Diego Ortiz

In the agentic AI era, the same modern lakehouse is also your AI-ready foundation: the quality, openness, and organization of your data is what separates successful AI outcomes. Apache Iceberg on Amazon S3 Tables gives you that true portability and engine interoperability to build your AI-ready data foundation.

This session shares the journey of implementing a hybrid Data Mesh architecture within a multi-company holding, balancing centralization and decentralization needs.

Apache Iceberg is designed for interoperability across compute engines without the need for data duplication or format conversions. Amazon SageMaker's lakehouse architecture simplifies data interoperability through its shared metadata capabilities and support for the REST API specification.

A data mesh is a distributed, domain-driven architecture that defines responsibilities and coordination across separate domain teams and data products. A data mesh requires data governance to ensure data quality, consistency, security, and trust across domains.

In this chalk talk, learn how users of all skill levels, including data engineers, ETL developers, and business users, can use the latest generative AI capabilities to onboard, author, and tune data-integration applications using natural language, without needing in-depth knowledge of the underlying systems.