At Second Dinner, delivering fast, personalized gameplay experiences is key to player engagement. In this session, Justin Wu shares how the team implemented real-time feature serving using Databricks to power responsive, data-driven game mechanics at scale. He’ll dive into the architecture, technical decisions, and trade-offs behind their solution—highlighting how they balance performance, scalability, and cost. Whether you're building live features or rethinking your game data stack, this session offers practical insights to accelerate your journey.

Unlock Genie's full potential with best practices for curating, deploying and monitoring Genie spaces at scale. This session offers a deep dive into the latest enhancements and provides practical guidance on designing high-quality spaces, streamlining deployment workflows and implementing robust monitoring to ensure accuracy and performance in production. Ideal for teams aiming to scale conversational analytics, you’ll leave with actionable strategies to keep your Genie spaces efficient, reliable and aligned with business outcomes.

Customer data is an organization's most valuable asset. It is also the hardest to govern and use in a dynamic business environment. Consumers can revoke their consent in an instant, regulations continue to grow, and internal data policies change. Most troubling is when cross-functional teams question whether, when, and how they can use customer data. How does an organization—let alone a data governance team and its stakeholders—manage this data and policy fragmentation, while enabling data use? Join product leaders from OneTrust as they explore new data governance practices and technologies for delivering AI-ready data. We’ll demo an integration that orchestrates data policy enforcement through Unity Data Catalog and the OneTrust Data Use Governance solution. Understand how this new offering in addition with OneTrust’s solutions for Consent & Preferences and AI Governance align your data governance & compliance initiatives for AI innovation.

Windsurf has taken the developer and vibe coding ecosystem by a storm since its launch in November 2024. Wave after wave of features like Tab, MCP support, browser Preview, web search, Deploys, etc. might all seem random, but there’s a method to the madness. We are building Windsurf to be the ultimate collaborative agent, one in which the human and the AI operate as if with the same brain. Windsurf will be everywhere the developer does work, understanding the entire SDLC, and providing value along the way. In this talk, you’ll learn how Windsurf drives its strategy and builds on the frontier for its users and customers.

Curious about the cutting-edge technology that's revolutionizing AI model performance? Join us for an in-depth exploration of TAO and discover how this innovative approach is transforming the capabilities of modern AI systems. This research-focused session peels back the layers of theoretical foundations, implementation challenges, and breakthrough applications that make TAO one of the most promising advancements in AI development. Key takeaways: Understanding the fundamental principles behind TAO and how it differs from conventional optimization techniques Examining the quantifiable improvements in model accuracy, efficiency, and generalization capabilities Exploring real-world case studies where TAO has solved previously intractable AI challenges Analyzing current research directions and future potential for further enhancements Whether you're a research scientist, AI engineer, or technical leader, this session will equip you with valuable insights into how TAO can be leveraged to push your AI models beyond current limitations.

Join us to discover how leading tech companies accelerate growth using open ecosystems and built-on solutions to foster collaboration, accelerate innovation and create scalable data products. This session will explore how organizations use Databricks to securely share data, integrate with partners and enable teams to build impactful applications powered by AI and analytics. Topics include: Using Delta Sharing for secure, real-time data collaboration across teams and partners Embedding analytics and creating marketplaces to extend product capabilities Building with open standards and governance frameworks to ensure compliance without sacrificing agility Hear real-world examples of how open ecosystems empower organizations to widen the aperture on collaboration, driving better business outcomes. Walk away with insights into how open data sharing and built-on solutions can help your teams innovate faster at scale.

Join us for an interactive breakout session designed to explore scalable, real-world solutions powered by Partners with Databricks. In this high-energy session, you'll hear from three of our leading partners — Accenture, Capgemini and Wipro — as they each deliver rapid-fire, 5-minute demos of their most impactful, production-grade solutions built for the telecom industry. From network intelligence to customer experience to AI-driven automation, these solutions are already driving tangible outcomes at scale. After the demos, you’ll have the unique opportunity to engage directly with each partner in a “speed dating” style format. Dive deep into the solutions, ask your questions and explore how these approaches can be tailored to your organization’s needs. Whether you're solving for churn, fraud, network ops or enterprise AI use cases, this session is your chance to connect, collaborate and walk away with practical ideas you can take back to your teams.

Ready to take your AI/BI dashboards to the next level? This session dives into the latest capabilities in Databricks AI/BI Dashboards and how to maximize impact across your organization. Learn how data authors can tailor visualizations for different audiences, optimize performance and seamlessly integrate with Genie for a unified analytics experience. We’ll also share practical tips on how business users and data teams can better collaborate — ensuring insights are accessible, actionable and aligned to business goals.

Join this session for a concise tour of Apache Spark™ 4.0’s most notable enhancements: SQL features: ANSI by default, scripting, SQL pipe syntax, SQL UDF, session variable, view schema evolution, etc. Data type: VARIANT type, string collation Python features: Python data source, plotting API, etc. Streaming improvements: State store data source, state store checkpoint v2, arbitrary state v2, etc. Spark Connect improvements: More API coverage, thin client, unified Scala interface, etc. Infrastructure: Better error message, structured logging, new Java/Scala version support, etc. Whether you’re a seasoned Spark user or new to the ecosystem, this talk will prepare you to leverage Spark 4.0’s latest innovations for modern data and AI pipelines.

Explore how AI is transforming business intelligence and data analytics across the Databricks platform. This session offers a comprehensive overview of AI-assisted capabilities, from generating dashboards and visualizations to integrating Genie on dashboards for conversational analytics. Whether you’re a data engineer, analyst or BI developer, this session will equip you to leverage AI with BI for better, smarter decisions.

Retail and CPG companies face growing pressure to better forecast demand, optimize pricing and manage inventory — yet traditional approaches take months to deploy and often require extensive engineering support. In this session, we will showcase Workcloud Modeling Studio, a low-code/no-code ML platform designed for data scientists working in retail and CPG. Learn how this tool improves forecasting accuracy and accelerates time-to-value from months to hours. We will walk through a real-world use case of demand forecasting for a retailer using Zebra's Modeling Studio. This talk will demonstrate how to build, train and deploy an ML forecasting pipeline — without reinventing the wheel.

The demand for data engineering keeps growing, but data teams are bored by repetitive tasks, stumped by growing complexity and endlessly harassed by an unrelenting need for speed. What if AI could take the heavy lifting off your hands? What if we make the move away from code-generation and into config-generation — how much more could we achieve? In this session, we’ll explore how AI is revolutionizing data engineering, turning pain points into innovation. Whether you’re grappling with manual schema generation or struggling to ensure data quality, this session offers practical solutions to help you work smarter, not harder. You’ll walk away with a good idea of where AI is going to disrupt the data engineering workload, some good tips around how to accelerate your own workflows and an impending sense of doom around the future of the industry!

In this session, discover how effective data movement is foundational to successful GenAI implementations. As organizations rush to adopt AI technologies, many struggle with the infrastructure needed to manage the massive influx of unstructured data these systems require. Jim Kutz, Head of Data at Airbyte, draws from 20+ years of experience leading data teams at companies like Grafana, CircleCI, and BlackRock to demonstrate how modern data movement architectures can enable secure, compliant GenAI applications. Learn practical approaches to data sovereignty, metadata management, and privacy controls that transform data governance into an enabler for AI innovation. This session will explore how you can securely leverage your most valuable asset—first-party data—for GenAI applications while maintaining complete control over sensitive information. Walk away with actionable strategies for building an AI-ready data infrastructure that balances innovation with governance requirements.

As AI adoption accelerates, unstructured data has emerged as a critical—yet often overlooked—asset for building accurate, trustworthy AI agents. But preparing and governing this data at scale remains a challenge. Traditional data integration and RAG approaches fall short. In this session, discover how IBM enables AI agents grounded in governed, high-quality unstructured data. Learn how our unified data platform streamlines integration across batch, streaming, replication, and unstructured sources—while accelerating data intelligence through built-in governance, quality, lineage, and data sharing. But governance doesn’t stop at data. We’ll explore how AI governance extends oversight to the models and agents themselves. Walk away with practical strategies to simplify your stack, strengthen trust in AI outputs, and deliver AI-ready data at scale.

Matei is a legend of open source: he started the Apache Spark project in 2009, co-founded Databricks, and worked on other widely used data and AI software, including MLflow, Delta Lake, and Dolly. His most recent research is about combining large language models (LLMs) with external data sources, such as search systems, and improving their efficiency and result quality. This will be a conversation coverering the latest and greatest of UC, Data Intelligence, AI Governance, and more.

Hear more on the latest in data collaboration, which is paramount to unlocking business success. Delta Sharing is an open-source approach to share and govern data, AI models, dashboards, and notebooks across clouds and platforms - without the costly need for replication. Databricks Clean Rooms provide safe hosting environments for data collaboration across companies, also without the costly duplication of data. And the Databricks Marketplace is the open marketplace for all your data, analytics, and AI needs.

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

Go beyond the user interface and explore the cutting-edge technology driving AI/BI Genie. This session breaks down the AI/BI Genie architecture, showcasing how LLMs, retrieval-augmented generation (RAG) and finely tuned knowledge bases work together to deliver fast, accurate responses. We’ll also explore how AI agents orchestrate workflows, optimize query performance and continuously refine their understanding. Ideal for those who want to geek out about the tech stack behind Genie, this session offers a rare look at the magic under the hood.

Join this session to hear from two incredible companies, Xylem and Joby Aviation. Xylem shares their successful journey from fragmented legacy systems to a unified Enterprise Data Platform, demonstrating how they integrated complex ERP data across four business segments to achieve breakthrough improvements in parts management and operational efficiency. Following Xylem's story, learn how Joby Aviation leveraged Databricks to automate and accelerate flight test data checks, cutting processing times from over two hours to under thirty minutes. This session highlights how advanced cloud tools empower engineers to quickly build and run custom data checks, improving both speed and safety in flight test operations.

Generic LLM metrics are useless until it meets your business needs.In this session we will dive deep into creating bespoke custom state-of-the-art AI metrics that matters to you. Discuss best practices on LLM evaluation strategies, when to use LLM judge vs. statistical metrics and many more. Through a live demo using Mosaic AI Framework, we will showcase: How you can build your own custom AI metric tailored to your needs for your GenAI application Implement autonomous AI evaluation suite for complex, multi-agent systems Generate ground truth data at scale and production monitoring strategies Drawing from extensive experience on working with customers on real-world use cases, we will share actionable insights on building a robust AI evaluation framework By the end of this session, you'll be equipped to create AI solutions that are not only powerful but also relevant to your organizations needs. Join us to transform your AI strategy and make a tangible impact on your business!

In the rapidly-evolving field of data analytics, (AI/BI) dashboards and Power BI stand out as two formidable approaches, each offering unique strengths and catering to specific use cases. Power BI has earned its reputation for delivering user-friendly, highly customisable visualisations and reports for data analysis. On the other hand, AI/BI dashboards have gained good traction due to their seamless integration with the Databricks platform, making them an attractive option for data practitioners. This session will provide a comparison of these two tools, highlighting their respective features, strengths and potential limitations. Understanding the nuances between these tools is crucial for organizations aiming to make informed decisions about their data analytics strategy. This session will equip participants with the knowledge needed to select the most appropriate tool or combination of tools to meet their data analysis requirements and drive data-informed decision-making processes.

This presentation explores how Databricks' Data Intelligence Platform supports the development and deployment of responsible AI in credit decisioning, ensuring fairness, transparency and regulatory compliance. Key areas include bias and fairness monitoring using Lakehouse Monitoring to track demographic metrics and automated alerts for fairness thresholds. Transparency and explainability are enhanced through the Mosaic AI Agent Framework, SHAP values and LIME for feature importance auditing. Regulatory alignment is achieved via Unity Catalog for data lineage and AIBI dashboards for compliance monitoring. Additionally, LLM reliability and security are ensured through AI guardrails and synthetic datasets to validate model outputs and prevent discriminatory patterns. The platform integrates real-time SME and user feedback via Databricks Apps and AI/BI Genie Space.

Erste Group's transition to Azure Databricks marked a significant upgrade from a legacy system to a secure, scalable and cost-effective cloud platform. The initial architecture, characterized by a complex hub-spoke design and stringent compliance regulations, was replaced with a more efficient solution. The phased migration addressed high network costs and operational inefficiencies, resulting in a 60% reduction in networking costs and a 30% reduction in compute costs for the central team. This transformation, completed over a year, now supports real-time analytics, advanced machine learning and GenAI while ensuring compliance with European regulations. The new platform features a Unity Catalogue, separate data catalogs and dedicated workspaces, demonstrating a successful shift to a cloud-based machine learning environment with significant improvements in cost, performance and security.

Redox & Databricks direct integration can streamline your interoperability workflows from responding in record time to preauthorization requests to letting attending physicians know about a change in risk for sepsis and readmission in near real time from ADTs. Data engineers will learn how to create fully-streaming ETL pipelines for ingesting, parsing and acting on insights from Redox FHIR bundles delivered directly to Unity Catalog volumes. Once available in the Lakehouse, AI/BI Dashboards and Agentic Frameworks help write FHIR messages back to Redox for direct push down to EMR systems. Parsing FHIR bundle resources has never been easier with SQL combined with the new VARIANT data type in Delta and streaming table creation against Serverless DBSQL Warehouses. We'll also use Databricks accelerators dbignite and redoxwrite for writing and posting FHIR bundles back to Redox integrated EMRs and we'll extend AI/BI with Unity Catalog SQL UDFs and the Redox API for use in Genie.

Navy Federal Credit Union has 200+ enterprise data sources in the enterprise data lake. These data assets are used for training 100+ machine learning models and hydrating a semantic layer for serving, at an average 4,000 business users daily across the credit union. The only option for extracting data from analytic semantic layer was to allow consuming application to access it via an already-overloaded cloud data warehouse. Visualizing data lineage for 1,000 + data pipelines and associated metadata is impossible and understanding the granular cost for running data pipelines is a challenge. Implementing Unity Catalog opened alternate path for accessing analytic semantic data from lake. It also opened the doors to remove duplicate data assets stored across multiple lakes which will save hundred thousands of dollars in data engineering efforts, compute and storage costs.