Technical debt in data systems can sink innovation faster than an iceberg—rigid schemas, unreliable updates, and sprawling data estates, unmanageable data lakes that hinder developers from building efficiently. In this session, we'll explore Apache Iceberg, an open-source table format that's empowering data engineers to create a culture shift: turning passive data storage into proactive, maintainable assets.Designed for beginners with real depth, we'll demystify Iceberg without jargon, explaining how it tackles common debt pitfalls like inconsistent data versions and slow analytics. Through live demos, you'll see auto-refactoring in action—seamless schema evolution, time-travel queries for auditing past states, and scalable analysis that makes updating petabyte-scale datasets as easy as editing a document. We'll cover why it's being adopted en masse by teams at Netflix, Adobe, and beyond, not just for performance gains, but for fostering a developer culture where impacting debt becomes routine.Attendees will leave with actionable strategies to re-invigorate their own codebases, including integration tips with tools like Spark and Hive, and a roadmap for starting small to drive big changes. Join us to learn how Iceberg isn't just a format—it's a catalyst for debt reduction and future-proof innovation.