November 4, 2021 | Episode 184

Real-Time Stream Processing with Kafka Streams ft. Bill Bejeck

  • Transcript
  • Notes

Kafka Streams is a native streaming library for Apache Kafka® that consumes messages from Kafka to perform operations like filtering a topic’s message and producing output back into Kafka. After working as a developer in stream processing, Bill Bejeck (Apache Kafka Committer and Integration Architect, Confluent) has found his calling in sharing knowledge and authoring his book, “Kafka Streams in Action.” As a Kafka Streams expert, Bill is also the author of the Kafka Streams 101 course on Confluent Developer, where he delves into what Kafka Streams is, how to use it, and how it works. 

Kafka Streams provides the abstraction over Kafka consumers and producers by minimizing administrative details like the need to code and manage frameworks required when using plain Kafka consumers and producers to process streams. Kafka Streams is declarative—you can state what you want to do, rather than how to do it. Kafka Streams leverages the KafkaConsumer protocol internally; it inherits its dynamic scaling properties and the consumer group protocol to dynamically redistribute the workload. When Kafka Streams applications are deployed separately but have the same, they are logically still one application. 

Kafka Streams has two processing APIs, the declarative API or domain-specific language (DSL)  is a high-level language that enables you to build anything needed with a processor topology, whereas the Processor API lets you specify a processor typology node by node, providing the ultimate flexibility. To underline the differences between the two APIs, Bill says it’s almost like using the object-relational mapping framework (ORM) versus SQL. 

The Kafka Streams 101 course is designed to get you started with Kafka Streams and to help you learn the fundamentals of: 

  • How streams and tables work 
  • How stateless and stateful operations work 
  • How to handle time windows and out of order data
  • How to deploy Kafka Streams

Continue Listening

Episode 185November 9, 2021 | 12 min

Confluent Platform 7.0: New Features + Updates

Confluent Platform 7.0 has launched and includes Apache Kafka 3.0, plus new features introduced by KIP-630: Kafka Raft Snapshot, KIP-745: Connect API to restart connector and task, and KIP-695: Further improve Kafka Streams timestamp synchronization. Reporting from Dubai, Tim Berglund (Senior Director, Developer Advocacy, Confluent) provides a summary of new features, updates, and improvements to the 7.0 release, including the ability to create a real-time bridge from on-premises environments to the cloud with Cluster Linking.

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.

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.

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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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