Mohit Kumar

Standard MLOps stacks assume you are operating tens or hundreds of models. What happens when you need to train, validate, and serve thousands of forecasting models on a weekly cadence, producing millions of forecasts for a national energy portfolio?

As data ecosystems grow more complex, organizations often struggle with siloed platforms and fragmented governance. In this session, we’ll explore how our team made Databricks the central hub for cross-platform interoperability, enabling seamless Snowflake integration through Unity Catalog and the Iceberg REST API. We’ll cover: - Why interoperability matters and the business drivers behind our approach - How Unity Catalog and Uniform simplify interoperability, allowing Databricks to expose an Iceberg REST API for external consumption - Technical deep dive into data sharing, query performance, and access control across Databricks and Snowflake - Lessons learned and best practices for building a multi-engine architecture while maintaining governance and efficiency By leveraging Uniform, Delta, and Iceberg, we created a flexible, vendor-agnostic architecture that bridges Databricks and Snowflake without compromising performance or security.