Real-time
Two-way sync
Changes in Neo4j or Treasuredata instantly reflect in both systems. No stale data, no manual imports.
Database ⇄ Analytics
Neo4j to Treasuredata integration — real-time, two-way sync
Keep Neo4j and Treasuredata in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.
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Adopted by fast-scaling companies moving mission-critical data in real time
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Why teams connect Neo4j and Treasuredata
Give Treasuredata the users, events, and records that live in Neo4j in real time, and sync the cohorts and scores Treasuredata computes back into Neo4j where your applications read them.
A database holds the rows your business runs on: the users, events, orders, and records that every service reads and writes. Treasuredata is where people make sense of them, as dashboards, funnels, cohorts, and metrics. Moving the data from Neo4j into Treasuredata usually means a hand-built extract or a change-data-capture pipeline that breaks the moment a column is renamed, and reporting that always trails last night's load.
Stacksync syncs Users & Roles, Nodes, Relationships, Properties in Neo4j with Query Jobs, Databases, Tables, Master (Parent) Segments in Treasuredata in real time and in both directions. Operational rows flow into Treasuredata as they change, so dashboards read current data with no pipeline to maintain, and the segments, cohorts, or scores Treasuredata computes flow back into Neo4j, where the applications and services that read from it get them at normal query latency. Field-level mapping, schema and type translation, and conflict resolution are handled for you.
Common use cases
- 01 Push Predictive Segment propensity scores from TD onto customer records in an operational database or CRM for prioritization and lead routing.
- 02 Write CRM and product-usage records into TD Tables so they become source tables feeding parent-segment unification.
- 03 Write computed relationship scores (fraud, influence, similarity) back to operational systems.
- 04 Keep a customer-360 graph continuously updated from ERP, CRM, and support sources.
Common sync patterns
Where Treasuredata tracks product events: behavior onto stored records
Signup, usage, and lifecycle events captured in Treasuredata sync into Neo4j as rows, so applications and internal tools can read behavioral data next to the records they already keep.
Where Treasuredata builds cohorts or scores: results your services can read
Segments, cohorts, or scores computed in Treasuredata sync back into Neo4j, where the services that read from the database act on them at query speed without calling the analytics API.
Analytics on live operational data, minus the pipeline
The users, events, orders, and records stored in Neo4j land in Treasuredata as they change, so dashboards, funnels, and metrics run on current data instead of last night's extract.
What you can sync between Neo4j and Treasuredata
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.
| Neo4j objects | Treasuredata objects | How this pairing syncs | |
|---|---|---|---|
| Databases Named databases in a single instance that scope multi-tenant or multi-domain syncs. | Databases Logical containers for tables; a sync targets one database and maps its tables to warehouse or operational-DB tables. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| Users & Roles Security principals controlling what an integration credential can query or modify. | Tables Columnar log tables in TD's Plazma storage; every row carries a mandatory `time` column (Unix epoch) that Stacksync uses as the incremental watermark and partition key. Synced two-way with warehouse or database tables. | Users & Roles is specific to Neo4j and Tables to Treasuredata — each maps to any object or custom field on the other side. | |
| Nodes Entity records (customers, products, accounts) written from source systems as labeled nodes. | Master (Parent) Segments Unified customer profiles assembled from multiple source tables in Audience Studio; read out to push enriched attributes onto CRM or warehouse records. | Nodes is specific to Neo4j and Master (Parent) Segments to Treasuredata — each maps to any object or custom field on the other side. | |
| Relationships Typed, directed edges that carry the connections syncs exist to model. | Segments Campaign subsets of a parent segment; membership read out to activate audiences in downstream systems, or audience flags written back onto records. | Relationships is specific to Neo4j and Segments to Treasuredata — each maps to any object or custom field on the other side. | |
| Properties Key-value attributes on both nodes and relationships, mapped from source fields. | Journeys Timeline-based event sequences in Audience Studio; stage and membership read out for reporting and cross-system activation. | Properties is specific to Neo4j and Journeys to Treasuredata — each maps to any object or custom field on the other side. | |
| Labels Node type markers used to map source tables or objects onto the graph. | Predictive Segments AI/ML-scored segments; propensity scores read out and written onto customer records in a CRM or database for prioritization. | Labels is specific to Neo4j and Predictive Segments to Treasuredata — each maps to any object or custom field on the other side. |
How changes propagate between Neo4j and Treasuredata
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.
Neo4j Treasuredata 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 written to Treasuredata through its API, with automatic retries and rate-limit backoff.
Treasuredata Neo4j Interval-based propagation
DetectionStacksync polls Treasuredata for changes on an incremental schedule, reading only records changed since the previous pass. Polling on the mandatory `time` column (Unix-epoch partition key) or an updated-at column.
DeliveryEach detected change is written to Neo4j through its API, with automatic retries and rate-limit backoff.
Rate-limit considerations
- Treasuredata: TD does not publish a fixed request-per-second cap; query throughput is bounded by the account's compute resource pool and large reads/exports run as asynchronous jobs.
What ships with Neo4j ⇄ Treasuredata
Connect Neo4j and Treasuredata for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Neo4j–Treasuredata connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Neo4j or Treasuredata 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 Neo4j or Treasuredata record.
Observability
Monitoring
Track your Neo4j ⇄ Treasuredata sync health, view errors, and replay failed events in one click.
Trading partners
EDI
Transform legacy EDI complexity into simple database interactions between Neo4j and Treasuredata.
How the Neo4j and Treasuredata connectors work
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
Treasuredata
- Integration surface
- TD API v3 (REST) for databases, tables, and jobs, plus the Audience API (REST) for CDP segments and journeys
- Authentication
- API key sent as an `Authorization: TD1 <api_key>` header (per-user or account key from the TD Console); requests go to the region-specific endpoint (e.g. api.treasuredata.com for US, with separate EU and Tokyo endpoints)
- Change detection
- Polling on the mandatory `time` column (Unix-epoch partition key) or an updated-at column; TD stores append-oriented columnar data with no per-row CDC stream, so incremental syncs query for rows past a stored watermark
- Capabilities
- read · write
- Rate limits
- TD does not publish a fixed request-per-second cap; query throughput is bounded by the account's compute resource pool and large reads/exports run as asynchronous jobs
How it works
How to connect Neo4j to Treasuredata — 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 Neo4j and Treasuredata with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Neo4j
connected
Treasuredata
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Neo4j and Treasuredata 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 · Neo4j ⇄ TreasuredataCustomers 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.
Neo4j TreasuredataCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Neo4j and Treasuredata integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Neo4j and Treasuredata: authenticate both systems, choose the objects to sync (such as Neo4j's Databases and Users & Roles), map fields visually, and changes propagate both ways in milliseconds — no code required.
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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.
-
Common patterns for Neo4j and Treasuredata: Where Treasuredata tracks product events: behavior onto stored records; Where Treasuredata builds cohorts or scores: results your services can read; Analytics on live operational data, minus the pipeline. Signup, usage, and lifecycle events captured in Treasuredata sync into Neo4j as rows, so applications and internal tools can read behavioral data next to the records they already keep.
-
Neo4j: Bolt binary protocol with Cypher via official drivers, plus an HTTP query API. Authentication: Username/password (basic auth); enterprise deployments add SSO options. Treasuredata: TD API v3 (REST) for databases, tables, and jobs, plus the Audience API (REST) for CDP segments and journeys. Authentication: API key sent as an `Authorization: TD1 <api_key>` header (per-user or account key from the TD Console); requests go to the region-specific endpoint (e.g. api.treasuredata.com for US, with separate EU and Tokyo endpoints). Stacksync manages authentication, retries, and rate limits on both sides.
-
Treasuredata: Every TD table requires a `time` column (Unix epoch); it is the primary partition key and the natural incremental cursor for time-based sync. Neo4j: Schema is optional, but uniqueness constraints and indexes are the standard way to make keyed syncs deterministic. Stacksync's field mapping accounts for these differences between Neo4j and Treasuredata 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 Neo4j and Treasuredata records are not retained after a sync operation.
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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