Building an open data lakehouse? Start with the right blueprint. This session walks through common reference architectures for interoperable lakehouse deployments across AWS, Google Cloud, Azure and tools like Snowflake, BigQuery and Microsoft Fabric. Learn how to design for cross-platform data access, unify governance with Unity Catalog and ensure your stack is future-ready — no matter where your data lives.

This session showcases how both Westinghouse Electric and Alabama Power Company are leveraging cloud-based tools, advanced analytics, and machine learning to transform operational resilience across the energy sector. In the first segment, we'll explore AI's crucial role in enhancing safety, efficiency, and compliance in nuclear operations through technologies like HiVE and Bertha, focusing on the unique reliability and credibility requirements of the regulated nuclear industry. We’ll then highlight how Alabama Power Company has modernized its grid management and storm preparedness by using Databricks to develop SPEAR and RAMP—applications that combine real-time data and predictive analytics to improve reliability, efficiency, and customer service.

Discover how SAP Business Data Cloud and Databricks can transform your business by unifying SAP and non-SAP data for advanced analytics and AI. In this session, we’ll highlight Optimizing Cash Flow with AI with integrated diverse data sources and AI algorithms that enable accurate cash flow forecasting to help businesses identify trends, prevent bottlenecks, and improve liquidity. You’ll also learn about the importance of high-quality, well-governed data as the foundation for reliable AI models and actionable reporting. Key Takeaways: • How to integrate and leverage SAP and external data in Databricks • Using AI for predictive analytics and better decision-making • Building a trusted data foundation to drive business performance Leave this session with actionable strategies to optimize your data, enhance efficiency, and unlock new growth opportunities.

Unlock the power of your SAP data with SAP Business Data Cloud—a fully managed SaaS solution that unifies and governs all SAP data while seamlessly connecting it with third-party data. As part of SAP Business Data Cloud, SAP Databricks brings together trusted, semantically rich business data with industry-leading capabilities in AI, machine learning, and data engineering. Discover how to access curated SAP data products across critical business processes, enrich and harmonize your data without data copies using Delta Sharing, and leverage the results across your business data fabric. See it all in action with a demonstration.

Unity Catalog (UC) enables governance and security for all data and AI assets within an enterprise’s data lake and is necessary to unlock the full potential of Databricks as a true Data Intelligence Platform. Unfortunately, UC migrations are non-trivial; especially for enterprises that have been using Databricks for more than five years, i.e., 7-Eleven. System Integrators (SIs) offer accelerators, guides, and services to support UC migrations; however, cloud infrastructure changes, anti-patterns within code, and data sprawl can significantly complicate UC migrations. There is no “shortcut” to success when planning and executing a complex UC migration. In this session, we will share how UCX by Databricks Labs, a UC Migration Assistant, allowed 7-Eleven to reorient their UC migration by leveraging assessments and workflows, etc., to assess, characterize, and ultimately plan a tenable approach for their UC migration.

In this session, we’ll walk through the latest advancements in platform security and compliance on Databricks — from networking updates to encryption, serverless security and new compliance certifications across AWS, Azure and Google Cloud. We’ll also share our roadmap and best practices for how to securely configure workloads on Databricks SQL Serverless, Unity Catalog, Mosaic AI and more — at scale. If you're building on Databricks and want to stay ahead of evolving risk and regulatory demands, this session is your guide.

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.

Delta Sharing enables cross-domain sharing of data assets for collaboration. A practical concern providers and recipients face in doing so is the need to manually configure network and storage firewalls. This is particularly challenging for large-scale providers and recipients with strict compliance requirements. In this talk, we will describe our solution to fully eliminate these complexities. This enhances user experience, scalability and security, facilitating seamless data collaboration across diverse environments and cloud platforms.

Elevate your AI initiatives on Databricks by harnessing the latest advancements in Google Cloud's Gemini models. Learn how to integrate Gemini's built-in reasoning and powerful development tools to build more dynamic and intelligent applications within your existing Databricks platform. We'll explore concrete ideas for agentic AI solutions, showcasing how Gemini can help you unlock new value from your data in Databricks.

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.

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.

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.

AI for enterprises, particularly in the era of GenAI, requires rapid experimentation and the ability to productionize models and agents quickly and at scale. Compliance, resilience and commercial flexibility drive the need to serve models across regions. As cloud providers struggle with rising demand for GPUs in environments, VM shortages have become commonplace, and add to the pressure of general cloud outages. Enterprises that can quickly leverage GPU capacity in other cloud regions will be better equipped to capitalize on the promise of AI, while staying flexible to serve distinct user bases and complying with regulations. In this presentation we will show and discuss how to implement AI deployments across cloud regions, deploying a model across regions and using a load balancer to determine where to best route a user request.

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.

Learn how Chevron transitioned their central finance and procurement analytics into the cloud using Databricks and ThoughtSpot’s Agentic Analytics Platform. Explore how Chevron leverages ThoughtSpot to unlock actionable insights, enhance their semantic layer with user-driven understanding, and ultimately drive more impactful strategies for customer engagement and business growth. In this session, Chevron explains the dos, don’ts, and best practices of migrating from outdated legacy business intelligence to real time, AI-powered insights.

Join cybersecurity leaders from SAP, Anvilogic, Capital One, Wiz, and Databricks to explore how modern data intelligence is transforming security operations. Discover how SAP adopted a modular, AI-powered detection engineering lifecycle using Anvilogic on Databricks. Learn how Capital One built a detection and correlation engine leveraging Delta Lake, Apache Spark Streaming, and Databricks to process millions of cybersecurity events per second. Finally, see how Wiz and Databricks’ partnership enhances cloud security with seamless threat visibility. Through expert insights and live demos, gain strategies to build scalable, efficient cybersecurity powered by data and AI.

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.

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.

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