Bill Bejeck's guest appearances

Bill Bejeck

Streaming Audio: Apache Kafka® & Real-Time Data

Jason Bell

Sam Ritchie

Kent Beck

Cornelia Davis

Anna McDonald

Kelsey Hightower

Lena Hall

Jay Smith

Matt Simpson

Colin McCabe

Sam Newman

Josh Long

Gwen Shapira

Paul Rayner

Dylan Scott

OwlTail

What is serverless?Having worked as a software engineer for over 15 years and as a regular contributor to Kafka Streams, Bill Bejeck (Integration Architect, Confluent) is an Apache Kafka® committer and author of “Kafka Streams in Action.” In today’s episode, he explains what serverless and the architectural concepts behind it are. To clarify, serverless doesn’t mean you can run an application without a server—there are still servers in the architecture, but they are abstracted away from your application development. In other words, you can focus on building and running applications and services without any concerns over infrastructure management. Using a cloud provider such as Amazon Web Services (AWS) enables you to allocate machine resources on demand while handling provisioning, maintenance, and scaling of the server infrastructure. There are a few important terms to know when implementing serverless functions with event stream processors: Functions as a service (FaaS)Stateless stream processingStateful stream processingServerless commonly falls into the FaaS cloud computing service category—for example, AWS Lambda is the classic definition of a FaaS offering. You have a greater degree of control to run a discrete chunk of code in response to certain events, and it lets you write code to solve a specific issue or use case. Stateless processing is simpler in comparison to stateful processing, which is more complex as it involves keeping the state of an event stream and needs a key-value store. ksqlDB allows you to perform both stateless and stateful processing, but its strength lies in stateful processing to answer complex questions while AWS Lambda is better suited for stateless processing tasks. By integrating ksqlDB with AWS Lambda together, they deliver serverless event streaming and analytics at scale.EPISODE LINKSWhat is Serverless?Serverless Stream Processing with Apache Kafka, AWS Lambda, and ksqlDBStateful Serverless Architectures with ksqlDB and AWS Lambda Serverless GitHub repositoryKafka Streams in ActionWatch the video version of this podcastJoin the Confluent CommunityLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Intro to Event-Driven Microservices with ConfluentUse PODCAST100 to get an additional $100 of free Confluent Cloud usage (details)

What is serverless?Having worked as a software engineer for over 15 years and as a regular contributor to Kafka Streams, Bill Bejeck (Integration Architect, Confluent) is an Apache Kafka® committer and author of “Kafka Streams in Action.” In today’s episode, he explains what serverless and the architectural concepts behind it are. To clarify, serverless doesn’t mean you can run an application without a server—there are still servers in the architecture, but they are abstracted away from your application development. In other words, you can focus on building and running applications and services without any concerns over infrastructure management. Using a cloud provider such as Amazon Web Services (AWS) enables you to allocate machine resources on demand while handling provisioning, maintenance, and scaling of the server infrastructure. There are a few important terms to know when implementing serverless functions with event stream processors: <ul><li>Functions as a service (FaaS)</li><li>Stateless stream processing</li><li>Stateful stream processing</li></ul>Serverless commonly falls into the FaaS cloud computing service category—for example, AWS Lambda is the classic definition of a FaaS offering. You have a greater degree of control to run a discrete chunk of code in response to certain events, and it lets you write code to solve a specific issue or use case. Stateless processing is simpler in comparison to stateful processing, which is more complex as it involves keeping the state of an event stream and needs a key-value store. ksqlDB allows you to perform both stateless and stateful processing, but its strength lies in stateful processing to answer complex questions while AWS Lambda is better suited for stateless processing tasks. By integrating ksqlDB with AWS Lambda together, they deliver serverless event streaming and analytics at scale. EPISODE LINKS<ul><li><a href="https://developer.confluent.io/learn/serverless/" rel="nofollow" target="_blank">What is Serverless?</a></li><li><a href="https://www.confluent.io/blog/serverless-event-stream-processing/" rel="nofollow" target="_blank">Serverless Stream Processing with Apache Kafka, AWS Lambda, and ksqlDB</a></li><li><a href="https://www.confluent.io/resources/white-paper/stateful-serverless-architectures-with-ksqldb-aws-lambda/" rel="nofollow" target="_blank">Stateful Serverless Architectures with ksqlDB and AWS Lambda </a></li><li><a href="https://github.com/confluentinc/CCloud-Serverless-Integration" rel="nofollow" target="_blank">Serverless GitHub repository</a></li><li><a href="https://www.manning.com/books/kafka-streams-in-action-second-edition" rel="nofollow" target="_blank">Kafka Streams in Action</a></li><li><a href="https://www.youtube.com/watch?v=3ApkMbvop5s&amp;list=PLa7VYi0yPIH1B0i7mhzVi78TIkKSd-0vE" rel="nofollow" target="_blank">Watch the video version of this podcast</a></li><li><a href="https://www.confluent.io/community/ask-the-community/" rel="nofollow" target="_blank">Join the Confluent Community</a></li><li><a href="https://developer.confluent.io/" rel="nofollow" target="_blank">Learn more with Kafka tutorials, resources, and guides at Confluent Developer</a></li><li><a href="https://www.confluent.io/resources/online-talk/demo-intro-to-event-driven-microservices-with-confluent/" rel="nofollow" target="_blank">Live demo: Intro to Event-Driven Microservices with Confluent</a></li><li><a href="https://www.confluent.io/confluent-cloud/tryfree" rel="nofollow" target="_blank">Use PODCAST100 to get an additional $100 of free Confluent Cloud usage</a> (<a href="https://www.confluent.io/confluent-cloud-promo-disclaimer/" rel="nofollow" target="_blank">details</a>)</li></ul>

Serverless Stream Processing with Apache Kafka ft. Bill Bejeck

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 application.id, 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 dataHow to deploy Kafka StreamsEPISODE LINKSKafka Streams 101 courseA Guide to Kafka Streams and Its UsesYour First Kafka Streams ApplicationKafka Streams 101 meetupWatch the video version of this podcastJoin the Confluent CommunityLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Intro to Event-Driven Microservices with ConfluentUse podcon19 to get 40% off "Kafka Streams in Action"Use podcon19 to get 40% off "Event Streaming with Kafka Streams and ksqlDB"Use PODCAST100 to get $100 of free Confluent Cloud usage (details)

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 <a href="http://application.id" rel="nofollow" target="_blank">application.id</a>, 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: <ul><li>How streams and tables work </li><li>How stateless and stateful operations work </li><li>How to handle time windows and out of order data</li><li>How to deploy Kafka Streams</li></ul>EPISODE LINKS<ul><li><a href="https://developer.confluent.io/learn-kafka/kafka-streams/get-started/" rel="nofollow" target="_blank">Kafka Streams 101 course</a></li><li><a href="https://www.confluent.io/blog/how-kafka-streams-works-guide-to-stream-processing/" rel="nofollow" target="_blank">A Guide to Kafka Streams and Its Uses</a></li><li><a href="https://kafka-tutorials.confluent.io/creating-first-apache-kafka-streams-application/confluent.html" rel="nofollow" target="_blank">Your First Kafka Streams Application</a></li><li><a href="https://www.meetup.com/Dallas-Kafka/events/281797737/?isFirstPublish=true" rel="nofollow" target="_blank">Kafka Streams 101 meetup</a></li><li><a href="https://www.youtube.com/watch?v=vc9j3sh5vu4&amp;list=PLa7VYi0yPIH1B0i7mhzVi78TIkKSd-0vE&amp;index=28" rel="nofollow" target="_blank">Watch the video version of this podcast</a></li><li><a href="https://www.confluent.io/community/ask-the-community/" rel="nofollow" target="_blank">Join the Confluent Community</a></li><li><a href="https://developer.confluent.io/" rel="nofollow" target="_blank">Learn more with Kafka tutorials, resources, and guides at Confluent Developer</a></li><li><a href="https://www.confluent.io/resources/online-talk/demo-intro-to-event-driven-microservices-with-confluent/" rel="nofollow" target="_blank">Live demo: Intro to Event-Driven Microservices with Confluent</a></li><li><a href="https://www.manning.com/books/kafka-streams-in-action" rel="nofollow" target="_blank">Use podcon19 to get 40% off "Kafka Streams in Action"</a></li><li><a href="https://www.manning.com/books/event-streaming-with-kafka-streams-and-ksqldb" rel="nofollow" target="_blank">Use podcon19 to get 40% off "Event Streaming with Kafka Streams and ksqlDB"</a></li><li><a href="https://www.confluent.io/confluent-cloud/tryfree" rel="nofollow" target="_blank">Use PODCAST100 to get $100 of free Confluent Cloud usage</a> (<a href="https://www.confluent.io/confluent-cloud-promo-disclaimer/" rel="nofollow" target="_blank">details</a>)</li></ul>

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

Tim Berglund interviews Bill Bejeck about the Kafka Streams API and his new book, Kafka Streams in Action. 

Tim Berglund interviews Bill Bejeck about the Kafka Streams API and his new book, <a href="https://www.manning.com/books/kafka-streams-in-action" rel="nofollow" target="_blank">Kafka Streams in Action</a>.