Olena Kutsenko

Imagine asking "What changed in the last few minutes, and who or what is affected?" and getting an answer based on live data, with events connected, context built in real time, and structured sources you can trust. This is where the Real-time Context Engine meets the Streamhouse: turning constantly changing data into context that LLM-powered agents can explore, query, and reason over. But it takes more than just plugging an LLM into a data source. We'll follow events from where they're created all the way into the Streamhouse, where schemas help us make sense of the data and manage changes. We'll explore when to append events to keep the full history and when to upsert records to maintain the latest state. Then we'll see how stream processing turns all of this into queryable tables, powered by a low-latency query engine. Next, we'll see what happens when a user asks a question. An LLM-powered agent can discover available data, explore schemas and metadata, and run bounded queries using tools like Model Context Protocol. We'll break down how it all fits together—and what it takes to make it work reliably in production: handling late events, access controls, query limits, failures,…

Real-time data is awesome… until you realize it’s leaking names, emails, and locations. In this talk, you’ll learn how to keep streaming data private, from simple masking to tricks that beat re-identification. All with live demos and some juicy real-world stories.

We all love real-time data — clicks, payments, rides, messages — but most of it comes with a catch: it contains personal information we’re not supposed to leak, such as names, emails, locations, or even small clues that can identify someone. The challenge: how do we keep streaming data useful and safe at the same time? In this talk, we’ll explore practical ways to protect privacy in streaming systems using Apache Kafka, Apache Flink, and Apache Iceberg. We’ll cover: - simple tricks like masking and tokenizing PII; - why “anonymous” data often isn’t anonymous (the re-identification problem); - techniques like bucketing, k-anonymity, and adding noise; - how to balance privacy with data utility (too much hiding makes data useless). Along the way, we’ll look at real-world stories: from public data leaks to surprising deanonymization attacks, and show live demos of pipelines that anonymize data before it’s written to storage. If you’ve ever wondered how to build privacy-aware pipelines, this talk will give you practical patterns you can use right away.

Data streaming is powering everything from fraud detection and real-time analytics to patient monitoring and order fulfillment. But as the role of streaming grows, so does the risk - because many streaming platforms, like Apache Kafka, aren't secure by default.

Data streaming is powering everything from fraud detection and real-time analytics to patient monitoring and order fulfillment. But as the role of streaming grows, so does the risk - because many streaming platforms, like Apache Kafka, aren't secure by default.

Storytelling has always been a way to connect and imagine new worlds. Now, with Generative Agents - AI-powered characters that can think, act, and adapt - we can take storytelling to a whole new level. But what if these agents could change and grow in real time, driven by live data streams? Inspired by the Standford's paper Generative Agents: Interactive Simulacra of Human Behavior, this session explores how to build dynamic, AI-driven worlds using Apache Kafka, Apache Flink, and Apache Iceberg. We'll use a Large Language Model to power for conversation and agent decision-making, integrate Retrieval-Augmented Generation (RAG) for memory storage and retrieval, and use JavaScript to tie it all together. Along the way, we’ll examine different approaches for data processing, storage, and analysis. By the end, you’ll see how data streaming and AI can work together to create lively, evolving virtual communities. Whether you’re into gaming, simulations, research or just exploring what’s possible, this session will give you ideas for building something amazing.

Inspired by Stanford's paper "Generative Agents: Interactive Simulacra of Human Behavior", this session explores how to build dynamic, AI-driven worlds using Apache Kafka and Apache Flink.

Inspired by Stanford's paper "Generative Agents: Interactive Simulacra of Human Behavior", this session explores how to build dynamic, AI-driven worlds using Apache Kafka and Apache Flink.

Explore how AI-powered Generative Agents can evolve in real time using live data streams. Inspired by Stanford's 'Generative Agents' paper, this session dives into building dynamic, AI-driven worlds with Apache Kafka, Flink, and Iceberg - plus LLMs, RAG, and Python.

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.

AI-powered agent systems are becoming essential for automation, personalization, and real-time decision-making. But how do we ensure that these agents can process information continuously, maintain context, and provide intelligent responses at scale? This talk explores how Apache Kafka and Apache Flink can be used to build dynamic real-time agent systems. We'll start with the basics of agent-based systems - how they work, how they communicate, and how they retrieve and generate relevant knowledge using Retrieval-Augmented Generation. Then, we'll look into real-time streaming architectures, showing how Kafka handles message passing between agents and Flink processes events to track context and enable intelligent responses. By the end of this session, you'll have a clear roadmap for designing AI-driven agent systems that are context-aware, efficient and work with a continuous stream of data. Whether you're working on chatbots, monitoring systems, or intelligent automation, this talk will provide practical insights into bridging streaming data with generative AI to power the next generation of autonomous agents. Perfect for beginners and experts alike, this session offers valuable…

You’re curious about what Apache Kafka does and how it works, but between the terminology and explanations that seem to start at a complex level, it's been difficult to embark.

Any conversation about Big Data would be incomplete without talking about Apache Kafka and Apache Flink: the winning open source combination for high-volume streaming data pipelines. In this talk we'll explore how moving from long running batches to streaming data changes the game completely.

Working with Big Data means that we need tools to organise and understand the data. And you don’t have to be a developer to search, aggregate and visualise your data.