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Episode 190December 14, 2021 | 28 min

Lessons Learned From Designing Serverless Apache Kafka ft. Prachetaa Raghavan

You might call building and operating Apache Kafka as a cloud-native data service synonymous with a serverless experience. Prachetaa Raghavan (Staff Software Developer I, Confluent) spends his days focused on this very thing. In this podcast, he shares his learnings from implementing a serverless architecture on Confluent Cloud using Kubernetes Operator.

Episode 189December 7, 2021 | 33 min

Using Apache Kafka as Cloud-Native Data System ft. Gwen Shapira

What does cloud native mean, and what are some design considerations when implementing cloud-native data services? Gwen Shapira (Apache Kafka Committer and Principal Engineer II, Confluent) addresses these questions in today’s episode. She shares her learnings by discussing a series of technical papers published by her team, which explains what they’ve done to expand Kafka’s cloud-native capabilities on Confluent Cloud.

Episode 188December 1, 2021 | 30 min

ksqlDB Fundamentals: How Apache Kafka, SQL, and ksqlDB Work Together ft. Simon Aubury

What is ksqlDB and how does Simon Aubury (Principal Data Engineer, Thoughtworks) use it to track down the plane that wakes his cat Snowy in the morning? Experienced in building real-time applications with ksqlDB since its genesis, Simon provides an introduction to ksqlDB by sharing some of his projects and use cases.

Episode 187November 23, 2021 | 29 min

Explaining Stream Processing and Apache Kafka ft. Eugene Meidinger

Many of us find ourselves in the position of equipping others to use Apache Kafka after we’ve gained an understanding of what Kafka is used for. But how do you communicate and teach others event streaming concepts effectively? As a Pluralsight instructor and business intelligence consultant, Eugene Meidinger shares tips for creating consumable training materials for conveying event streaming concepts to developers and IT administrators, who are trying to get on board with Kafka and stream processing.

Episode 186November 16, 2021 | 37 min

Handling Message Errors and Dead Letter Queues in Apache Kafka ft. Jason Bell

If you ever wondered what exactly dead letter queues (DLQs) are and how to use them, Jason Bell (Senior DataOps Engineer, Digitalis) has an answer for you. Dead letter queues are a feature of Kafka Connect that acts as the destination for failed messages due to errors like improper message deserialization and improper message formatting. Lots of Jason’s work is around Kafka Connect and the Kafka Streams API, and in this episode, he explains the fundamentals of dead letter queues, how to use them, and the parameters around them.

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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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