Viktor Gamov
Principal Developer Advocate, Confluent
New York City, New York, United States
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Viktor Gamov is a Principal Developer Advocate at Confluent, founded by the original creators of Apache Kafka®. With a rich background in implementing and advocating for distributed systems and cloud-native architectures, Viktor excels in open-source technologies. He is passionate about assisting architects, developers, and operators in crafting systems that are not only low in latency and scalable but also highly available.
As a Java Champion and an esteemed speaker, Viktor is known for his insightful presentations at top industry events like JavaOne, Devoxx, Kafka Summit, and QCon. His expertise spans distributed systems, real-time data streaming, JVM, and DevOps.
Viktor has co-authored "Enterprise Web Development" from O'Reilly and "Apache Kafka® in Action" from Manning.
Follow Viktor on X—@gamussa to stay updated with his latest thoughts on technology, his gym and food adventures, and insights into open-source and developer advocacy.
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Apache Kafka Event streaming platform for .NET developers
When it comes time to choose a distributed messaging system, everyone knows the answer: Apache Kafka. But how about when you’re on the hook to choose a world-class, horizontally scalable stream data processing system? When you need not just publish and subscribe messaging, but also long-term storage, a flexible integration framework, and a means of deploying real-time stream processing applications at scale without having to integrate many different pieces of infrastructure yourself? The answer is still Apache Kafka.
In this talk, Viktor will give a rapid-fire review of the breadth of Kafka as a streaming data platform. You will see its internal architecture, including how it partitions messaging workloads in a fault-tolerant way and how it provides message durability. Viktor will explain Kafka’s approach to pub/sub messaging and how the Confluent .NET client offers a framework for computation over streaming data.
Stream Processing Smackdown: Kafka Streams vs. Apache Flink
Attention, Data Streaming Engineers! In a world where speed is everything, choosing the proper stream processing framework is crucial.
Want to supercharge your apps with real-time data processing? Should you opt for the streamlined Kafka Streams, a lightweight library for building streaming applications, or the feature-rich Apache Flink, a powerful and flexible stream processing framework?
Viktor Gamov, a principal developer advocate at Confluent with extensive experience in stream processing, will walk you through the nuts and bolts of these two leading technologies. Through live coding and practical examples, we'll cover:
• Mastering State Management: Discover how each framework handles stateful computations and pick up optimization tips.
• Fault Tolerance in Practice: See how Kafka Streams and Flink keep your applications running smoothly, even when things go wrong.
• Scalability Showdown: Find out which tool scales better under heavy loads and complex tasks.
• Integration Insights: Learn how to seamlessly fit these frameworks into your existing setup to boost productivity.
We'll explore scenarios showcasing each option’s strengths and weaknesses, giving you the tools to choose the best fit for your next project. Whether you're into microservices, event-driven systems, or big data streaming, this talk is packed with practical knowledge that you can immediately apply to your projects, improving performance and efficiency.
Streamlined Data Processing with Apache Flink and Kotlin
Building real-time data processing platforms often leads developers to struggle with traditional tools that can't handle massive data streams efficiently. Apache Flink offers a powerful solution to these challenges, and this talk demonstrates how to leverage it effectively using Kotlin.
We'll explore the DataStream API for efficient stream processing and show how to write cleaner, more maintainable code using Kotlin's language features. Through practical examples, you'll learn:
Building expressive stream processing pipelines that handle unbounded data efficiently
Working with Flink's Table API and SQL features while maintaining type safety
Managing application state effectively for complex event processing
Implementing robust testing strategies for stream-processing applications
The session includes real-world examples of how Flink solved critical performance challenges in production systems. You'll learn how to handle common pitfalls, implement best practices, and structure your applications for scalability. Whether building real-time analytics dashboards or processing event streams, you'll walk away with practical knowledge to implement Flink in your next project.
One Does Not Simply Query a Stream
Suppose you have embraced Apache Kafka as the core of your data infrastructure. In that case, you have probably integrated event-driven services to communicate with each other through topics, combined with legacy systems through an ecosystem of connectors, and responded more or less in real-time to things happening in the world outside your software. Immutable logs of events form a more robust backbone than the one-database-to-rule-them-all of your profound monolith past. Your stack is more evolvable, responsive, and easier to work with. However, you might face a challenge now that everything is a stream - how do you query things?
Although you may name at least one or two ways off the top of your head, it's time you think through how to make the choice. In this talk, we'll explore the solutions currently in use for asking questions about the contents of a topic, including Kafka Streams, the various streaming SQL implementations, your favorite relational database, your favorite data lake, and real-time analytics databases like Apache Pinot. There is no single correct answer to the question, so as responsible builders of systems, we must understand our options and the trade-offs they present to us.
You'll leave this talk even more satisfied that you've embraced Kafka as the heart of your system and are ready to deploy the right choice for querying the logs that hold your data.
Devnexus 2026 Sessionize Event
Jfokus 2026 Sessionize Event
KotlinConf 2025 Sessionize Event
Current London 2025 Sessionize Event
Iceberg Summit 2025 Sessionize Event
NDC Copenhagen 2022 Sessionize Event
swampUP 2022 Sessionize Event
Kafka Summit London 2022 Sessionize Event
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