Connecting to Apache Kafka with Neo4j

What’s a graph? How does Cypher work? In today's episode of Streaming Audio, Tim Berglund sits down with Michael Hunger (Lead of Neo4j Labs) and David Allen (Partner Solution Architect, Neo4j) to discuss Neo4j basics and get the scoop on major features introduced in Neo4j 3.4 and 3.5. Among these are geospatial and temporal types, but there’s also more to come in 4.0: a multi-database feature, fine-grained security, and reactive drivers/Spring Data Neo4j RX. 

In addition to sharing a little bit about the history of the integration and features in relation to Apache Kafka®, they also discuss change data capture (CDC), using Neo4j to put graph operations into an event streaming application, and how GraphQL fits in with event streaming and GRANDstack. The goal is to add graph abilities to help any distributed application become more successful.

EPISODE LINKS

• Kafka Connect Neo4j Sink
• Neo4j Streams Kafka Integration
• Extending the Stream/Table Duality into a Trinity, with Graphs (with Will Lyon)
• Neo4j Online Developer Summit
• Announcing NODES 2019 Global GraphHack
• Join the Confluent Community Slack

Episode source