Neo4j

Unlock the value of data with event streaming and graph databases

Businesses today rely on data that is connected and event driven. Neo4j’s connected data technology allows data to be represented as a graph. Combined with the event streaming platform from Confluent enables real time data relationships; resulting in powerful, new capabilities for enterprise customers. Integration with Kafka and Confluent Platform allows change events from Neo4j to be available to Kafka, so that other systems can consume them and, for example, update downstream systems. Additionally, the integration enables Neo4j users to consume events from Kafka and turn events into graph structures.

Why Neo4j and Confluent Platform?

  • Realize more value from streaming data

    Realize more value from streaming data

    Use flexible “whiteboard friendly” graph data modeling to understand the relationships between items in a flow of data transactions

  • Enrich transaction streams with powerful pattern insight

    Enrich transaction streams with powerful pattern insight

    For example, assign a fraud score to a financial transaction; or provide an influence metric with a social connection

     

     

  • Unlock new solutions

    Unlock new solutions

    The combination of streaming data and the leading native graph database unlock new solutions to difficult problems

  • Enable graph-based analytics

    Enable graph-based analytics

    Uncover fraud rings and other scams in real time with Neo4j data relationships graph database and Confluent event streaming

Neo4j and Confluent Integration

The Neo4j Verified Gold Kafka Connect Sink Connector allows data movement from Kafka topics into Neo4j via Cypher templated queries.

Neo4j and Confluent Integration

Nous utilisons des cookies afin de comprendre comment vous utilisez notre site et améliorer votre expérience. Cliquez ici pour en apprendre davantage ou pour modifier vos paramètres de cookies. En poursuivant la navigation, vous consentez à ce que nous utilisions des cookies.