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September 9, 2019 | Episode 52

Connecting to Apache Kafka with Neo4j

  • Transcript
  • Notes

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.


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Episode 53September 12, 2019 | 48 min

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Episode 54September 16, 2019 | 29 min

Should You Run Apache Kafka on Kubernetes? ft. Balthazar Rouberol

What are the maturing stages of Kubernetes adoption? How did Datadog experience these stages? Balthazar Rouberol explains what to think about before hopping on Kubernetes hype train.

Episode 55September 18, 2019 | 43 min

KIP-500: Apache Kafka Without ZooKeeper ft. Colin McCabe and Jason Gustafson

Colin McCabe and Jason Gustafson discuss the history of Kafka, the creation of KIP-500, and the implications of removing ZooKeeper dependency and replacing it with a self-managed metadata quoroum.

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