Streaming Audio: a Confluent podcast about Apache Kafka

Connecting to Apache Kafka with Neo4j

September 09, 2019 Season 1 Episode 52
Streaming Audio: a Confluent podcast about Apache Kafka
Connecting to Apache Kafka with Neo4j
Chapters
Streaming Audio: a Confluent podcast about Apache Kafka
Connecting to Apache Kafka with Neo4j
Sep 09, 2019 Season 1 Episode 52
Confluent, original creators of Apache Kafka®
Michael Hunger and David Allen discuss Neo4j basics and major features introduced in Neo4j 3.4.15. They'll cover the history of the integration and features in relation to Apache Kafka®, 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.
Show 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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