Join us at the Media & Advertising Forum to explore how data and AI are transforming media and advertising from content to creative and identity to outcomes. Featuring innovators from leading agencies, platforms, streamers and ad tech — plus exciting announcements from Databricks — this session delivers must-have insights for industry leaders and change agents. What to expect: Business imperatives: Learn how media and advertising organizations navigate and grow through secular change leveraging Data + AI. Databricks leaders and customers will share how data intelligence is powering the next wave of innovation Future trends in Media & Advertising: Learn how data + AI will continue to shape the future of monetization, real-time personalization and intelligent storytelling Customer success stories: Discover how top media and advertising companies are using data intelligence to unlock new creative potential, increase agility and grow revenue

Join the 60-minute kickoff session at the Public Sector Forum for an opportunity to to accelerate innovation into your enterprise through governance, compliance and GenAI. Featuring keynotes from data-driven agency leaders and providing a future-looking journey from Databricks, this event offers invaluable insights. Understand the outcomes of Data and AI powering transformation across common areas of government and beyond: Improving constituent experience Reducing cost and enhancing services Identifying fraud, waste and abuse Achieving scale and security You will not want to miss this exclusive opportunity to own your data and eliminate government silos. Discover the Data + AI Company with deep compliance experience and widespread adoption.

Consumer industries are being transformed by AI as physical and digital experiences converge. In this flagship session for retail, travel, restaurants and consumer goods attendees at Data + AI Summit, Databricks and a panel of industry leaders will explore how real-time data and machine learning are enabling brands to gain deeper consumer insights, personalize interactions and move closer to true 1:1 marketing. From AI agents shopping on behalf of consumers to consumer-centric supply chains, discover how the most innovative companies will use AI to reshape customer relationships and drive growth in an increasingly connected world.

Introducing the First-Ever Telecom Industry Forum at DAIS 2025 For the first time ever, Data + AI Summit (DAIS) will feature a dedicated Telecom Industry Forum — your exclusive opportunity to connect with telecom peers, exchange ideas, and hear directly from visionary leaders who are redefining the future of communications with data and AI. This forum will showcase how telecom operators are using the Databricks Data Intelligence Platform to deliver measurable results — from optimizing operations and enhancing customer experience to enabling new revenue models — all while maintaining the interoperability and agility needed to thrive in an evolving landscape. Join us on Tuesday, June 10 at 4:00 PM for a compelling lineup of speakers: Matt Dugan, VP, Data Platforms, AT&T, will share how AT&T is reducing operational costs and improving productivity with a data-first strategy that continues to evolve. Jesse Ross, VP, Information Technology, Frontier Communications, will discuss how Frontier is unlocking new revenue and enhancing CX through a modern, AI-powered data platform. Dayle Stevens and Arnab Chakraborty will present the bold AI transformation journey of Telstra and Accenture, detailing how their joint venture is driving process reinvention and deploying agentic AI at scale. Guy Lupo, TM Forum, will outline a forward-looking blueprint for telco data and AI transformation — and share how TM Forum is collaborating with Databricks to future-proof the industry. This is a rare opportunity to hear from the leaders at the forefront of telecom innovation. Be inspired, connect with global industry peers, and take away actionable insights to lead your organization's next wave of transformation. The future of telecom is being built now — with Databricks and TM Forum at the center of it.

As organizations increasingly leverage sensitive data for AI applications, generating high-quality synthetic data with mathematical guarantees of privacy has become crucial. This talk explores the use of Gretel Safe Synthetics (now part of NVIDIA) to generate differentially private synthetic data that maintains high fidelity to the source data and high utility on downstream tasks across heterogeneous datasets. Our analysis presents a framework for privacy-preserving synthetic data generation with two use cases: e-commerce reviews and doctor’s notes. We reveal nuanced strategies for: Calibrating privacy parameters ε and δ for mixed text and tabular data Maintaining statistical properties and high utility on downstream classification tasks under stringent privacy constraints (e.g. Quantifying resilience to membership inference and attribute inference attacks

Westat, a leader in data-driven research for 60 years+, has implemented a centralized Databricks platform to support hundreds of research projects for government, foundations, and private clients. This initiative modernizes Westat’s technical infrastructure while maintaining rigorous statistical standards and streamlining data science. The platform enables isolated project environments with strict data boundaries, centralized oversight, and regulatory compliance. It allows project-specific customization of compute and analytics, and delivers scalable computing for complex analyses. Key features include config-driven Infrastructure as Code (IaC) with Terragrunt, custom tagging and AWS cost integration for ROI tracking, budget policies with alerts for proactive cost management, and a centralized dashboard with row-level security for self-service cost analytics. This unified approach provides full financial visibility and governance while empowering data teams to deliver value. Audio for this session is delivered in the conference mobile app, you must bring your own headphones to listen.

In this talk, we will explore how AI and advanced infrastructure are transforming Insulet's development and operations. We'll highlight how our innovations have reduced scrap part costs through manufacturing analytics, showcasing efficiency and cost savings. On leveraging Databricks AI solutions and productivity, it not only identifies errors but also fixes code and assists in writing complex queries. This goes beyond suggestions, providing actual solutions. On the infrastructure side, integrating Spark with Databricks simplifies setup and reduces costs. Additionally Databricks Lakeflow Connect enables real-time updates and simplification without much coding as we integrate with Salesforce. We'll also discuss real-time processing of patient data, demonstrating how Databricks drives efficiency and productivity. Join us to learn how these innovations enhance efficiency, cost savings and performance.

Spatial data is increasingly important, but working with it can be complex. In this session, we’ll explore how Databricks SQL supports spatial analysis and helps analysts and engineers get more value from location-based data. We’ll cover what’s coming in the Public Preview of Spatial SQL, when and how to use the new Geometry and Geography data types, and practical use cases for H3. You’ll also learn about common challenges with spatial data and how we're addressing them, along with a look at the near-term roadmap.

Learn how GenAI is being applied to financial services teams using Claude, an acknowledged leader in large language models. Integrated with the scale and security of the Databricks Data Intelligence Platform, we will share how Claude is enabling financial services organizations to streamline operations, maximize productivity for investment and compliance teams and in some cases turn traditional cost-centers into revenue drivers.

Delta Lake is redesigning its Spark connector through the combination of three key technologies: First, we're updating our Spark APIs to DSv2 to achieve deeper catalog integration and improved integration with the Spark optimizer. Second, we're fully integrating on top of Delta Kernel to take advantage of its simplified abstraction of Delta protocol complexities, accelerating feature adoption and improving maintainability. Third, we are transforming Delta to become a catalog-aware lakehouse format with Catalog Commits, enabling more efficient metadata management, governance and query performance. Join us to explore how we're advancing Delta Lake's architecture, pushing the boundaries of metadata management and creating a more intelligent, performant data lakehouse platform.

Master Data Management (MDM) is the foundation of a successful enterprise data strategy — delivering consistency, accuracy and trust across all systems that depend on reliable data. But how can organizations integrate trusted third-party data to enhance their MDM frameworks? How can they ensure that this master data is securely and efficiently shared across internal platforms and external ecosystems? This session explores how Dun & Bradstreet’s pre-mastered data serves as a single source of truth for customers, suppliers and vendors — reducing duplication and driving alignment across enterprise systems. With Delta Sharing, organizations can natively ingest Dun & Bradstreet data into their Databricks environment and establish a scalable, interoperable MDM framework. Delta Sharing also enables secure, real-time distribution of master data across the enterprise ensuring that every system operates from a consistent and trusted foundation.

Companies need a lot of data to build and deploy AI models—and they want it quickly. To meet this demand, platform teams are quickly scaling their Databricks usage, resulting in excess cost driven by inefficiencies and performance anomalies. Capital One has over 4,000 users leveraging Databricks to power advanced analytics and machine learning capabilities at scale. In this talk, we’ll share lessons learned from optimizing our own Databricks usage while balancing lower cost with peak performance. Attendees will learn how to identify top sources of waste, best practices for cluster management, tips for user governance and methods to keep costs in check.

In today’s logistics landscape, operational continuity depends on real time awareness and proactive decision making. This session presents an AI agent driven solution built on Databricks that transforms real time fleet IoT data into autonomous workflows. Streaming telemetry such as bearing vibration data is ingested and analyzed using FFT to detect anomalies. When a critical pattern is found, an AI agent diagnoses root causes and simulates asset behavior as a digital twin, factoring in geolocation, routing, and context. The agent then generates a corrective strategy by identifying service sites, skilled personnel, and parts, estimating repair time, and orchestrating reroutes. It evaluates alternate delivery vehicles and creates transfer plans for critical shipments. The system features human AI collaboration, enabling teams to review and execute plans. Learn how this architecture reduces downtime and drives resilient, adaptive fleet management.

Today’s supply chains demand more than historical insights–they need real-time intelligence. In this actionable session, discover how leading enterprises are unlocking the full potential of their SAP data by integrating it with Databricks and AI. See how CPG companies are transforming supply chain planning by combining SAP ERP data with external signals like weather and transportation data–enabling them to predict disruptions, optimize inventory, and make faster, smarter decisions. Powered by Databricks, this solution delivers true agility and resilience through a unified data architecture. Join us to learn how: You can eliminate SAP data silos and make them ML and AI-ready at scale External data sources amplify SAP use cases like forecasting and scenario planning AI-driven insights accelerate time-to-action across supply chain operations Whether you're just starting your data modernization journey or seeking ROI from SAP analytics, this session will show you what’s possible.

This session is repeated. In the race to revolutionize healthcare and drug discovery, biopharma companies are turning to AI to streamline workflows and unlock new scientific insights. This session, we will explore how NVIDIA BioNeMo, combined with Databricks Delta Lakehouse, can be used for advancing drug discovery for critical applications like molecular structure modeling, protein folding and diagnostics. We’ll demonstrate how BioNeMo pre-trained models can run inference on data securely stored in Delta Lake, delivering actionable insights. By leveraging containerized solutions on Databricks’ ML Runtime with GPU acceleration, users can achieve significant performance gains compared to traditional CPU-based computation.

Join us to hear about how Epsilon Data Management migrated Epsilon’s unique, AI-powered marketing identity solution from multi-petabyte on-prem Hadoop and data warehouse systems to a unified Databricks Lakehouse platform. This transition enabled Epsilon to further scale its Decision Sciences solution and enable new cloud-based AI research capabilities on time and within budget, without being bottlenecked by the resource constraints of on-prem systems. Learn how Delta Lake, Unity Catalog, MLflow and LLM endpoints powered massive data volume, reduced data duplication, improved lineage visibility, accelerated Data Science and AI, and enabled new data to be immediately available for consumption by the entire Epsilon platform in a privacy-safe way. Using the Databricks platform as the base for AI and Data Science at global internet scale, Epsilon deploys marketing solutions across multiple cloud providers and multiple regions for many customers.

Many studies have indicated that having a strong Data & AI culture helps our businesses be more successful. This can lead to better business performance, becoming more profitable and being more competitive compared to your peer companies as well as attaining and retaining top talent. What does it mean to have a Data & AI culture? It’s the ability for an organization to make data-driven decisions. It means using insights to improve your business results and using data ultimately allows you to enable AI. It tends to be the people that get in the way of having and sustaining an effective Data & AI culture. Do you have people already in your teams that can help you build your Data & AI culture? Can you attract and retain that talent to your organization? Can you help integrate that great talent into your organization to promote a Data & AI culture? It’s also ensuring that you fundamentally change the way you/your teams/organizations work.

As first-party data becomes increasingly invaluable to organizations, Walmart Data Ventures is dedicated to bringing to life new applications of Walmart’s first-party data to better serve its customers. Through Scintilla, its integrated insights ecosystem, Walmart Data Ventures continues to expand its offerings to deliver insights and analytics that drive collaboration between our merchants, suppliers, and operators.​Scintilla users can now access Walmart data using Cloud Feeds, based on Databricks Delta Sharing technologies. In the past, Walmart used API-based data sharing models, which required users to possess certain skills and technical attributes that weren’t always available. Now, with Cloud Feeds, Scintilla users can more easily access data without a dedicated technical team behind the scenes making it happen. Attendees will gain valuable insights into how Walmart has built its robust data sharing architecture and strategies to design scalable and collaborative data sharing architectures in their own organizations.

This session is repeated. Get the most out of your AI/BI Dashboards by scaling them across your entire organization. This session covers best practices for automating report distribution, embedding dashboards in external applications, and ensuring secure access across all surfaces. You'll walk away with practical strategies for delivering insights to the right people at the right time—empowering decision-makers at every level with the data they need to drive impactful outcomes.

Delta Kernel makes it easy for engines and connectors to read and write Delta tables. It supports many Delta features and robust connectors, including DuckDB, Clickhouse, Spice AI and delta-dotnet. In this session, we'll cover lessons learned about how to build a high-performance library that lets engines integrate the way they want, while not having to worry about the details of the Delta protocol. We'll talk through how we streamlined the API as well as its changes and underlying motivations. We'll discuss some new highlight features like write support, and the ability to do CDF scans. Finally we'll cover the future roadmap for the Kernel project and what you can expect from the project over the coming year.

Databricks Unity Catalog (UC) is the industry’s only unified and open governance solution for data and AI, built into the Databricks Data Intelligence Platform. UC provides a single source of truth for organization’s data and AI, providing open connectivity to any data source, any format, lineage, monitoring and support for open sharing and collaboration. In this session we will discuss the challenges in upgrading to UC from your existing databricks Non-UC set up. We will discuss a few customer use cases and how we overcame difficulties and created a repeatable pattern and reusable assets to replicate the success of upgrading to UC across some of the largest databricks customers. It is co-presented with our partner Celebal Technologies.

Join us to hear about how National Australia Bank (NAB) successfully completed a significant milestone in its data strategy by decommissioning its 26-year-old Teradata environment and migrating to a new strategic data platform called 'Ada'. This transition marks a pivotal shift from legacy systems to a modern, cloud-based data and AI platform powered by Databricks. The migration process, which spanned two years, involved ingesting 16 data sources, transferring 456 use cases, and collaborating with hundreds of users across 12 business units. This strategic move positions NAB to leverage the full potential of cloud-native data analytics, enabling more agile and data-driven decision-making across the organization. The successful migration to Ada represents a significant step forward in NAB's ongoing efforts to modernize its data infrastructure and capitalize on emerging technologies in the rapidly evolving financial services landscape

Tonal is the ultimate strength training system, giving you the expertise of a personal trainer and a full gym in your home. Through user interviews and social media feedback, we identified a consistent challenge: members found it difficult to measure their progress in their fitness journey. To address this, we developed the Training Goal (TG) ecosystem, a four-part solution that introduced new preference options to capture users' fitness aspirations, implemented weekly metrics that accumulate as members complete workouts, defined personalized weekly targets to guide progress, and enhanced workout details to show how each session contributes toward individual goals.We present how we leveraged Spark, MLflow, and Workflows within the Databricks ecosystem to compute TG metrics, manage model development, and orchestrate data pipelines. These tools allowed us to launch the TG system on schedule, supporting scalability, reliability, and a more meaningful, personalized way for members to track their progress.

The Global Water Security Center translates environmental science into actionable insights for the U.S. Department of Defense. Prior to incorporating Databricks, responding to these requests required querying approximately five hundred thousand raster files representing over five hundred billion points. By leveraging lakehouse architecture, Databricks Auto Loader, Spark Streaming, Databricks Spatial SQL, H3 geospatial indexing and Databricks Liquid Clustering, we were able to drastically reduce our “time to analysis” from multiple business days to a matter of seconds. Now, our data scientists execute queries on pre-computed tables in Databricks, resulting in a “time to analysis” that is 99% faster, giving our teams more time for deeper analysis of the data. Additionally, we’ve incorporated Databricks Workflows, Databricks Asset Bundles, Git and Git Actions to support CI/CD across workspaces. We completed this work in close partnership with Databricks.

Customer support is going through the GenAI revolution, but how can we use AI to foster deeper empathy with our end users?To enable this, Earnin has built its GenAI observability platform on Databricks, leveraging Lakeflow Declarative Pipeliness, Kafka and Databricks AI/BI.This session covers how we use Lakeflow Declarative Pipelines to monitor our customer care chatbot in near real-time and how we leverage Databricks to better anticipate our customers' needs.