Will Martin

The modern enterprise is increasingly defined by the need for open, governed, and intelligent data access. This session explores how Apache Iceberg, Dremio, and the Model Context Protocol (MCP) come together to enable the Agentic Lakehouse. A data platform that is interoperable, high-performing, and AI-ready. We’ll begin with Apache Iceberg, which provides the foundation for data interoperability across teams and organisations, ensuring shared datasets can be reliably accessed and evolved. From there, we’ll highlight how Dremio extends Iceberg with turnkey governance, management, and performance acceleration, unifying your lakehouse with databases and warehouses under one platform. Finally, we’ll introduce MCP and showcase how innovations like the Dremio MCP server enable natural-language analytics on your data. With the power of Dremio’s built-in semantic layer, AI agents and humans alike can ask complex business questions in plain language and receive accurate, governed answers. Join us to learn how to unlock the next generation of data interaction with the Agentic Lakehouse.

Streaming data is a critical component of modern data architectures. This talk explores how to determine your streaming needs and design a robust solution using Apache Iceberg, a next-generation table format built for flexibility and scalability. We’ll dive into the foundational tools that enable streaming pipelines, including Apache Flink, Apache Kafka, Debezium, Kafka Connect, and Apache Spark, breaking down their roles and use cases in processing, transporting, and transforming streaming data. The talk will also highlight Iceberg-specific considerations, such as managing compaction to optimize query performance and dealing with delete files for handling record-level updates and deletes. Whether you’re building real-time analytics, powering machine learning models, or streaming raw data into your data lakehouse, this session will provide actionable insights and best practices for building reliable and efficient streaming workflows with Apache Iceberg.