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Streaming Audio Podcast

Listen to Tim Berglund, Gwen Shapira, and guests unpack a variety of topics surrounding Apache Kafka®, Confluent, real-time data streaming, and the cloud.

All Topics
  • All Topics
  • Apache Kafka
  • Architecture
  • Ask Confluent
  • Big Ideas
  • Clients
  • Cloud
  • Confluent Platform
  • Distributed Systems Engineering
  • Event Streaming
  • Integration
  • Kafka
  • Kafka 101
  • Kafka Connect
  • Kafka Streams
  • ksqlDB
  • Kubernetes
  • Microservices
  • Schema Registry
  • Spring
  • Stream Processing
  • Streaming Audio Special
  • Use Cases
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Episode 91March 30, 2020 | 33 min

Cloud Marketplace Considerations with Dan Rosanova

Coming from decades of experience in messaging, Dan Rosanova discusses the pros and cons of cloud event streaming services on GCP, Azure, and Confluent Cloud. He also compares major stream processing and messaging services: Cloud Pub/Sub vs. Azure vs. Confluent Cloud, and outlines major differences among them.

Episode 90March 25, 2020 | 54 min

Explore, Expand, and Extract with 3X Thinking ft. Kent Beck

Kent Beck chats about various topics of broad interest to developers, including some of his books. He shares about what it’s like to experiment and implement new ideas, especially when others doubt what you're trying to achieve, as well as the difference between refactoring and tidying, his involvement with agile software and test-driven development, and what exactly test-commit-revert is. And yes, Kent talks a little bit about event streaming too!

Episode 89March 24, 2020 | 25 min

Ask Confluent #17: The “What is Apache Kafka?” Episode ft. Tim Berglund

Ask Confluent is back! Gwen and Tim talk through how to submit a proposal and get accepted to conferences, the secret for a long marriage, REST Proxy, and how Kafka relates to Splunk. For those who have just started integrating Kafka, Tim and Gwen provide pointers about how to go about understanding it.

Episode 88March 18, 2020 | 50 min

Domain-Driven Design and Apache Kafka with Paul Rayner

Paul Rayner describes how the vast tooling in DDD enables developers to focus on the coding that really matters and makes systems more collaborative, taking into account three primary considerations: how to get better at collaborating, strategic design and understanding why design really matters, and modeling codes.

Episode 87March 11, 2020 | 53 min

Machine Learning with TensorFlow and Apache Kafka ft. Chris Mattmann

TensorFlow is Google's version of wrangling various technologies to help group them together and work smoothly as large amounts of data flow through. Chris Mattmann, author of Machine Learning with TensorFlow, explains neural networks, how technology simulates cerebral processes that take place when our visual cortex receives a new image, and a use case that involves Apache Kafka® and event streaming to achieve TensorFlow's goals.

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Meet your hosts

Tim Berglund

Tim Berglund is a teacher, author, and technology leader with Confluent, where he serves as the senior director of developer advocacy. He can frequently be found at speaking at conferences in the U.S. and all over the world. Tim is the co-presenter of various O'Reilly training videos on topics ranging from Git to distributed systems, and he is the author of "Gradle Beyond the Basics." He lives in Littleton, CO, U.S., with the wife of his youth.

Gwen Shapira

Gwen Shapira is an engineering leader at Confluent. She has over 15 years of experience working with code and customers to build scalable data architectures, integrating relational and big data technologies. Gwen is the author of "Kafka: The Definitive Guide" and "Hadoop Application Architectures." Gwen is a frequent presenter at industry conferences, a PMC member on the Apache Kafka project, and a committer on Apache Sqoop™. When Gwen isn't building data pipelines or thinking up new features, you can find her pedaling on her bike exploring the roads and trails of California, and beyond.

Got questions?

If there's something you want to know about Apache Kafka, Confluent or event streaming, please send us an email with your question and we'll hope to answer it on the next episode of Ask Confluent.

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