Unity Catalog support for Apache Iceberg™ brings open, interoperable table formats to the heart of the Databricks Lakehouse. In this session, we’ll introduce new capabilities that allow you to write Iceberg tables from any REST-compatible engine, apply fine-grained governance across all data, and unify access to external Iceberg catalogs like AWS Glue, Hive Metastore, and Snowflake Horizon. Learn how Databricks is eliminating data silos, simplifying performance with Predictive Optimization, and advancing a truly open lakehouse architecture with Delta and Iceberg side by side.

Bagelcode, a leader in the social casino industry, has utilized Databricks since 2018 and manages over 10,000 tables via Hive Metastore. In 2024, we embarked on a transformative journey to resolve inefficiencies and unlock new capabilities. Over five months, we redesigned ETL pipelines with Delta Lake, optimized partitioned table logs and executed a seamless migration with minimal disruption. This effort improved governance, simplified management and unlocked Unity Catalog’s advanced features. Post-migration, we integrated the Genie Room with Slack to enable natural language queries, accelerating decision-making and operational efficiency. Additionally, a lineage-powered internal tool allowed us to quickly identify and resolve issues like backfill needs or data contamination. Unity Catalog has revolutionized our data ecosystem, elevating governance and innovation. Join us to learn how Bagelcode unlocked its data’s full potential and discover strategies for your own transformation.

American Airlines migrated from Hive Metastore to Unity Catalog using automated processes with Databricks APIs and GitHub Actions. This automation streamlined the migration for many applications within AA, ensuring consistency, efficiency and minimal disruption while enhancing data governance and disaster recovery capabilities.

This session showcases how both Westinghouse Electric and Alabama Power Company are leveraging cloud-based tools, advanced analytics, and machine learning to transform operational resilience across the energy sector. In the first segment, we'll explore AI's crucial role in enhancing safety, efficiency, and compliance in nuclear operations through technologies like HiVE and Bertha, focusing on the unique reliability and credibility requirements of the regulated nuclear industry. We’ll then highlight how Alabama Power Company has modernized its grid management and storm preparedness by using Databricks to develop SPEAR and RAMP—applications that combine real-time data and predictive analytics to improve reliability, efficiency, and customer service.

In this session we will explore the revolutionary advancements in nuclear AI capabilities with HiVE and Bertha on Databricks architecture. HiVE, developed by Westinghouse, leverages over a century of proprietary data to deliver unparalleled AI capabilities. At its core is Bertha, a generative AI model designed to tackle the unique challenges of the nuclear industry. This session will delve into the technical architecture of HiVE and Bertha, showcasing how Databricks' scalable environment enhances their performance. We will discuss the secure data infrastructure supporting HiVE, ensuring data integrity and compliance. Real-world applications and use cases will demonstrate the impact of HiVE and Bertha on improving efficiency, innovation and safety in nuclear operations. Discover how the fusion of HiVE and Bertha with Databricks architecture is transforming the nuclear AI landscape and driving the future of nuclear technology.

In today's data landscape, organizations often grapple with fragmented data spread across various databases, data warehouses and catalogs. Lakehouse Federation addresses this challenge by enabling seamless discovery, querying, and governance of distributed data without the need for duplication or migration. This session will explore how Lakehouse Federation integrates external data sources like Hive Metastore, Snowflake, SQL Server and more into a unified interface, providing consistent access controls, lineage tracking and auditing across your entire data estate. Learn how to streamline analytics and AI workloads, enhance compliance and reduce operational complexity by leveraging a single, cohesive platform for all your data needs.

You shouldn’t have to sacrifice data governance just to leverage the tools your business needs. In this session, we will give practical tips on how you can cut through the data sprawl and get a unified view of your data estate in Unity Catalog without disrupting existing workloads. We will walk through how to set up federation with Glue, Hive Metastore, and other catalogs like Snowflake, and show you how powerful new tools help you adopt Databricks at your own pace with no downtime and full interoperability.

This presentation outlines Rabobank Credit analytics transition to a secure, audit-ready data architecture using Unity Catalog (UC), addressing critical regulatory challenges in credit analytics for IRB and IFRS9 regulatory modeling. Key technical challenges included legacy infrastructure (Hive metastore, ADLS mounts using Service Principals and Credential passthrough) lacking granular access controls, data access auditing and limited visibility into lineage, creating governance and compliance gaps. Details cover a framework for phased migration to UC. Outcomes include data lineage mapping demonstrating compliance with regulatory requirements, granular role based access control and unified audit trails. Next steps involve a lineage visualization toolkit (custom app for impact analysis and reporting) and lineage expansion to incorporate upstream banking systems.