Streaming Audio: A Confluent Podcast about Apache Kafka

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.

Jay Kreps to talk about stream processing, his early coding days at LinkedIn, starting Confluent, the highs, the lows, and everything in between.

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.

Gwen Shapira answers your questions on creating tables in nested JSON topics, how to balance ordering, latency and reliability, building event-based systems, and how to navigate the tricky endOffsets API. She also discusses the hardships of fencing zombie requests, talks given at previous Kafka Summits, and an important question from Ask Confluent #3.

Tim Berglund sits down with Ramesh Sringeri to discuss two Kafka use cases that Children's Healthcare of Atlanta is working on: achieving near-real-time streams of data to support meaningful intracranial pressure prediction and better manage intracranial pressure, and testing machine learning models with KSQL, Kafka Streams, and Kafka.