How enterprises can enable the Agentic AI Lakehouse on Apache Iceberg

"A flaw of warehouses is that you need to move all your data into them so you can keep it going, and for a lot of organisations that's a big hassle,” says Will Martin, EMEA Evangelist at Dremio. “It can take a long time, it can be expensive, and you ultimately can end up ripping up processes that are there." In this episode of the Don’t Panic It’s Just Data podcast, recorded live at Big Data LDN (BDL) 2025, Will Martin, EMEA Evangelist at Dremio, joins Shubhangi Dua, Podcast Host and Tech Journalist at EM360Tech. They talk about how enterprises can enable the Agentic AI Lakehouse on Apache Iceberg and why query performance is critical for efficient data analysis. "If you have a data silo, it exists for a reason—something's feeding information to it. You usually have other processes feeding off of it. So if you shift all that to a warehouse, it disrupts a lot of your business," Martin tells Dua. This is where a lakehouse comes into play. Organisations can federate their access through a lakehouse data approach. They can centralise access to the respective organisation’s lakehouse while keeping their data in its original location. Such a system helps people get started quickly. In terms of data quality, if you access everything from one location, even with separate data silos, you can see all your data. This visibility allows you to identify issues, address them, and enhance your data quality. That’s beneficial for AI, too, Martin explains. Key Takeaways Agentic AI and Apache Iceberg are current hot topics. Lakehouses offer quicker, less disruptive data access for AI compared to data warehouses. Centralised access in a lakehouse improves data quality and simplifies AI integration. Lakehouses, with their data catalogues, ease governance and permission management for AI agents working with sensitive data. Apache Iceberg is resolving metadata format issues, though metadata management remains an overhead. Dremio, an Iceberg-native provider, champions open source and interoperability, offering autonomous optimisation features to free engineers from mundane tasks. Beyond technology, a robust data strategy is crucial for organisational data improvement. Agentic AI will evolve to handle more delegated, multi-step tasks with less supervision. The open-source ecosystem will see consolidation and improved features, making advanced catalogue and governance tools widely available. Ultimately, for IT decision-makers, the quality of data is paramount for all analytical endeavours, including AI. Chapters 0:00 - Introduction to Agentic AI 0:35 - Discussing Big Data London, Hot Topics: Agentic AI and Apache Iceberg 1:37 - Data Lakehouse vs. Data Warehouse for AI 2:30 - Data Quality and AI with a Lakehouse 3:18 - AI Agents and Sensitive Data: Governance with a Lakehouse 4:19 - Challenges and Solutions in Lakehouse Technology (Apache Iceberg) 5:47 - Dremio's Use Cases and Interoperability 7:40 - Dremio's Standout Features and Autonomous Optimisation 9:39 - The Importance of Data Strategy 10:29 - Future of Agentic AI 11:34 - Future of the Open-Source Ecosystem 12:51 - Final Takeaway for IT Decision Makers: Data Quality is Critical 13:51 - Conclusion