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.

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.

This session is repeated. Databricks Serverless revolutionizes data engineering and analytics by eliminating the complexities of infrastructure management. This talk will provide an overview of this powerful serverless compute option, highlighting how it enables practitioners to focus solely on building robust data pipelines. We'll explore the core benefits, including automatic scaling, cost optimization and seamless integration with the Databricks ecosystem. Learn how serverless workflows simplify the orchestration of various data tasks, from ingestion to dashboards, ultimately accelerating time-to-insight and boosting productivity. This session is ideal for data engineers, data scientists and analysts looking to leverage the agility and efficiency of serverless computing in their data workflows.

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.

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.

This session will show how we implemented a solution to support high-frequency data ingestion from smart meters. We implemented a robust API endpoint that interfaces directly with IoT devices. This API processes messages in real time from millions of distributed IoT devices and meters across the network. The architecture leverages cloud storage as a landing zone for the raw data, followed by a streaming pipeline built on Lakeflow Declarative Pipelines. This pipeline implements a multi-layer medallion architecture to progressively clean, transform and enrich the data. The pipeline operates continuously to maintain near real-time data freshness in our gold layer tables. These datasets connect directly to Databricks Dashboards, providing stakeholders with immediate insights into their operational metrics. This solution demonstrates how modern data architecture can handle high-volume IoT data streams while maintaining data quality and providing accessible real-time analytics for business users.

Managing metrics across teams can feel like everyone’s speaking a different language, which often leads to loss of trust in numbers. Based on a real-world use case, we’ll show you how to establish a governed source of truth for metrics that works at scale and builds a solid foundation for AI integration. You’ll explore how Bolt.eu’s data team governs consistent metrics for different data users and leverages Euno’s automations to navigate the overlap between Looker and dbt. We’ll cover best practices for deciding where your metrics belong and how to optimize engineering and maintenance workflows across Databricks, dbt and Looker. For curious analytics engineers, we’ll dive into thinking in dimensions & measures vs. tables & columns and determining when pre-aggregations make sense. The goal is to help you contribute to a self-serve experience with consistent metric definitions, so business teams and AI agents can access the right data at the right time without endless back-and-forth.

With 75M+ Treats Rewards members, PetSmart knows how to build loyalty with pet parents. But recently, traditional email testing and personalization strategies weren’t delivering the engagement and growth they wanted—especially in the Salon business. This year, they replaced their email calendar and A/B testing with AI Decisioning, achieving a +22% incremental lift in bookings. Join Bradley Breuer, VP of Marketing – Loyalty, Personalization, CRM, and Customer Analytics, to learn how his team reimagined CRM using AI to personalize campaigns and dynamically optimize creative, offers, and timing for every unique pet parent. Learn: How PetSmart blends human insight and creativity with AI to deliver campaigns that engage and convert. How they moved beyond batch-and-blast calendars with AI Decisioning Agents to optimize sends—while keeping control over brand, messaging, and frequency. How using Databricks as their source of truth led to surprising learnings and better outcomes.

Despite the proliferation of cloud data warehousing, BI tools, and AI, spreadsheets are still the most ubiquitous data tool. Business teams in finance, operations, sales, and marketing often need to analyze data in the cloud data warehouse but don't know SQL and don't want to learn BI tools. AI tools offer a new paradigm but still haven't broadly replaced the spreadsheet. With new AI tools and legacy BI tools providing business teams access to data inside Databricks, security and governance are put at risk. In this session, Row Zero CEO, Breck Fresen, will share examples and strategies data teams are using to support secure spreadsheet analysis at Fortune 500 companies and the future of spreadsheets in the world of AI. Breck is a former Principal Engineer from AWS S3 and was part of the team that wrote the S3 file system. He is an expert in storage, data infrastructure, cloud computing, and spreadsheets.

Join this session to hear how Western Governors University leverages Tealium & Databricks to power their data activation strategy with real-time customer data collection, activation and advanced analytics.

Transform your AI/BI Genie into a text-to-SQL powerhouse using the Genie Conversation APIs. This session explores how Genie functions as an intelligent agent, translating natural language queries into SQL to accelerate insights and enhance self-service analytics. You'll learn practical techniques for configuring agents, optimizing queries and handling errors — ensuring Genie delivers accurate, relevant responses in real time. A must-attend for teams looking to level up their AI/BI capabilities and deliver smarter analytics experiences.

The "Doordash Customer 360 Data Store" represents a foundational step in centralizing and managing customer profile to enable targeting and personalized customer experiences built on Delta Lake. This presentation will explore the initial goals and architecture of the Customer 360 Data Store, its journey to becoming a robust entity management framework, and the challenges and opportunities encountered along the way. We will discuss how the evolution addressed scalability, data governance and integration needs, enabling the system to support dynamic and diverse use cases, including customer lifecycle analytics, marketing campaign targeting using segmentation. Attendees will gain insight into key design principles, technical innovations and strategic decisions that transformed the system into a flexible platform for entity management, positioning it as a critical enabler of data-driven growth at Doordash. Audio for this session is delivered in the conference mobile app, you must bring your own headphones to listen.

Dropbox, a leading cloud storage platform, is on a mission to accelerate data insights to better understand customers’ needs and elevate the overall customer experience. By leveraging Fivetran’s data movement platform, Dropbox gained real-time visibility into customer sentiment, marketing ROI, and ad performance-empowering teams to optimize spend, improve operational efficiency, and deliver greater business outcomes.Join this session to learn how Dropbox:- Cut data pipeline time from 8 weeks to 30 minutes by automating ingestion and streamlining reporting workflows.- Enable real-time, reliable data movement across tools like Zendesk Chat, Google Ads, MySQL, and more — at global operations scale.- Unify fragmented data sources into the Databricks Data Intelligence Platform to reduce redundancy, improve accessibility, and support scalable analytics.

Nasdaq’s rapid growth through acquisitions led to fragmented client data across multiple Salesforce instances, limiting cross-sell potential and sales insights. To solve this, Nasdaq partnered with Slalom to build a unified Client Data Hub on the Databricks Lakehouse Platform. This cloud-based solution merges CRM, product usage, and financial data into a consistent, 360° client view accessible across all Salesforce orgs with bi-directional integration. It enables personalized engagement, targeted campaigns, and stronger cross-sell opportunities across all business units. By delivering this 360 view directly in Salesforce, Nasdaq is improving sales visibility, client satisfaction, and revenue growth. The platform also enables advanced analytics like segmentation, churn prediction, and revenue optimization. With centralized data in Databricks, Nasdaq is now positioned to deploy next-gen Agentic AI and chatbots to drive efficiency and enhance sales and marketing experiences.

Health Catalyst (HCAT) transformed its CI/CD strategy by replacing a rigid, internal deployment tool with Databricks Asset Bundles (DABs), unlocking greater agility and efficiency. This shift streamlined deployments across both customer workspaces and HCAT's core platform, accelerating time to insights and driving continuous innovation. By adopting DABs, HCAT ensures feature parity, standardizes metric stores across clients, and rapidly delivers tailored analytics solutions. Attendees will gain practical insights into modernizing CI/CD pipelines for healthcare analytics, leveraging Databricks to scale data-driven improvements. HCAT's next-generation platform, Health Catalyst Ignite™, integrates healthcare-specific data models, self-service analytics, and domain expertise—powering faster, smarter decision-making.

This presentation outlines the evolution of our marketing data strategy, focusing on how we’ve built a strong foundation using the Databricks Lakehouse. We will explore key advancements across data ingestion, strategy, and insights, highlighting the transition from legacy systems to a more scalable and intelligent infrastructure. Through real-world applications, we will showcase how unified Customer 360 insights drive personalization, predictive analytics enhance campaign effectiveness, and GenAI optimizes content creation and marketing execution. Looking ahead, we will demonstrate the next phase of our CDP, the shift toward an end-user-first analytics model powered by AIBI, Genie and Matik, and the growing importance of clean rooms for secure data collaboration. This is just the beginning, and we are poised to unlock even greater capabilities in the future.

As organizations increasingly adopt Databricks as a unified platform for analytics and AI, ensuring robust data governance becomes critical for compliance, security, and operational efficiency. This presentation will explore the end-to-end framework for governing the Databricks cloud, covering key use cases, foundational governance principles, and scalable automation strategies. We will discuss best practices for metadata, data access, catalog, classification, quality, and lineage, while leveraging automation to streamline enforcement. Attendees will gain insights into best practices and real-world approaches to building a governed data cloud that balances innovation with control.

At Data and AI in 2022, Databricks pioneered the term to shift left in how AI workloads would enable less data science driven people to create their own apps. In 2025, we take a look at how Experian is doing on that journey. This session highlights Databricks services that assist with the shift left paradigm for Generative AI, including how AI/BI Genie helps with Generative analytics, and how Agent Studio helps with synthetic generation of test cases to validate model performance.