Nestlé USA, a division of the world’s largest food and beverage company, Nestlé S.A., has embarked on a transformative journey to unlock GenAI capabilities on their data platform. Deloitte, Databricks, and Nestlé have collaborated on a data platform modernization program to address gaps associated with Nestlé’s existing data platform. This joint effort introduces new possibilities and capabilities, ranging from development of advanced machine learning models, implementing Unity Catalog, and adopting Lakehouse Federation, all while adhering to confidentiality protocols. With help from Deloitte and Databricks, Nestlé USA is now able to meet its advanced enterprise analytics and AI needs with the Databricks Data Intelligence Platform.

Domo's Databricks integration seamlessly connects business users to both Delta Lake data and AI/ML models, eliminating technical barriers while maximizing performance. Domo's Cloud Amplifier optimizes data processing through pushdown SQL, while the Domo AI Services layer enables anyone to leverage both traditional ML and large language models directly from Domo. During this session, we’ll explore an AI solution around fraud detection to demonstrate the power of leveraging Domo on Databricks.

In the rapidly evolving landscape of pharmaceuticals, the integration of AI and GenAI is transforming how organizations operate and deliver value. We will explore the profound impact of the AI program at Takeda Pharmaceuticals and the central role of Databricks. We will delve into eight pivotal AI/GenAI use cases that enhance operational efficiency across commercial, R&D, manufacturing, and back-office functions, including these capabilities: Responsible AI Guardrails: Scanners that validate and enforce responsible AI controls on GenAI solutions Reusable Databricks Native Vectorization Pipeline: A scalable solution enhancing data processing with quality and governance One-Click Deployable RAG Pattern: Simplifying deployment for AI applications, enabling rapid experimentation and innovation AI Asset Registry: A repository for foundational models, vector stores, and APIs, promoting reuse and collaboration

To meet the growing internal demand for accessible, reliable data, TradeStation migrated from fragmented, spreadsheet-driven workflows to a scalable, self-service analytics framework powered by Sigma on Databricks. This transition enabled business and technical users alike to interact with governed data models directly on the lakehouse, eliminating data silos and manual reporting overhead. In brokerage trading operations, the integration supports robust risk management, automates key operational workflows, and centralizes collaboration across teams. By leveraging Sigma’s intuitive interface on top of Databricks’ scalable compute and unified data architecture, TradeStation has accelerated time-to-insight, improved reporting consistency, and empowered teams to operationalize data-driven decisions at scale.

Rheem's journey from a fragmented data landscape to a robust, scalable data platform powered by Databricks showcases the power of data modernization. In just 1.5 years, Rheem evolved from siloed reporting to 30+ certified data products, integrated with 20+ source systems, including MDM. This transformation has unlocked significant business value across sales, procurement, service and operations, enhancing decision-making and operational efficiency. This session will delve into Rheem's implementation of Databricks, highlighting how it has become the cornerstone of rapid data product development and efficient data sharing across the organization. We will also explore the upcoming enhancements with Unity Catalog, including the full migration from HMS to UC. Attendees will gain insights into best practices for building a centralized data platform, enhancing developer experience, improving governance capabilities as well as tips and tricks for a successful UC migration and enablement.

The Databricks Data Intelligence Platform and Lakeflow Connect have transformed how Porsche manages and uses its customer data. By opting to use Lakeflow Connect instead of building a custom solution, the company has reaped the benefits of both operational efficiency and cost management. Internally, teams at Porsche now spend less time managing data integration processes. “Lakeflow Connect has enabled our dedicated CRM and Data Science teams to be more productive as they can now focus on their core work to help innovate, instead of spending valuable time on the data ingestion integration with Salesforce,” says Gruber. This shift in focus is aligned with broader industry trends, where automotive companies are redirecting significant portions of their IT budgets toward customer experience innovations and digital transformation initiatives. This story was also shared as part of a Databricks Success Story — Elise Georis, Giselle Goicochea.

This presentation outlines the evolution of Databricks and its integration with cloud analytics at Edward Jones. It focuses on the transition from Cloud V1.x to Cloud V2.0, which highlights the challenges faced with initial setup, Unity Catalog implementation and the improvements planned for the future particularly in terms of Data Cataloging, Architecture and Disaster Recovery. Highlights: Cloud Analytics Journey Current Setup (Cloud V1.x) Utilizes Medallion architecture customized to Edward Jones need. Challenges & limitations identified with integration, limited catalogs, Disaster Recovery etc. Cloud V2.0 Enhancements Modifications in storage and compute in Medallion layers Next level integration with enterprise suites Disaster Recovery readiness Future outlook

We’re introducing a new developer experience for Lakeflow Declarative Pipelines designed for data practitioners who prefer a code-first approach and expect robust developer tooling. The new multi-file editor brings an IDE-like environment to declarative pipeline development, making it easy to structure transformation logic, configure pipelines throughout the development lifecycle and iterate efficiently.Features like contextual data previews and selective table updates enable step-by-step development. UI-driven tools, such as DAG previews and DAG-based actions, enhance productivity for experienced users and provide a bridge for those transitioning to declarative workflows.In this session, we’ll showcase the new editor in action, highlighting how these enhancements simplify declarative coding and improve development for production-ready data pipelines. Whether you’re an experienced developer or new to declarative data engineering, join us to see how Lakeflow Declarative Pipelines can enhance your data practice.

Building a custom Spark data source connector once required Java or Scala expertise, making it complex and limiting. This left many proprietary data sources without public SDKs disconnected from Spark. Additionally, data sources with Python SDKs couldn't harness Spark’s distributed power. Spark 4.0 changes this with a new Python API for data source connectors, allowing developers to build fully functional connectors without Java or Scala. This unlocks new possibilities, from integrating proprietary systems to leveraging untapped data sources. Supporting both batch and streaming, this API makes data ingestion more flexible than ever. In this talk, we’ll demonstrate how to build a Spark connector for Excel using Python, showcasing schema inference, data reads/writes and streaming support. Whether you're a data engineer or Spark enthusiast, you’ll gain the knowledge to integrate Spark with any data source — entirely in Python.

We’re excited to share with you how SAP Business Data Cloud supports Delta Sharing to share SAP data securely and seamlessly with Databricks—no complex ETL or data duplication required. This enables organizations to securely share SAP data for analytics and AI in Databricks while also supporting bidirectional data sharing back to SAP.In this session, we’ll demonstrate the integration in action, followed by a discussion of how the global beauty group, Natura, will leverage this solution. Whether you’re looking to bring SAP data into Databricks for advanced analytics or build AI models on top of trusted SAP datasets, this session will show you how to get started — securely and efficiently.

Unlock the truth behind data modeling in Databricks. This session will tackle the top 10 myths surrounding relational and dimensional data modeling. Attendees will gain a clear understanding of what Databricks Lakehouse truly supports today, including how to leverage primary and foreign keys, identity columns for surrogate keys, column-level data quality constraints and much more. This session will talk through the lens of medallion architecture, explaining how to implement data models across bronze, silver, and gold tables. Whether you’re migrating from a legacy warehouse or building new analytics solutions, you’ll leave equipped to fully leverage Databricks’ capabilities, and design scalable, high-performance data models for enterprise analytics.

In this presentation, we'll show how we achieved a unified development experience for teams working on Mercedes-Benz Data Platforms in AWS and Azure. We will demonstrate how we implemented Azure to AWS and AWS to Azure data product sharing (using Delta Sharing and Cloud Tokens), integration with AWS Glue Iceberg tables through UniForm and automation to drive everything using Azure DevOps Pipelines and DABs. We will also show how to monitor and track cloud egress costs and how we present a consolidated view of all the data products and relevant cost information. The end goal is to show how customers can offer the same user experience to their engineers and not have to worry about which cloud or region the Data Product lives in. Instead, they can enroll in the data product through self-service and have it available to them in minutes, regardless of where it originates.

In this session, we’ll explore the data intelligence capabilities within Databricks, focusing on Predictive Optimization. This feature enhances the performance of Unity Catalog managed tables by automatically optimizing data layouts, resulting in improved query performance and reduced storage costs. You’ll learn how Predictive Optimization works and see real-world examples of customers using it to fully automate data layout management. We’ll also share a preview of the exciting features and enhancements coming down the road.

Join us as we introduce Delta-Kernel-RS, a new Rust implementation of the Delta Lake protocol designed for unparalleled interoperability across query engines. In this session, we will explore how maintaining a native implementation of the Delta specification — with native C and C++ FFI support — can deliver consistent benefits across diverse data processing systems, eliminating the need for repetitive, engine-specific reimplementations. We will dive deep into a real-world case study where a query engine harnessed Delta-Kernel-RS to unlock significant data skipping improvements — enhancements achieved “for free” by leveraging the kernel. Attendees will gain insights into the architectural decisions, interoperability strategies and the practical impact of this innovation on performance and development efficiency in modern data ecosystems.

Enterprises generate massive amounts of unstructured data — from support tickets and PDFs to emails and product images. But extracting insight from that data requires brittle pipelines and complex tools. Databricks AI Functions make this simpler. In this session, you’ll learn how to apply powerful language and vision models directly within your SQL and ETL workflows — no endpoints, no infrastructure, no rewrites. We’ll explore practical use cases and best practices for analyzing complex documents, classifying issues, translating content, and inspecting images — all in a way that’s scalable, declarative, and secure. What you’ll learn: How to run state-of-the-art LLMs like GPT-4, Claude Sonnet 4, and Llama 4 on your data How to build scalable, multimodal ETL workflows for text and images Best practices for prompts, cost, and error handling in production Real-world examples of GenAI use cases powered by AI Functions

Discover how Adobe is redefining its Data Supply Chain through an AI-first, agentic solution that transforms the entire Data Development and Delivery Lifecycle (DDLC). This next-generation engineering workbench empowers data engineers, analysts, and practitioners with intelligent automation, context-aware assistance, and seamless collaboration to accelerate and streamline every phase of the data supply chain — from capturing business intent and sourcing data to building pipelines, validating quality, and delivering trusted, actionable insights.

In this session, you’ll gain actionable insights to modernize your security operations and strengthen cyber resilience. Arctic Wolf will highlight how they eliminated data silos & enhanced their MDR pipeline to investigate suspicious threat actors for customers using Databricks.

Blue Origin is revolutionizing space exploration with a mission-critical data strategy powered by Databricks on AWS GovCloud. Learn how they leverage Databricks to meet ITAR and FedRAMP High compliance, streamline manufacturing and accelerate their vision of a 24/7 factory. Key use cases include predictive maintenance, real-time IoT insights and AI-driven tools that transform CAD designs into factory instructions. Discover how Delta Lake, Structured Streaming and advanced Databricks functionalities like Unity Catalog enable real-time analytics and future-ready infrastructure, helping Blue Origin stay ahead in the race to adopt generative AI and serverless solutions.

Feastables, founded by YouTube sensation MrBeast, partnered with Engine to build a modern, AI-enabled BI ecosystem that transforms complex, disparate data into actionable insights, driving smarter decision-making across the organization. In this session, learn how Engine, a Built-On Databricks Partner, brought expertise combined with strategic partnerships that enabled Feastables to rapidly stand up a secure, modern data estate to unify complex internal and external data sources into a single, permissioned analytics platform. Feastables unlocked the power of cross-functional collaboration by democratizing data access throughout their enterprise and seamlessly integrating financial, retailer, supply chain, syndicated, merchandising and e-commerce data. Discover how a scalable analytics framework combined with advanced AI models and tools empower teams with Smarter BI across sales, marketing, supply chain, finance and executive leadership to enable real-time decision-making at scale.

FedEx, a global leader in transportation and logistics, faced a common challenge in the era of big data: how to democratize data and foster data-driven decision making with thousands of data practitioners at FedEx wanting to build models, get real-time insights, explore enterprise data, and build enterprise-grade solutions to run the business. This breakout session will highlight how FedEx overcame challenges in data governance and security using Unity Catalog, ensuring that sensitive information remains protected while still allowing appropriate access across the organization. We'll share their approach to building intuitive self-service interfaces, including the use of natural-language processing to enable non-technical users to query data effortlessly. The tangible outcomes of this initiative are numerous, but chiefly: increased data literacy across the company, faster time-to-insight for business decisions, and significant cost-savings through improved operational efficiency.

Storage and processing costs of your legacy Teradata data warehouses impact your ability to deliver. Migrating your legacy Teradata data warehouse to the Databricks Data Intelligence Platform can accelerate your data modernization journey. In this session, learn the top strategies for completing this data migration. We will cover data type conversion, basic to complex code conversions, validation and reconciliation best practices. How to use Databricks natively hosted LLMs to assist with migration activities. See before-and-after architectures of customers who have migrated, and learn about the benefits they realized.

AI/BI Genie has transformed self-service analytics for the Databricks Marketing team. This user-friendly conversational AI tool empowers marketers to perform advanced data analysis using natural language — no SQL required. By reducing reliance on data teams, Genie increases productivity and enables faster, data-driven decisions across the organization. But realizing Genie’s full potential takes more than just turning it on. In this session, we’ll share the end-to-end journey of implementing Genie for over 200 marketing users, including lessons learned, best practices and the real business impact of this Databricks-on-Databricks solution. Learn how Genie democratizes data access, enhances insight generation and streamlines decision-making at scale.

Most enterprise data is trapped in unstructured formats — documents, PDFs, scanned images and tables — making it difficult to access, analyze and use. This session shows how to unlock that hidden value by building intelligent document processing workflows on the Databricks Data Intelligence Platform. You’ll learn how to ingest unstructured content using Lakeflow Connect, extract structured data with AI Parse — even from complex tables and scanned documents — and apply analytics or AI to this newly structured data. What you’ll learn: How to build scalable pipelines that transform unstructured documents into structured tables Techniques for automating document workflows with Databricks tools Strategies for maintaining quality and governance with Unity Catalog Real-world examples of AI applications built with intelligent document processing

Ever struggled with getting AI to work effectively for your specific domain needs? Join us to discover how establishing the right data foundation transforms the way you build and deploy specialized AI agents. This session demonstrates how to prepare and structure your information assets to enable more powerful, efficient AI applications. Learn proven approaches for extracting value from both structured and unstructured data, creating knowledge bases that serve as the backbone for domain-specific agents, and implement optimization techniques that balance quality and resource constraints.Key takeaways: Preparing diverse data sources for AI consumption and knowledge extraction Building scalable data foundations that naturally facilitate agent development Optimizing domain-specific agents for performance and cost efficiency Implementing practical governance frameworks for AI systems built on your data

Join us to explore Lakeflow, Databricks' end-to-end solution for simplifying and unifying the most complex data engineering workflows. This session builds on keynote announcements, offering an accessible introduction for newcomers while emphasizing the transformative value Lakeflow delivers.We’ll cover: What is Lakeflow? – A cohesive overview of its components: Lakeflow Connect, Lakeflow Declarative Pipelines, and Lakeflow Jobs. Core Capabilities in Action – Live demos showcasing no-code data ingestion, code-optional declarative pipelines, and unified, end-to-end orchestration. Vision for the Future – Unveil the roadmap, introducing no-code and open-source initiatives. Discover how Lakeflow equips data teams with a seamless experience for ingestion, transformation, and orchestration, reducing complexity and driving productivity. By unifying these capabilities, Lakeflow lays the groundwork for scalable, reliable, efficient data pipelines in a governed and high-performing environment.