Supercharge advanced analytics and AI insights on Databricks with accurate and consistent master data. This session explores how Informatica’s Master Data Management (MDM) integrates with Databricks to provide high-quality, integrated golden record data like customer, supplier, product 360 or reference data to support downstream analytics, Generative AI and Agentic AI. Enterprises can accelerate and de-risk the process of creating a golden record via a no-code/low-code interface, allowing data teams to quickly integrate siloed data and create a complete and consistent record that improves decision-making speed and accuracy.

Learn how visionaries from the world’s leading organizations use Moveworks to give employees a single place to find information, automate tasks, and be more productive. See the Moveworks AI Assistant in action and experience how its reasoning-based architecture allows it to be a one-stop-shop for all employee requests (across IT, HR, finance, sales, and more), how Moveworks empowers developers to easily build new AI agents atop this architecture, and how we give stakeholders tools to implement effective AI governance. Finally, experience how customers and partners alike leverage information in Databricks to supplement their employees' AI journeys.

Unlock the power of your SAP data with SAP Business Data Cloud—a fully managed SaaS solution that unifies and governs all SAP data while seamlessly connecting it with third-party data. As part of SAP Business Data Cloud, SAP Databricks brings together trusted, semantically rich business data with industry-leading capabilities in AI, machine learning, and data engineering. Discover how to access curated SAP data products across critical business processes, enrich and harmonize your data without data copies using Delta Sharing, and leverage the results across your business data fabric. See it all in action with a demonstration.

Unity Catalog (UC) enables governance and security for all data and AI assets within an enterprise’s data lake and is necessary to unlock the full potential of Databricks as a true Data Intelligence Platform. Unfortunately, UC migrations are non-trivial; especially for enterprises that have been using Databricks for more than five years, i.e., 7-Eleven. System Integrators (SIs) offer accelerators, guides, and services to support UC migrations; however, cloud infrastructure changes, anti-patterns within code, and data sprawl can significantly complicate UC migrations. There is no “shortcut” to success when planning and executing a complex UC migration. In this session, we will share how UCX by Databricks Labs, a UC Migration Assistant, allowed 7-Eleven to reorient their UC migration by leveraging assessments and workflows, etc., to assess, characterize, and ultimately plan a tenable approach for their UC migration.

Earlier this year, we finished migration of all dashboards from a traditional BI system to Databricks AI/BI ecosystem, resulting in annual savings of approximately $900,000. We also unlocked the below advantages: Data security, integrity and safety Cost savings Single source of truth Real-time data Genie space We will speak about our journey and how you can migrate your dashboards from traditional BI to AI/BI. Having listed the advantages above, we will also speak of some challenges faced. Migration steps: Analytical scope of dashboard inventory Feature mapping: From traditional BI to AI/BI Building bronze, silver and gold tables Building dashboards Migration shenanigans: Hypercare phase Change management KT documents Demo sessions Deprecation of licenses and dashboards on traditional BI tools We look forward to sharing these lessons learned and insights with you to help you streamline your BI infrastructure and unlock the full potential of Databricks AI/BI.

Whether you're using OpenAI, Anthropic or open-source models like Meta Llama, the Mosaic AI Gateway is the central control plane across any AI model or agent. Learn how you can streamline access controls, enforce guardrails for compliance, ensure an audit trail and monitor costs across providers — without slowing down innovation. Lastly, we’ll dive even deeper into how AI Gateway works with Unity Catalog to deliver a full governance story for your end-to-end AI agents across models, tools and data. Key takeaways: Centrally manage governance and observability across any LLM (proprietary or open-source) Give developers a unified query interface to swap, experiment and A/B test across models Attribute costs and usage to teams for better visibility and chargebacks Enforce enterprise-grade compliance with guardrails and payload logging Ensure production reliability with load balancing and fallbacks

What connects your lakehouse to real data intelligence? The answer: the catalog. But not just any catalog. In this session, we break down why Unity Catalog is purpose-built for the lakehouse, and how it goes beyond operational or business catalogs to deliver cross-platform interoperability and a shared understanding of your data. You’ll walk away with a clear view of how the right data foundation unlocks smarter decisions and trusted AI.

Struggling to implement traditional machine learning models that deliver real business value? Join us for a hands-on exploration of classical ML techniques powered by Databricks' Mosaic AI platform. This session focuses on time-tested approaches like regression, classification and clustering — showing how these foundational methods can solve real business problems when combined with Databricks' scalable infrastructure and MLOps capabilities. Key takeaways: Building production-ready ML pipelines for common business use cases including customer segmentation, demand forecasting and anomaly detection Optimizing model performance using Databricks' distributed computing capabilities for large-scale datasets Implementing automated feature engineering and selection workflows Establishing robust MLOps practices for model monitoring, retraining and governance Integrating classical ML models with modern data processing techniques

The Databricks Lakehouse for Public Sector is the only enterprise data platform that allows you to leverage all your data, from any source, on any workload to always offer better citizen services/warfighter support/student success with the best outcomes, at the lowest cost, with the greatest investment protection.

Databricks Clean Rooms make privacy-safe collaboration possible for data, analytics, and AI — across clouds and platforms. Built on Delta Sharing, Clean Rooms enable organizations to securely share and analyze data together in a governed, isolated environment — without ever exposing raw data. In this session, you’ll learn how to get started with Databricks Clean Rooms and unlock advanced use cases including: Cross-platform collaboration and joint analytics Training machine learning and AI models Enforcing custom privacy policies Analyzing unstructured data Incorporating proprietary libraries in Python and SQL notebooks Auditing clean room activity for compliance Whether you're a data scientist, engineer or data leader, this session will equip you to drive high-value collaboration while maintaining full control over data privacy and governance.

In this session, Joëlle van der Bijl, Chief Data & Analytics Officer at FrieslandCampina, shares the bold journey of replacing legacy data systems with a single, unified data, analytics, and AI platform built on Databricks. Rather than evolving gradually, the company took a leap: transforming its entire data foundation in one go. Today, this data-centric vision is delivering high-value impact: from optimizing milk demand and supply to enabling commercial AI prediction models and scaling responsible AI across the business. Learn how FrieslandCampina is using Databricks to blend tradition with innovation, and unlock a smarter, more sustainable future for dairy.

In this session, we’ll walk through the latest advancements in platform security and compliance on Databricks — from networking updates to encryption, serverless security and new compliance certifications across AWS, Azure and Google Cloud. We’ll also share our roadmap and best practices for how to securely configure workloads on Databricks SQL Serverless, Unity Catalog, Mosaic AI and more — at scale. If you're building on Databricks and want to stay ahead of evolving risk and regulatory demands, this session is your guide.

Databricks continues to redefine how organizations securely and openly collaborate on data. With new innovations like Clean Rooms for multi-party collaboration, Sharing for Lakehouse Federation, cross-platform view sharing and Databricks Apps in the Marketplace, teams can now share and access data more easily, cost-effectively and across platforms — whether or not they’re using Databricks. In this session, we’ll deliver live demos of key capabilities that power this transformation: Delta Sharing: The industry’s only open protocol for seamless cross-platform data sharing Databricks Marketplace: A central hub for discovering and monetizing data and AI assets Clean Rooms: A privacy-preserving solution for secure, multi-party data collaboration Join us to see how these tools enable trusted data sharing, accelerate insights and drive innovation across your ecosystem. Bring your questions and walk away with practical ways to put these capabilities into action today.

Picture the scene — you're exploring a deep, dark cave looking for insights to unearth when, in a burst of smoke, Genie appears and offers you not three but unlimited data wishes. This isn't a folk tale, it's the growing wave of Generative BI that is going to be a part of analytics platforms. Databricks Genie is a tool powered by a SQL-writing LLM that redefines how we interact with data. We'll look at the basics of creating a new Genie room, scoping its data tables and asking questions. We'll help it out with some complex pre-defined questions and ensure it has the best chance of success. We'll give the tool a personality, set some behavioural guidelines and prepare some hidden easter eggs for our users to discover. Generative BI is going to be a fundamental part of the analytics toolset used across businesses. If you're using Databricks, you should be aware of Genie, if you're not, you should be planning your Generative BI Roadmap, and this session will answer your wishes.

Databricks has been innovating the AI landscape, faster than ever before. Hear what's new directly from the Chief AI Scientist at Databricks, Jonathan Frankle, who co-founded MosaicML and now leads the AI research team.

Be first to witness the latest breakthroughs from Databricks and share the success of innovative data and AI companies.

The Virtue Foundation uses cutting-edge techniques in AI to optimize global health care delivery to save lives. With Unity Catalog as a foundation, they are using advanced Gen AI with model serving, vector search and MLflow to radically change how they map volunteer health resources with the right locations and facilities. Audio for this session is delivered in the conference mobile app, you must bring your own headphones to listen.

As machine learning (ML) models scale in complexity and impact, organizations must establish a robust MLOps foundation to ensure seamless model deployment, monitoring and retraining. In this session, we’ll share how we leverage Databricks as the backbone of our MLOps ecosystem — handling everything from workflow orchestration to large-scale inference. We’ll walk through our journey of transitioning from fragmented workflows to an integrated, scalable system powered by Databricks Workflows. You’ll learn how we built an automated pipeline that streamlines model development, inference and monitoring while ensuring reliability in production. We’ll also discuss key challenges we faced, lessons learned and best practices for organizations looking to operationalize ML with Databricks.

In this session, we'll explore how Rooms To Go enhances organizational collaboration by integrating AI/BI Genie with Microsoft Teams. Genie enables warehouse employees and members of the sales team to interact with data using natural language, simplifying data exploration and analysis. By connecting Genie to Microsoft Teams, we bring real-time data insights directly to a user’s phone. We'll provide a comprehensive overview on setting up this integration as well as a demo of how the team uses it daily. Attendees will gain practical knowledge to implement this integration, empowering their teams to access and interact with data seamlessly within Microsoft Teams.

We will present a framework for FinCrime detection leveraging Databricks lakehouse architecture specifically how institutions can achieve both data flexibility & ACID transaction guarantees essential for FinCrime monitoring. The framework incorporates advanced ML models for anomaly detection, pattern recognition, and predictive analytics, while maintaining clear data lineage & audit trails required by regulatory bodies. We will also discuss some specific improvements in reduction of false positives, improvement in detection speed, and faster regulatory reporting, delve deep into how the architecture addresses specific FATF recommendations, Basel III risk management requirements, and BSA compliance obligations, particularly in transaction monitoring and SAR. The ability to handle structured and unstructured data while maintaining data quality and governance makes it particularly valuable for large financial institutions dealing with complex, multi-jurisdictional compliance requirements.

As online gaming evolves, so do cheating methods that exploit client-server vulnerabilities. Traditional anti-cheat, such as kernel-level drivers and runtime detections, has long been the primary defense. However, advanced cheats like Direct Memory Access (DMA) exploits and AI-powered Computer Vision (CV) hacks increasingly render client-side detection ineffective. This presentation examines the escalating arms race between cheat creators and developers, highlighting client-side limitations. With CV cheats mimicking human behavior, anti-cheat must shift toward server-side, data-driven detection. By leveraging AI, machine learning, and behavioral analytics to analyze player patterns, input anomalies, and decision inconsistencies, future solutions can move beyond static detection to adaptive security models, ensuring fair play at scale. The session will also include real-life examples from Riot Games’ anti-cheat efforts, specifically insights and case studies from the development and operation of Riot Vanguard, to illustrate how these strategies are applied in practice.

Prada has developed a complex solution, leveraging MosaicAI to propose an interactive and natural language product discovery capability that could improve its e-commerce search bar. The backbone is a 70B model and a Vector Store, which collaborates with additional filterings and AI solutions to suggest not only the perfect outfit for each occasion, but also provide alternative solutions and similar items.

In the modern business landscape, AI and data strategies can no longer operate in isolation. To drive meaningful outcomes, organizations must align these critical components within a unified framework tied to overarching business objectives. This presentation explores the necessity of integrating AI and data strategies, emphasizing the importance of high-quality data, scalable architectures and robust governance. Attendees will learn three essential steps that need to be taken: Recognize that AI requires the right data to succeed Prioritize data quality and architecture Establish strong governance practices Additionally, the talk will highlight the cultural shift required to bridge IT and business silos, fostering roles that combine technical and business expertise. We’ll dive into specific practical steps that can be taken to ensure an organization has a cohesive and blended AI and data strategy, using specific case examples.

Crisp streamlines a brand’s data ingestion across 60+ retail sources, to build a foundation of sales and inventory intelligence on Databricks. Data is normalized and analysis-ready, and integrates seamlessly with AI tools - such as Databricks’ Genie and Blueprints. This session will provide an overview of the Crisp retail data platform and how our semantic layer, normalized and harmonized data sets can help drive powerful insights for supply chain, BI/Analytics, and data science teams.

goose is an open source AI agent framework that allows anyone to connect language model output to real world action. Released in January by Block (the company made up of Square, Cash App, Afterpay, and TIDAL), its use cases range from vibe coding to connecting all of the internal apps and services an enterprise uses. It can be powered by any language model that has tool calling capabilities.goose's modular design allows it to connect with any system through simple extensions. Built on the open Model Context Protocol (developed with Anthropic), goose transforms natural language into actions across various tools and services. Whether integrating with platforms like Jira and GitHub, or executing system commands and scripts, its plug-and-play architecture means anyone can extend Goose's capabilities to suit their needs.Finally, goose has both a command line interface and desktop app — it isn't limited to an IDE to start connecting to MCP servers and building powerful agentic workflows.