Nuri Halperin

Step into the world of stream processing, where events arrive sporadically and timing matters.

This talk explores managing the journey and lifetime of an events during stream processing. We will discuss data events from ingestion to output and examine what happens along the way.

Using a movie theater metaphor, we will explain key ideas like time windows, late arrivals, and dead letter queues. The talk connects high-level concepts to practical implementation notes. You will leave with a clear and useful mental model for working with real-time data.

If you’re a practitioner, developer, or data person, you’re probably being told to “just use AI”. But what does that actually mean?

What is “AI” really? Is it just ChatGPT, Claude, or Canva drawing pictures for you? 

This talk maps out the main building blocks of modern AI. I’ll discuss models and model architecture, training, embeddings, tokens, parameters, agents, RAG, hallucinations, “agentic” systems, and a few other terms you keep hearing.

Importantly, we’ll look at how these pieces are actually put together in real systems: where they’re genuinely useful, where they’re fragile, and where they’re oversold.

You’ll come out of the talk with a solid mental picture of how these ideas connect, what’s behind the buzzwords, and enough understanding to engage in a more informed way on your won AI journey.

Is the term "AI" obfuscating every practical conversation about everything? Can we cut through the noise and ride this wave by understanding, designing, and deploying a new breed of search, fully integrated with your existing data and database? Yes, yes, we can!

This talk will dig into the weeds of vector search. We'll explore both the core math of the technology, as well as the applicability to a wide variety of use cases. We'll differentiate it from preceding search technologies and find how it fits within an overall data retrieval strategy.

Generative AI models are built on data - but not on yourdata.

So how can you use your own proprietary source of truth with these "generic" models which don't know about your own data?

This session will explain how to ingest and leverage your own data in your own database to answer natural next questions.

We'll talk about generative AI models and theory, as well as how to put it all together for your own needs. You will learn to exploit the power of AI without the expensive training and building of full-blown models.

Devs, DBAs, Architects, Analysts - all are welcome!