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.

The web is on the verge of a major shift. Agentic applications will redefine how customers engage with digital experiences—delivering highly personalized, relevant interactions. In this talk, Snowplow CTO Yali Sassoon explores how Snowplow Signals enables agents to perceive users through short- and long-term memory, natively on the Databricks Data Intelligence Platform.

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.

Amgen is advancing its Enterprise Data Fabric to securely manage sensitive multimodal data, such as imaging and research data, across formats.Databricks is already the de facto standard for governance on structured data, and Amgen seeks to extend it for unstructured multi modal data too. This approach will also allow Amgen to standardize its GenAI projects on Databricks. Key priorities include: Centralized data access: establishing a unified, secure access control system Enhanced traceability: implementing detailed processes for transparency and accountability Consistent access standards: ensuring uniform data access privilege experience User support: providing flexible access for diverse stakeholders Comprehensive auditing: enabling thorough permission audits and data usage tracking Learn strategies for implementing a comprehensive multimodal data governance framework using Databricks, as we share our experience on standardizing data governance for GenAI use cases.

Effective Identity and Access Management (IAM) is essential for securing enterprise environments while enabling innovation and collaboration. As companies scale, ensuring users have the right access without adding administrative overhead is critical. In this session, we’ll explore how Databricks is simplifying identity management by integrating with customers’ Identity Providers (IDPs). Learn about Automatic Identity Management in Azure Databricks, which eliminates SCIM for Entra ID users and ensures scalable identity provisioning for other IDPs. We'll also cover externally managed groups, PIM integration and upcoming enhancements like a bring-your-own-IDP model for Google Cloud. Through a customer success story and live demo, see how Databricks is making IAM more scalable, secure and user-friendly.

This intermediate-level presentation will explore the various methods we've leveraged within Databricks to deliver and evaluate guardrail models for AI safety. From prompt engineering with custom built frameworks to hosting models served from the market place and beyond. We've utilized GPU within clusters to fine-tune and run large open sourced models at inference such as Llama Guard 3.1 and generate synthetic datasets based on questions we've received from production.

Life as a father, tech leader, and fitness enthusiast demands efficiency. To reclaim my time, I’ve built AI-driven solutions that automate everyday tasks—from research agents that prep for podcasts to multi-agent systems that plan meals—all powered by real-time data and automation. This session dives into the technical foundations of these solutions, focusing on event-driven agent design and scalable patterns for robust AI systems. You’ll discover how Databricks technologies like Delta Lake, for reliable and scalable data management, and DSPy, for streamlining the development of generative AI workflows, empower seamless decision-making and deliver actionable insights. Through detailed architecture diagrams and a live demo, I’ll showcase how to design systems that process data in motion to tackle complex, real-world problems. Whether you’re an engineer, architect, or data scientist, you’ll leave with practical strategies to integrate AI-driven automation into your workflows.

Zillow has well-established, comprehensive systems for defining and enforcing data quality contracts and detecting anomalies.In this session, we will share how we evaluated Databricks’ native data quality features and why we chose Lakeflow Declarative Pipelines expectations for Lakeflow Declarative Pipelines, along with a combination of enforced constraints and self-defined queries for other job types. Our evaluation considered factors such as performance overhead, cost and scalability. We’ll highlight key improvements over our previous system and demonstrate how these choices have enabled Zillow to enforce scalable, production-grade data quality.Additionally, we are actively testing Databricks’ latest data quality innovations, including enhancements to lakehouse monitoring and the newly released DQX project from Databricks Labs.In summary, we will cover Zillow’s approach to data quality in the lakehouse, key lessons from our migration and actionable takeaways.

As organizations scale AI initiatives on platforms like Databricks, one challenge remains: bridging the gap between the data in the lakehouse and the vast, distributed data that lives elsewhere. Turning massive volumes of technical metadata into trusted, business-ready insight requires more than cataloging what's inside the lakehouse—it demands true enterprise-wide intelligence. Actian CTO Emma McGrattan will explore how combining Databricks Unity Catalog with the Actian Data Platform extends visibility, governance, and trust beyond the lakehouse. Learn how leading enterprises are: Integrating metadata across all enterprise data assets for complete visibility Enriching Unity Catalog metadata with business context for broader usability Empowering non-technical users to discover, trust, and act on AI-ready data Building a foundation for scalable data productization with governance by design

In the era of data-driven enterprises, true democratization requires more than just access–it demands context, trust, and governance at scale. In this session, discover how to seamlessly integrate Databricks Unity Catalog with Alation’s Enterprise Data Catalog to deliver: End-to-End Lineage Storytelling: Unify technical and business views into a single, cohesive narrative that resonates with both technical engineers and non-technical stakeholders across business domains Accelerated and Democratized Insights: Automate metadata stitching to reduce time-to-insight, enabling analysts to answer critical business questions faster and drive multi-domain collaboration Empowered, Trustworthy Discovery: Equip business users with a unified platform, populated with rich documentation and usage signals, so they can find, understand, and confidently use trusted data assets

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.

Organizations continue to struggle under the weight of data that still exists across multiple siloed sources, leaving data teams caught between their crumbling legacy data foundations and the race to build new AI and data-driven applications. Modern enterprises are quickly pivoting to data products that simplify and improve reusable data pipelines by joining data at massive scale and publishing it for internal users and the applications that drive business outcomes. Learn how Quantexa with Databricks enables an internal data marketplace to deliver the value that traditional data platforms never could.

This session showcases our journey of adopting Apache Iceberg™ to build a modern lakehouse architecture and leveraging Databricks advanced Iceberg support to take it to the next level. We’ll dive into the key design principles behind our lakehouse, the operational challenges we tackled and how Databricks enabled us to unlock enhanced performance, scalability and streamlined data workflows. Whether you’re exploring Apache Iceberg™ or building a lakehouse on Databricks, this session offers actionable insights, lessons learned and best practices for modern data engineering.

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: Data ETL with Lakeflow Declarative Pipelines and jobs Data storage for governance and access with Unity Catalog Code development with Notebooks Agent versioning and metric tracking with MLflow and Unity Catalog Evaluation and optimizations with Mosaic AI Agent Framework and DSPy Hosting infrastructure with monitoring with Model Serving and AI Gateway Front-end apps using Databricks Apps 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.

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.

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.

Barclays Post Trade real-time trade monitoring platform was historically built on a complex set of legacy technologies including Java, Solace, and custom micro-services.This session will demonstrate how the power of Lakeflow Declarative Pipelines' new real-time mode, in conjunction with the foreach_batch_sink, can enable simple, cost-effective streaming pipelines that can load high volumes of data into Databricks new Serverless OLTP database with very low latency.Once in our OLTP database, this can be used to update real-time trading dashboards, securely hosted in Databricks Apps, with the latest stock trades - enabling better, more responsive decision-making and alerting.The session will walk-through the architecture, and demonstrate how simple it is to create and manage the pipelines and apps within the Databricks environment.

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.

Join us as we unveil how we transformed the largest Databricks workspace into the best-in-class lakehouse through Unity Catalog. Discover how we harnessed lineage and unified access management to build ultimate governance automation.