Neo4j + BigQuery
Neo4j is a graph database management system designed to store and query highly connected data, efficiently representing relationships between entities. Postgres, on the other hand, is a highly robust and scalable open-source relational database management system that supports a wide range of data types and advanced features.
With Erathos, your Neo4j data reaches BigQuery in minutes, ready to be joined with other business sources in ad hoc queries, without your team having to manage ingestion infrastructure.
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Connect your source
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Configure the sync
Pick the tables, the schedule and the update type: batch, cursor-based incremental or CDC.
Choose your destination
Load into BigQuery, Redshift, Databricks, PostgreSQL, ClickHouse, Amazon S3 and more.
What Neo4j data does Erathos sync with PostgreSQL?
The integration automatically syncs key Neo4j objects:
- Selected tables incremental replication of any configured table
- Schema drift new columns are automatically detected and added to the destination
- Primary keys and timestamps used for efficient incremental syncs
- Historical data full initial load followed by incremental updates
Why sync Neo4j with BigQuery?
In BigQuery, you can query your Neo4j data alongside other business sources, running ad hoc queries in seconds over large volumes without managing any infrastructure.
How it works
Erathos connects to Neo4j via the official API and syncs your data incrementally: only new or updated records are processed in each run, keeping pipelines fast and BigQuery costs predictable. You choose the sync frequency (from 5 minutes to daily), the objects to sync, and the target dataset. Each run is logged with full observability: execution time, rows processed, error context, and instant alerts via Slack or email if anything goes wrong.
Centralizing Neo4j data in BigQuery 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 BigQuery — with schema drift detection and complete logs for every run.
Data in your data warehouse in minutes
Ready-to-use Neo4j connector
Replicate Neo4j tables to BigQuery with incremental synchronization and automatic schema drift detection—without breaking pipelines when the structure changes.
Learn moreTotal control over your Neo4j pipelines
Configure schedule, frequency, and sync type at the table level. Incremental synchronization processes only new or modified records—keeping BigQuery costs low and your analyses always up to date.
Learn moreEnd-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.
Learn moreBuilding data-driven stories
“Every new source implementation, if I had to do it myself, would take about two or three months. With Erathos, it's done in a few hours. I don't need the skills of a data engineer to generate real value from the company's data.”
“Erathos revolutionized data management at WE. By integrating multiple SaaS tools into a single DW, our technical team now focuses on the core business. We implemented dashboards with insights across every area, enriching our organizational culture and improving our decision-making.”
“We used to have a lot of rework, and now we don't. It's more efficient to work this way. If it's not your core business, hire Erathos. Spend your time modeling your own domain, not someone else's.”
“If I didn't have this infrastructure, I'd need three or four people to do what Erathos does today. I was able to generate real value from my data with a much leaner setup than I thought I'd need. That's the core of building a data-driven culture: the team only uses data when they can trust it.”
“Erathos brought a practical turnaround at CCM. We were able to integrate financial systems, CRM, and processes with BigQuery in just a few clicks, with no technical team required. That gave us a reliable data warehouse that powers automations, dashboards, and even our customer service bots.”
“The robustness and efficiency of Erathos's connectors — whether for Meta, Google, RD Station, or ERPs like Bling and Conta Azul — make the whole process much faster and more reliable. The technical documentation is extremely well put together and intuitive, which makes implementation much easier.”
Other paths for your data
Other destinations for Neo4j
Other sources that land in BigQuery
CRMs, ERPs, ad platforms and databases, all through the same managed pipeline.
Frequently Asked Questions
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.
Erathos uses incremental replication to sync tables from Neo4j to BigQuery. Schema drift is automatically detected—if a column is added or changed in Neo4j, the pipeline adapts without manual intervention.
You can configure the sync frequency at the table level, from every 5 minutes up to daily. Erathos uses incremental synchronization—only new or updated records are processed in each run, keeping the Neo4j pipeline efficient and BigQuery costs predictable.
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.
Yes. Every Erathos connector includes a 14-day free trial. Connect Neo4j to BigQuery and start syncing right away—no credit card required.