delivery

areasofinterest

track

type

level

industry

Founder & Investor

muddu sudhakar

VP,  IT Head of Cloud, Data, and AI

Murali Vridhachalam

 Igniting Innovation at Gilead: Convergence of Cloud, Data, AI and Agents

In Bruce’s career in cyber warfare and enterprise cybersecurity, he worked on many of the highest profile botnet and nation state takedowns in history. He also helped build the tech in one of the world’s most advanced SOCs. Bruce will explain what he learned from that experience and why it prompted him to leave early retirement, sell his beloved sports car and co-found ziggiz. We all know there’s more data than ever. Anyone close to cybersecurity also knows that SIEMs, typically at the center of enterprise cybersecurity operations, have become too expensive even at the highest levels of government and Fortune 100s.

“What I Wish I Had Known in My Last SOC.” Confessions of a Cybersecurity Executive

This session is repeated.Is stream processing the future? We think so — and we’re building it with you using the latest capabilities in Apache Spark™ Structured Streaming. If you're a power user, this session is for you: we’ll demo new advanced features, from state transformations to real-time mode. If you prefer simplicity, this session is also for you: we’ll show how Lakeflow Declarative Pipelines simplifies managing streaming pipelines. And if you’re somewhere in between, we’ve got you covered — we’ll explain when to use your own streaming jobs versus Lakeflow Declarative Pipelines.

Director of Engineering

Indrajit Roy

Director, Product Management

Ray Zhu

A Comprehensive Guide to Streaming on the Data Intelligence Platform

SMBC, a major Japanese multinational financial services institution, has embarked on an initiative to build a GenAI-powered, modern and well-governed cloud data platform on Azure/Databricks. This initiative aims to build an enterprise data foundation encompassing loans, deposits, securities, derivatives, and other data domains.Its primary goals are:<ul>	<li>To decommission legacy data platforms and reduce data sprawl by migrating 20+ core banking systems to a multi-tenant Azure Databricks architecture</li>	<li>To leverage Databrick’s delta-share capabilities to address SMBC’s unique global footprint and data sharing needs</li>	<li>To govern data by design using Unity Catalog</li>	<li>To achieve global adoption of the frameworks, accelerators, architecture and tool stack to support similar implementations across EMEA</li>	<li>Deloitte and SMBC leveraged the Brickbuilder asset “Data as a Service for Banking” to accelerate this highly strategic transformation.</li></ul>

Principal

Anshul Wadhawan

Chief Information Officer - Americas

Gordon Wilson

A Japanese Mega-Bank’s Journey to a Modern, GenAI-Powered, Governed Data Platform

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.

AI Product Director

Moez Ali

A No-Code ML Forecasting Platform for Retail and CPG Companies

The demand for skilled Databricks data engineers continues to rise as enterprises accelerate their adoption of the Databricks platform. However, navigating the complex ecosystem of data engineering tools, frameworks and best practices can be overwhelming. This session provides a structured roadmap to becoming an expert Databricks data engineer, offering a clear progression from foundational skills to advanced capabilities. Acadford, a leading training provider, has successfully trained thousands of data engineers on Databricks, equipping them with the skills needed to excel in their careers and obtain professional certifications. Drawing on this experience, we will guide attendees through the most in-demand skills and knowledge areas through a combination of structured learning and practical insights. Key takeaways:<ul>	<li>Understand the core tech stack in Databricks</li>	<li>Explore real-world code examples and live demonstrations</li>	<li>Receive an actionable learning path with recommended resources</li></ul>

Derar Alhussein

A Practical Roadmap to Becoming an Expert Databricks Data Engineer

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.

Product Specialist

Prashanth Babu Velanati Venkata

A Practitioner’s Guide to Databricks Serverless

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.

Principal Data Engineer

Brendon Allen

Sr. Solutions Architect

Alex Owen

A Prescription for Success: Leveraging DABs for Faster Deployment and Better Patient Outcomes

This session introduces ByteDance’s challenges in data management and model training, and addresses them by Magnus (enhanced Apache Iceberg) and Byted Streaming (customized Mosaic Streaming). Magnus uses Iceberg’s branch/tag to manage massive datasets/checkpoints efficiently. With enhanced metadata and a custom C++ data reader, Magnus achieves optimal sharding, shuffling and data loading. Flexible table migration, detailed metrics and built-in full-text indexes on Iceberg tables further ensure training reliability. When training with ultra-large datasets, ByteDance faced scalability and performance issues. Given Streaming's scalability in distributed training and good code structure, the team chose and customized it to resolve challenges like slow startup, high resource consumption, and limited data source compatibility. In this session, we will explore Magnus and Byted Streaming, discuss their enhancements and demonstrate how they enable efficient and robust distributed training.

Software Engineer

Zilong Zhou

A Unified Solution for Data Management and Model Training With Apache Iceberg and Mosaic Streaming

A production-ready GenAI application is more than the framework itself. Like ML, you need a unified platform to create an end-to-end workflow for production quality applications.Below is an example of how this works on Databricks:<ul>	<li>Data ETL with Lakeflow Declarative Pipelines and jobs</li>	<li>Data storage for governance and access with Unity Catalog</li>	<li>Code development with Notebooks</li>	<li>Agent versioning and metric tracking with MLflow and Unity Catalog</li>	<li>Evaluation and optimizations with Mosaic AI Agent Framework and DSPy</li>	<li>Hosting infrastructure with monitoring with Model Serving and AI Gateway</li>	<li>Front-end apps using Databricks Apps</li></ul>In this session, learn how to build agents to access all your data and models through function calling. Then, learn how DSPy enables agent interaction with each other to ensure the question is answered correctly. We will demonstrate a chatbot, powered by multiple agents, to be able to answer questions and reason answers the base LLM does not know and very specialized topics.ow and very specialized topics.

Delivery Solutions Architect

Austin Choi

Accelerate End-to-End Multi-Agents on Databricks and DSPy

This session is repeated. Did you know that you can integrate with your favorite BI tools directly from Databricks SQL? You don’t even need to stand up an additional warehouse. This session shows the integrations with Microsoft Power Platform, Power BI, Tableau and dbt so you can have a seamless integration experience. Directly connect your Databricks workspace with Fabric and Power BI workspaces or Tableau to publish and sync data models, with defined primary and foreign keys, between the two platforms.

Sr. Staff Product Manager

Fuat Can Efeoglu

Product Manager

Toussaint Webb

Accelerating Analytics: Integrating BI and Partner Tools to Databricks SQL

In this session, we will share NCS’s approach to implementing a Databricks Lakehouse architecture, focusing on key lessons learned and best practices from our recent implementations. By integrating Databricks SQL Warehouse, the DBT Transform framework and our innovative test automation framework, we’ve optimized performance and scalability, while ensuring data quality. We’ll dive into how Unity Catalog enabled robust data governance, empowering business units with self-serve analytical workspaces to create insights while maintaining control. Through the use of solution accelerators, rapid environment deployment and pattern-driven ELT frameworks, we’ve fast-tracked time-to-value and fostered a culture of innovation. Attendees will gain valuable insights into accelerating data transformation, governance and scaling analytics with Databricks.

Practice Director, Data & AI

Kevin Wilson

Accelerating Data Transformation: Best Practices for Governance, Agility and Innovation

Growth in capital markets thrives on innovation, agility and real-time insights. This session highlights how leading firms use Databricks’ Data Intelligence Platform to uncover opportunities, optimize trading strategies and deliver personalized client experiences. Learn how advanced analytics and AI help organizations expand their reach, improve decision-making and unlock new revenue streams. Industry leaders share how unified data platforms break down silos, deepen insights and drive success in a fast-changing market. Key takeaways:<ul>	<li>Predictive analytics and machine learning strategies for growth</li>	<li>Real-world examples of optimized trading and enhanced client engagement</li>	<li>Tools to innovate while ensuring operational efficiency</li></ul> Discover how data intelligence empowers capital markets firms to thrive in today’s competitive landscape!

CDO - Wealth Management

Jimmy Kozlow

Head of Index Platform

Raul Chavarria

Financial Services Industry Director

Antoine Amend

Head AI and Digital Innovation

Bobby Grubert

Accelerating Growth in Capital Markets: Data-Driven Strategies for Success

Scaling large language models (LLMs) and multimodal architectures requires efficient data management and computational power. NVIDIA NeMo Framework Megatron-LM on Databricks is an open source solution that integrates GPU acceleration and advanced parallelism with Databricks Delta Lakehouse, streamlining workflows for pre-training and fine-tuning models at scale. This session highlights context parallelism, a unique NeMo capability for parallelizing over sequence lengths, making it ideal for video datasets with large embeddings. Through the case study of TwelveLabs’ Pegasus-1 model, learn how NeMo empowers scalable multimodal AI development, from text to video processing, setting a new standard for LLM workflows.

Chief Technology Officer & Co-Founder

Aiden Lee

Wenwen Gao

Accelerating Model Development and Fine-Tuning on Databricks with TwelveLabs

Government leaders overwhelmingly recognize the potential benefits of AI as critical to long-term strategic goals of efficiency, but implementation challenges and security concerns could be obstacles to success.

Senior Information Officer and Data Leader

Suresh Kaudi

Retired Chief AI Officer/Chief Technology at Lunar Analytics

Spencer Schaefer

Solutions Architect

Andrew Hahn

Senior Field Marketing Manager

Shannon Bisselink

Achieve Your Mission With AI-Driven Decisions

Join for an insightful presentation on creating a robust data architecture to drive business outcomes in the age of Generative AI. Santosh Kudva, GE Vernova Chief Data Officer and Kevin Tollison, EY AI Consulting Partner, will share their expertise on transforming data strategies to unleash the full potential of AI.Learn how GE Vernova, a dynamic enterprise born from the 2024 spin-off of GE, revamped its diverse landscape. They will provide a look into how they integrated the pre-spin-off Finance Data Platform into the GE Vernova Enterprise Data & Analytics ecosystem utilizing Databricks to enable high-performance AI-led analytics. Key insights include: <ul>	<li>Incorporating Generative AI into your overarching strategy </li>	<li>Leveraging comprehensive analytics to enhance data quality </li>	<li>Building a resilient data framework adaptable to continuous evolution</li></ul> Don't miss this opportunity to hear from industry leaders and gain valuable insights to elevate your data strategy and AI success.

Partner, AI Consulting

Kevin Tollison

Chief Data Officer

Santosh Kudva

Achieving AI Success with a Solid Data Foundation

This session will explore how Adobe uses a sophisticated data security architecture built on the Databricks Data Intelligence Platform, along with the Open Cybersecurity Schema Framework (OCSF), to enable scalable, real-time threat detection across more than 10 PB of security data. We’ll compare different approaches to OCSF implementation and demonstrate how Adobe processes massive security datasets efficiently — reducing query times by 18%, maintaining 99.4% SLA compliance, and supporting 286 security users across 17 teams with over 4,500 daily queries. By using Databricks' Platform for serverless compute, scalable architecture, and LLM-powered recommendations, Adobe has significantly improved processing speed and efficiency, resulting in substantial cost savings. We’ll also highlight how OCSF enables advanced cross-tool analytics and automation, streamlining investigations. Finally, we’ll introduce Databricks’ new open-source OCSF toolkit for scalable security data normalization and invite the community to contribute.

Andrew Krioukov

Lead Security Data Engineer

Bharat Gamini

Adobe’s Security Lakehouse: OCSF, Data Efficiency and Threat Detection at Scale

As data-driven companies scale from small startups to global enterprises, managing secure data access becomes increasingly complex. Traditional access control models fall short at enterprise scale, where dynamic, purpose-driven access is essential. In this talk, we explore how our “Just-in-Time” Purpose-Based Access Control (PBAC) platform addresses the evolving challenges of data privacy and compliance, maintaining least privilege while ensuring productivity. Using features like Unity Catalog, Delta Sharing & Databricks Apps, the platform delivers real-time, context-aware data governance. Leveraging JIT PBAC keeps your data secure, your engineers productive, your legal & security teams happy and your organization future-proof in the ever-evolving compliance landscape.

Engineering Manager

Arpan Ghosh

software engineer

Shuting Zhang

Advanced Data Access Control for the Exabyte Era: Scaling with Purpose

Explore advanced governance and authentication patterns for building secure, enterprise-grade apps with Databricks Apps. Learn how to configure complex permissions and manage access control using Unity Catalog. We’ll dive into “on-behalf-of-user” authentication — allowing agents to enforce user-specific access controls — and cover API-based authentication, including PATs and OAuth flows for external integrations. We’ll also highlight how Addepar uses these capabilities to securely build and scale applications that handle sensitive financial data. Whether you're building internal tools or customer-facing apps, this session will equip you with the patterns and tools to ensure robust, secure access in your Databricks apps.

Staff Software Engineer

Andre Furlan Bueno

Software Engineering Manager

Doug Judice

Advanced Governance and Auth With Databricks Apps

This session explores advanced JSON Schema handing(inference and evolving), and event DemuxingTopics include:<ul>	<li>How from_json is currently used today and its challenges.</li>	<li>How to use Variant for rapidly changing schema.</li>	<li>How from_json in Lakeflow Declarative Pipelines with primed schema helps simplify schema handling.</li>	<li>Demultiplexing patterns for scalable stream processing.</li>	<li>Simply event Demuxing with Lakeflow Declarative Pipelines.</li></ul>

Specialist Solutions Architect

Dattatraya Walake

Murali Talluri

Advanced JSON Schema handing and Event Demuxing

The course is designed to cover advanced concepts and workflows in machine learning operations. It starts by introducing participants to continuous integration (CI) and continuous development (CD) workflows within machine learning projects, guiding them through the deployment of a sample CI/CD workflow using Databricks in the first section. Moving on to the second part, participants delve into data and model testing, where they actively create tests and automate CI/CD workflows. Finally, the course concludes with an exploration of model monitoring concepts, demonstrating the use of Lakehouse Monitoring to oversee machine learning models in production settings. Pre-requisites: Familiarity with Databricks workspace and notebooks; knowledge of machine learning model development and deployment with MLflow (e.g. intermediate-level knowledge of traditional ML concepts, development with CI/CD, the use of Python and Git for ML projects with popular platforms like GitHub) Labs: YesCertification Path: Databricks Certified Machine Learning Professional

Advanced Machine Learning Operations

The most common RAG systems rely on a frozen RAG system — one where there’s a single embedding model and single vector index. We’ve achieved a modicum of success with that, but when it comes to increasing accuracy for production systems there is only so much this approach solves. In this session we will explore how to move from the frozen systems to adaptive RAG systems which produce more tailored outputs with higher accuracy. Databricks services: Lakehouse, Unity Catalog, Mosaic, Sweeps, Vector Search, Agent Evaluation, Managed Evaluation, Inference Tables

Senior Generative AI Engineer

Jason Li

Head of AI/ML Innovation

James Lin

Advanced RAG Overview — Thawing Your Frozen RAG Pipeline

Learn how to build sophisticated systems that enable natural language interactions with both your structured databases and unstructured document collections. This session explores advanced techniques for creating unified and governed AI systems that can seamlessly interpret questions, retrieve relevant information and generate accurate answers across your entire data ecosystem. Key takeaways include:<ul>	<li>Strategies for combining vector search over unstructured documents with retrieval from structured databases</li>	<li>Techniques for optimizing unstructured data processing through effective parsing, metadata enrichment and intelligent chunking</li>	<li>Methods for integrating different retrieval mechanisms while ensuring consistent data governance and security</li>	<li>Practical approaches for evaluating and improving KBQA system quality through automated and human feedback</li></ul>

Staff Product Manager

Elise Gonzales

Agent Bricks: Building Multi-Agent Systems for Structured and Unstructured Information

Medical providers often receive less than 15 minutes of instruction in how to interact with patients during emotionally charged end of life interactions. Continuing education for clinicians is critical to hone these skills but is difficult to scale traditional approaches that require professional patients and instructors. Here, we describe a custom chatbot that plays the role of patient and coach to provide a scaling learning experience. A critical challenge was how to mitigate the persistently cheerful and helpful tone which results from standard pretraining in the Patient Persona AI. We accomplished this by implementing a multi-agent architecture based upon a graphical model of the conversation. System prompts reflecting the patient’s cognitive state are dynamically updated as the conversation progresses. Future extensions of the work are intended to focus on additional custom model fine-tuning in the Mosaic AI platform to further improve the realism of the conversation.

Senior Data Scientist

Alex Ralevski

Agentic Architectures to Create Realistic Conversations: Using GenAI to Teach Empathy in Healthcare

Marketing professionals build campaigns, create content and use effective copywriting to tell a good story to promote a product/offer. All of this requires a thorough and meticulous process for every individual campaign. In order to assist marketing professionals at 7-Eleven, we built a multi-purpose assistant that could:<ol>	<li>Use campaign briefs to generate campaign ideas and taglines</li>	<li>Do copy-writing for marketing content</li>	<li>Verify images for messaging accuracy</li>	<li>Answer general questions and browse the web as a generic assistant</li></ol> We will walk you through how we created multiple agents as different personas with LangGraph and Mosaic AI to create a chat assistant that assumes a different persona based on the user query. We will also explain our evaluation methodology in choosing models and prompts and how we implemented guardrails for high reliability with sensitive marketing content. This assistant by 7-Eleven was showcased at the Databricks booth at NRF earlier this year.

Head of AI Center of Excellence

Sailesh Bharathwaaj Krishnamurthy

AI Agents for Marketing: Leveraging Mosaic AI to Create a Multi-Purpose Agentic Marketing Assistant

This course will introduce you to AI agents, their transformative impact on organizations, and how Databricks enables the creation of AI agent systems. We’ll begin by exploring what AI agents are, how they differ from traditional AI systems, and why they are becoming essential in today’s data-driven landscape. Next, we’ll examine how AI agents can be used to automate tasks, enhance decision-making, and unlock new efficiencies for businesses of all sizes. Finally, we’ll review real-world examples of AI agent systems on Databricks, showcasing practical applications across industries and sharing key considerations for successful adoption. You can pass a short quiz and earn a badge to validate your learning on completion.

AI Agents Fundamentals Training

This in-person, full-day hackathon focuses on the development of innovative AI Agents using the Databricks Data Intelligence Platform. Collaborating in teams of up to four, participants will utilize Databricks' specialized agent authoring and evaluation tools to build, test, and refine intelligent agent systems. Diverse datasets from the Databricks Marketplace are available to enhance agent capabilities. The objective is to produce a compelling proof-of-concept agent showcasing creativity, intelligent data utilization, and effective tool-calling in a novel and useful manner. This event provides a platform for demonstrating technical quality with Databricks tools, creativity in agent design or application, and clarity of purpose. The hackathon promotes hands-on experience with cutting-edge agent development tools and concludes with short team demonstrations of proofs of concept created during the event. Three finalist teams will be selected, and the winners will be announced at the end of the Hackathon. Cash prizes will be awarded to the top teams, with $10,000 for first place, $5,000 for second place, and $2,500 for third place. Complete details regarding eligibility and the rules governing this hackathon are available in the official rules, available at http://bit.ly/44HRyxz. In the event of any discrepancies between the official rules and other hackathon materials, the official rules govern.Agenda:7:30am Registration/Breakfast8:15am Opening Ceremony8:30am Hacking Begins12:00pm- 1:30pm Lunch2:30pm Hacking Ends2:30pm- 3:45pm Expo/Judging3:45pm Closing Ceremony/Winners Announced4:00pm Hackathon Ends

AI Agents Hackathon

LLM agents aren’t just answering questions — they’re running entire workflows. In this talk, we’ll show how agents can autonomously ingest, process and structure unstructured data using Unstructured, with outputs flowing directly into Databricks. Powered by the Model Context Protocol (MCP), agents can interface with Unstructured’s full suite of capabilities — discovering documents across sources, building ephemeral workflows and exporting structured insights into Delta tables. We’ll walk through a demo where an agent responds to a natural language request, dynamically pulls relevant documents, transforms them into usable data and surfaces insights — fast. Join us for a sneak peek into the future of AI-native data workflows, where LLMs don’t just assist — they operate.

Head of Product and Engineering

Christopher Maddock

AI Agents in Action: Structuring Unstructured Data on Demand With Databricks and Unstructured

This session is repeated. Improving healthcare impacts us all. We highlight how Premier Inc. took risk-adjusted patient data from more than 1,300 member hospitals across America, applying a natural language interface using AI/BI Genie, allowing our users to discover new insights. The stakes are high, new insights surfaced represent potential care improvement and lives positively impacted. Using Genie and our AI-ready data in Unity Catalog, our team was able to stand up a Genie instance in three short days, bypassing costs and time of custom modeling and application development. Additionally, Genie allowed our internal teams to generate complex SQL, as much as 10 times faster than writing it by hand. As Genie and lakehouse apps continue to advance rapidly, we are excited to leverage these features by introducing Genie to as many as 20,000 users across hundreds of hospitals. This will support our members’ ongoing mission to enhance the care they provide to the communities they serve.

Software Architect

Jay Sharma

Senior Director, Analytics

Tim Riddle

AI and Genie: Analyzing Healthcare Improvement Opportunities

Is your AI evaluation process holding back your system's true potential? Many organizations struggle with improving GenAI quality because they don't know how to measure it effectively. This research session covers the principles of GenAI evaluation, offers a framework for measuring what truly matters, and demonstrates implementation using Databricks.Key Takeaways:-Practical approaches for establishing reliable metrics for subjective evaluations-Techniques for calibrating LLM judges to enable cost-effective, scalable assessment-Actionable frameworks for evaluation systems that evolve with your AI capabilitiesWhether you're developing models, implementing AI solutions, or leading technical teams, this session will equip you to define meaningful quality metrics for your specific use cases and build evaluation systems that expose what's working and what isn't, transforming AI guesswork into measurable success.

Chief Scientist - Neural Networks

Jonathan Frankle

Research Scientist

Pallavi Koppol

AI Evaluation from First Principles: You Can't Manage What You Can't Measure

Stuck on a treadmill of endless report building requests? Wondering how you can ship reliable AI products to internal users and even customers? Omni is a BI and embedded analytics platform on Databricks that lets users answer their own data questions – sometimes with a little AI help. No magic, no miracles – just smart tooling that cuts through the noise and leverages well-known concepts (semantic layer, anyone?) to improve accuracy and delight users. This talk is your blueprint for getting reliable AI use cases into production and reaching the promised land of contagious self-service.

Co-Founder & President

James Davidson

AI for BI without the BS

This high-velocity workshop is designed for data and AI leaders seeking to rapidly develop a comprehensive AI strategy tailored to their organization's needs. In just 30 minutes, participants will engage in a focused, interactive session that delivers actionable insights and a strategic framework for AI implementation. Key components of the workshop include: <ul>	<li>Rapid assessment: Quickly evaluate your organization's AI readiness and potential impact areas</li>	<li>Strategic alignment: Align AI initiatives with core business objectives and value creation opportunities</li>	<li>Resource optimization: Identify critical resources, skills and technologies required for successful AI adoption</li>	<li>Risk mitigation: Address key challenges and ethical considerations in AI deployment</li>	<li>Priority areas: Where to focus first to get the best start</li></ul> By the end of this intensive session, you will have the foundation of a robust AI strategy and guidance on roadmap execution.

Lead Data & AI Strategist

Lexy Kassan

AI in Motion: Build a Roadmap for Impact in Just 30 Minutes


This session is repeated.Integrating AI into existing data workflows can be challenging, often requiring specialized knowledge and complex infrastructure. In this session, we'll share how SQL users can leverage AI/ML to access large language models (LLMs) and traditional machine learning directly from within SQL, simplifying the process of incorporating AI into data workflows. We will demonstrate how to use Databricks SQL for natural language processing, traditional machine learning, retrieval augmented generation and more. You'll learn about best practices and see examples of solving common use cases such as opinion mining, sentiment analysis, forecasting and other common AI/ML tasks.

Senior Engineering Manager

Sid Taneja

Youngbin Kim

AI Meets SQL: Leverage GenAI at Scale to Enrich Your Data

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.

Vice President, Decision Sciences

Boaz Super

Vice President , Database

Gairik Chakraborty

AI Powering Epsilon's Identity Strategy: Unified Marketing Platform on Databricks

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.

Sr. Product Manager

Alex Lichen

Principal Software Engineer

Chung Wu

AI-Assisted BI: Everything You Need to Know

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.

Sr Solutions Architect

Karuna Nadadur

Sr Specialist Solutions Architect

Srijit Chandrashekhar Nair

AI-Driven Drug Discovery: Accelerating Molecular Insights With NVIDIA and Databricks

This session is repeated. In today’s data landscape, the challenge isn’t just storing or processing data — it’s enabling every user, from data stewards to analysts, to find and trust the right data, fast. This session explores how Databricks is reimagining data discovery with the new Discover Page Experience — an intuitive, curated interface showcasing key data and workspace assets. We’ll dive into AI-assisted governance and AI-powered discovery features like AI-generated metadata, AI-assisted lineage and natural language data exploration in Unity Catalog. Plus, see how new certifications and deprecations bring clarity to complex data environments. Whether you’re a data steward highlighting trusted assets or an analyst navigating data without deep schema knowledge, this session will show how Databricks is making data discovery seamless for everyone.

Hongyi Zhang

Peter Wang

AI-Powered Data Discovery and Curation With Unity Catalog

Marketing teams struggle with ‘dirty data’ — incomplete, inconsistent, and inaccurate information that limits campaign effectiveness and reduces the accuracy of AI agents. Our AI-powered marketing data management platform, built on Databricks, solves this with anomaly detection, ML-driven transformations and the built-in Acxiom Referential Real ID Graph with Data Hygiene.We’ll showcase how Delta Lake, Unity Catalog and Lakeflow Declarative Pipelines power our multi-tenant architecture, enabling secure governance and 75% faster data processing. Our privacy-first design ensures compliance with GDPR, CCPA and HIPAA through role-based access, encryption key management and fine-grained data controls.Join us for a live demo and Q&A, where we’ll share real-world results and lessons learned in building a scalable, AI-driven marketing data solution with Databricks.

VP, Cloud Product Management & UX

Steven Kostrzewski

Chief Cloud & Data Modernization Officer

Ankur Jain

AI-Powered Marketing Data Management: Solving the Dirty Data Problem with Databricks

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.

Preview the
Sessions

Sessions