Open Lakehouse and AI
Real-time databases are integrating with data lakes to reduce storage costs and share data with AI and data science. Please join us to hear from a range of experts as they share current problems and solutions while navigating the transition from closed storage models to open table formats like Apache Iceberg.
Join us in London for an evening with experts from Altinity, Confluent, and AWS. Networking to follow presentations. Food and drink provided!
A big thanks to TheTradeDesk for providing the venue! Thank you for London Technology Community for co-hosting!
Speakers
Robert Hodges, CEO @ Altinity
Olena Kutsenko, Staff Developer Advocate @ Confluent
Prachi Gupta, Sr. Data & AI Solution Architect @ AWS
Description of the Talks
Building a Foundation for AI with ClickHouse® and Apache Iceberg Storage - Robert Hodges, CEO @ Altinity.
AI applications need data. Lots of it. Altinity's Project Antalya is adapting open source ClickHouse® to introduce separation of compute and storage on shared Iceberg table data. The result: fast, cheap, flexible query that extends the life of real-time analytic applications and lays the foundation for handling new AI use cases on the same datasets. We cover architecture, performance results, roadmap, and how to get started yourself.
Teaching databases to speak human with LLMs and MCP - Olena Kutsenko, Staff Developer Advocate @ Confluent
Imagine asking your database a question in plain English - and getting the right answer back, instantly. Thanks to advances in large language models (LLMs), this is now a practical reality. Natural Language to SQL (NL2SQL) systems are making data more accessible than ever, helping teams move faster without writing a single query by hand. In this talk, we’ll walk through the key building blocks that make it possible for LLMs to "talk" to databases. We’ll start with natural language: how NL2SQL systems understand what the user is asking, map questions to the right parts of a database, and generate executable SQL. Of course, this is easier said than done. Natural language is full of ambiguity, and many databases have complex schemas, tricky joins, and domain-specific terms. But despite these challenges, benchmarks like Spider and BIRD show just how far we've come in the past decade. Next, we’ll introduce the Model Context Protocol (MCP) - a way to give LLMs access to metadata, table relationships, and tools for query execution. Instead of guessing, the model can reason step-by-step using chain-of-thought, consult the schema, and run sub-queries to reach the right result. Whether you're an engineer building LLM-powered interfaces or a data leader exploring self-serve analytics, this session will give you a clear view of how natural language is reshaping the way we interact with data and how to start using it in your stack today.
Managing Apache Iceberg Tables with Amazon S3: High Performance and Interoperability at Scale - Prachi Gupta, Sr. Data & AI Solution Architect @ AWS
Amazon S3 Tables delivers a fully managed storage solution for Apache Iceberg data lakes, offering performance and seamless interoperability across analytics platforms. We'll dive into how S3 Tables provides 10x faster transactions compared to standard S3 buckets while maintaining vendor-agnostic compatibility through the Iceberg REST Catalog (IRC). The session covers key features including automated maintenance, intelligent compaction strategies, and flexible integration options that enable both AWS native services and third-party applications to work seamlessly with the same datasets. Learn how organizations can leverage S3 Tables to build high-performance data lakes without sacrificing interoperability or getting locked into proprietary formats. Real-world examples will demonstrate how enterprises are using S3 Tables to manage massive datasets while maintaining open standards compliance and cross-platform accessibility.
Description of the Presenters
Robert Hodges - Robert is the CEO of Altinity, an enterprise provider for ClickHouse data warehouse. He's also a database geek with experience on at least 20 DBMS types. Robert caught the Kubernetes bug at VMware in 2018.
Connect with Robert on LinkedIn.
Olena Kutsenko - 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.
Connect with Olena on LinkedIn.
Prachi Gupta - Prachi is a Senior Data & AI Solution Architect at AWS, specializing in designing and implementing large-scale data infrastructure and data migration solutions. She has extensive experience in building robust data management systems, with a focus on integrating storage with analytical and AI platforms. Prachi helps customers modernize their data platforms on AWS, leveraging the latest services and technologies with her current focus on Amazon S3 and the S3 Table feature, which enables efficient management and querying of structured data at scale. She has worked on creating solution for migration of iceberg &hive data to S3 Tables, multiple workshops focusing on S3 Tables use cases along with testing the product and its performance.
Connect with Prachi on LinkedIn.