Neo4j record.
Observability
Monitoring
Track your Cloudera Data Platform ⇄ Neo4j sync health, view errors, and replay failed events in one click.
Trading partners
EDI
Transform legacy EDI complexity into simple database interactions between Cloudera Data Platform and Neo4j.
How the Cloudera Data Platform and Neo4j connectors work
Cloudera Data Platform
- Integration surface
- JDBC/ODBC over Hive and Impala SQL endpoints, plus REST management APIs
- Authentication
- Kerberos, LDAP, or workload user credentials, often brokered through the Knox gateway
- Change detection
- Polling via SQL on timestamp or partition columns; no consumer-facing change feed
- Capabilities
- read · write
- Rate limits
- Constrained by cluster capacity and admission control rather than API rate limits
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 Cloudera Data Platform 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 Cloudera Data Platform 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.
Cloudera Data Platform
connected
Neo4j
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Cloudera Data Platform 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 · Cloudera Data Platform ⇄ 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.
Cloudera Data Platform Neo4jCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Cloudera Data Platform and Neo4j integration FAQ
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Yes. Stacksync provides a managed, real-time two-way integration between Cloudera Data Platform and Neo4j: authenticate both systems, choose the objects to sync (such as Cloudera Data Platform's Databases and Iceberg tables), map fields visually, and changes propagate both ways in milliseconds — no code required.
-
Common patterns for Cloudera Data Platform 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 Cloudera Data Platform and keep Neo4j focused on its operational workload.
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Cloudera Data Platform: JDBC/ODBC over Hive and Impala SQL endpoints, plus REST management APIs. Authentication: Kerberos, LDAP, or workload user credentials, often brokered through the Knox gateway. 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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Cloudera Data Platform: Access is commonly brokered by Apache Knox and secured with Kerberos or LDAP, which integration tooling must support. 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 Cloudera Data Platform 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 Cloudera Data Platform 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 Cloudera Data Platform and Neo4j connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Cloudera Data Platform–Neo4j integration in-house.
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
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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