Shift Left with Apache Iceberg Data Products to Power AI | Andrew Madson | Shift Left Data Conference 2025

High-quality, governed, and performant data from the outset is vital for agile, trustworthy enterprise AI systems. Traditional approaches delay addressing data quality and governance, causing inefficiencies and rework. Apache Iceberg, a modern table format for data lakes, empowers organizations to "Shift Left" by integrating data management best practices earlier in the pipeline to enable successful AI systems.

This session covers how Iceberg's schema evolution, time travel, ACID transactions, and Git-like data branching allow teams to validate, version, and optimize data at its source. Attendees will learn to create resilient, reusable data assets, streamline engineering workflows, enforce governance efficiently, and reduce late-stage transformations—accelerating analytics, machine learning, and AI initiatives.

Data DevOps applies rigorous software development practices—such as version control, automated testing, and governance—to data workflows, empowering software engineers to proactively manage data changes and address data-related issues directly within application code. By adopting a "shift left" approach with Data DevOps, SWE teams become more aware of data requirements, dependencies, and expectations early in the software development lifecycle, significantly reducing risks, improving data quality, and enhancing collaboration.

This session will provide practical strategies for integrating Data DevOps into application development, enabling teams to build more robust data products and accelerate adoption of production AI systems.