Neo4j + Databricks
Neo4j is a graph database management system, designed to store and query highly connected data by efficiently modeling relationships between entities. Databricks, in turn, is a serverless Big Data solution that delivers robust storage and large-scale analytics capabilities.
With Erathos, you can integrate data from Neo4j into Databricks in just a few minutes. Our platform handles the entire data movement process into your analytics environment and lets you combine that data with other sources in your Data Warehouse. That way, your time goes where it really creates value — extracting actionable insights and making more data-driven decisions.
What Neo4j data does Erathos sync with Databricks?
The integration automatically syncs Neo4j's core objects:
Selected tables — incremental replication of any configured table
Schema drift — new columns detected and automatically added to the destination
Primary keys and timestamps — used for efficient incremental syncing
Historical data — full initial load followed by incremental updates
Why sync Neo4j with Databricks?
Keeping an analytical copy of Neo4j operational data in Databricks ensures heavy queries don't impact production application performance. With incremental replication and schema drift detection, your data warehouse stays up to date while the transactional database remains stable and responsive.
How it works
Erathos connects to Neo4j through the official API and syncs data incrementally — only new or updated records are processed on each run, keeping pipelines fast and Databricks costs predictable. You choose the sync frequency (from 5 minutes to daily), the objects to sync, and the destination dataset. Every run is logged with full observability: run time, processed rows, contextual errors, and instant alerts via Slack or email if something goes wrong.
No credit card required.
Why do data teams choose Erathos for Neo4j?
Ready-to-use Neo4j connector
Replicate tables from Neo4j to Databricks with incremental synchronization and automatic schema drift detection—without breaking pipelines when the structure changes.
Total control over your Neo4j pipelines
Configure frequency, sync type, and partitioning by table. Data arrives in Databricks ready for ML, analytics, and ad hoc queries—with predictable cost.
End-to-end observability
No more finding out about Neo4j failures when the business team complains. Every run is logged with runtime, processed rows, and error context. Automatic alerts via Slack, Discord, or email as soon as something goes off track — keeping replication up to date without impacting the transactional database.
Why Companies Move Data from Neo4j to Databricks with Erathos
Centralizing Neo4j data in Databricks has never been easier
Erathos is a data ingestion platform for teams that need to replicate operational databases for analytics. With the Neo4j connector, you can incrementally sync tables and transactional records to Databricks—with schema drift detection and complete logs for every run.
Our Customers
Trusted by data-driven companies
Simplified data ingestion
1
Select your data source
More than 80 plug-and-play connectors to consolidate data from multiple sources, eliminate time-consuming manual processes, and create a streamlined path forward.
2
Setup your pipeline
Manage your pipeline seemlessly. Select a sync hour, frequency and type at a table/endpoint level.
3
Select your data warehouse
Choose between Amazon S3, BigQuery, Databricks, Redshift and PosgreSQL to centrlize your data
FAQ
What is Erathos and how can it help my company?
Erathos is a data ingestion platform built for reliability, transparency, and control. We help data teams connect tools like Neo4j to their data warehouse—with full observability into every run, zero maintenance, and none of the opacity of traditional market tools.
How does Erathos synchronize data from Neo4j to Databricks?
Erathos uses incremental replication to synchronize Neo4j tables to Databricks. Schema drift is detected automatically—if a column is added or changed in Neo4j, the pipeline adapts without manual intervention.
How often does Erathos synchronize data from Neo4j to Databricks?
You can configure sync frequency from every 5 minutes up to daily, at the table level. Erathos uses incremental synchronization—only new or updated records are processed in each run, keeping the Neo4j pipeline efficient and Databricks costs predictable.
What happens if a Neo4j synchronization fails?
Erathos automatically detects failures and sends alerts to your email, Slack, or Discord with full context—not just "job failed." Smart retries handle transient errors, and every execution is logged with run time, processed rows, and error context so your team can debug in minutes, not hours.
Is there a free trial period for the Neo4j connector?
Yes. Every Erathos connector includes a 14-day free trial. Connect Neo4j to Databricks and start syncing immediately—no credit card required.