Purvaja Narayanaswamy

When your consumers were dashboards, a stale number meant a wrong decision. When your consumers are agents, a stale number means a wrong action — at machine speed, across thousands of decisions per minute. AI-ready data isn't cleaner data. It is data that carries trust into every agent that consumes it. In this session, we will show how to build a continuous column-aware trust signal across your warehouse using native Snowflake Data Metric Functions, cross-platform lineage and Cortex, turning detection and root cause analysis into a closed loop that runs in minutes. We'll show how this signal becomes the runtime contract between your data and the agents that act on it.

Discover advanced strategies for implementing Apache Iceberg on AWS, focusing on Amazon S3 Tables and integration of Iceberg Rest Catalog with the lakehouse in Amazon SageMaker.

Chief data officers, data platform administrators, architects, owners, and consumers are looking to simplify data access permissions and governance. AWS Lake Formation makes it easier to centrally govern, secure, and globally share data for analytics and machine learning use cases.

AWS Lake Formation lets you manage, secure, and share data in data lakes in days. In this chalk talk, learn about the latest features and speak with experts on the best way to get the most out of Lake Formation.