Olena is a Staff Developer Advocate at Confluent and a recognized expert in data streaming and analytics. With two decades of experience in software engineering, she has built mission-critical applications, led high-performing teams, and driven large-scale technology adoption at industry leaders like Nokia, HERE Technologies, AWS, and Aiven.
A passionate advocate for real-time data processing and AI-driven applications, Olena empowers developers and organizations to use the power of streaming data. She is an AWS Community Builder, a dedicated mentor, and a volunteer instructor at a nonprofit tech school, helping to shape the next generation of engineers.
As an international speaker and thought leader, Olena regularly presents at top global conferences, sharing deep technical insights and hands-on expertise. Whether through her talks, workshops, or content, she is committed to making complex technologies accessible and inspiring innovation in the developer community.
Detecting problems as they happen is essential in today’s fast-moving world. This talk shows how to build a simple, powerful system for real-time anomaly detection. We’ll use Apache Kafka for streaming data, Apache Flink for processing it, and AI to find unusual patterns. Whether it’s spotting fraud, monitoring systems, or tracking IoT devices, this solution is flexible and reliable.
First, we’ll explain how Kafka helps collect and manage fast-moving data. Then, we’ll show how Flink processes this data in real time to detect events as they happen. We’ll also explore how to add AI to the pipeline, using pre-trained models to find anomalies with high accuracy. Finally, we’ll look at how Apache Iceberg can store past data for analysis and model improvements. Combining real-time detection with historical data makes the system smarter and more effective over time.
This talk includes clear examples and practical steps to help you build your own pipeline. It’s perfect for anyone who wants to learn how to use open-source tools to spot problems in real-time data streams.
Real-time data can be messy, unpredictable, and hard to manage. To unlock its full potential, you need a way to turn raw streams into clean, structured data. In this talk, we’ll show you how to use Apache Kafka, Apache Flink, and Apache Iceberg to organize real-time data streams efficiently and prepare them for advanced use cases, including AI applications.
We’ll start by explaining how Kafka handles high-speed data streams and how Flink processes these streams in real time. You’ll learn how to use Flink to transform raw data into structured formats, ensuring it’s ready for storage and analysis. Then, we’ll dive into Iceberg, demonstrating how it stores and organizes structured data for easy querying, versioning, and integration with machine learning pipelines.
Through clear examples, we’ll walk you through building a practical pipeline that turns chaotic data streams into organized schemas. By the end of the session, you’ll know how to manage real-time data effectively and set the stage for downstream AI and analytics. Whether you’re a beginner or an experienced developer, this talk will give you the tools to simplify and enhance your data pipelines!
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