Join us for a comprehensive discussion on stream processing with Redpanda and Apache Flink. This talk is designed for those who have some working knowledge of Kafka/Redpanda and are looking to learn more about stream processing.
The first part of our talk will focus on introducing stream processing with Flink. We begin by discussing stateless operations in Flink. Then we dive into how Flink handles the state and time concepts, covering a range of topics including stateful aggregations, window processing, and joins. Furthermore, we will explore some advanced topics, such as fault tolerance, state checkpointing, and event time processing, shedding light on its importance in stream processing.
The second half of the talk transitions from theory to practice, and will be more hands-on. We will guide you through the process of building a Flink application in Java. This segment is designed to provide a practical understanding of how to write, build, and deploy an application to a Flink cluster. We will demonstrate how this application is set up to ingest data from a Kafka topic, deploy it to a Flink cluster, and monitor how it operates under various conditions.
This talk is an excellent opportunity to enhance your understanding of these powerful tools. It will equip you with the knowledge and skills needed to build robust stream processing applications with Redpanda and Apache Flink.
Lecturer: Dunith Danushka
My short biography:
Dunith avidly enjoys designing, building, and operating large-scale real-time, event-driven architectures. He's got 10+ years of doing so and loves to share his learnings through blogging, videos, and public speaking.
Dunith works at Redpanda as a Senior Developer Advocate, where he spends much time educating developers about building event-driven applications with Redpanda.
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