Join us for this insightful session to learn how you can leverage the power of the Microsoft ecosystem along with Azure Databricks to take your business to the next level. Azure Databricks is a fully integrated, native, first-party solution on Microsoft Azure. Databricks and Microsoft continue to actively collaborate on product development, ensuring tight integration, optimized performance, and a streamlined support experience. Azure Databricks offers seamless integrations with Power BI, Azure Open AI, Microsoft Purview, Azure Data Lake Storage (ADLS) and Foundry. In this session, you’ll learn how you can leverage deep integration between Azure Databricks and the Microsoft solutions to empower your organization to do more with your data estate. You’ll also get an exclusive sneak peek into the product roadmap.

Faced with the limitations of a legacy, on-prem data stack and scalability bottlenecks in MicroStrategy, Saddle Creek Logistics Services needed a modern solution to handle massive data volumes and accelerate insight delivery. By migrating to a cloud-native architecture powered by Sigma and Databricks, the team achieved significant performance gains and operational efficiency. In this session, Saddle Creek will walk through how they leveraged Databricks’ cloud-native processing engine alongside a unified governance layer through Unity Catalog to streamline and secure downstream analytics in Sigma. Learn how embedded dashboards and near real-time reporting—cutting latency from 9 minutes to just 3 seconds—have empowered data-driven collaboration with external partners and driven a major effort to consolidate over 30,000 reports and objects to under 1,000.

Using SQL for data transformation is a powerful way for an analytics team to create their own data pipelines. However, relying on SQL often comes with tradeoffs such as limited functionality, hard-to-maintain stored procedures or skipping best practices like version control and data tests. Databricks supports building high-performing SQL ETL workloads. Attend this session to hear how Databricks supports SQL for data transformation jobs as a core part of your Data Intelligence Platform. In this session we will cover 4 options to use Databricks with SQL syntax to create Delta tables: Lakeflow Declarative Pipelines: A declarative ETL option to simplify batch and streaming pipelines dbt: An open-source framework to apply engineering best practices to SQL based data transformations SQLMesh: an open-core product to easily build high-quality and high-performance data pipelines SQL notebooks jobs: a combination of Databricks Workflows and parameterized SQL notebooks

Join us to explore the dynamic partnership between FactSet and Databricks, transforming data accessibility and insights. Discover the launch of FactSet’s Structured DataFeeds via Delta Sharing on the Databricks Marketplace, enhancing access to crucial financial data insights. Learn about the advantages of streamlined data delivery and how this integration empowers data ecosystems. Beyond structured data, explore the innovative potential of vectorized data sharing of unstructured content such as news, transcripts, and filings. Gain insights into the importance of seamless vectorized data delivery to support GenAI applications and how FactSet is preparing to simplify client GenAI workflows with AI-ready data. Experience a demo that showcases the complete journey from data delivery to actionable GenAI application responses in a real-world Financial Services scenario. See firsthand how FactSet is simplifying client GenAI workflows with AI-ready data that drives faster, more informed financial decisions.

Timely and actionable insights are critical for staying competitive in today’s fast-paced environment. At HP Print, manual reporting for executive leadership (ELT) has been labor-intensive, hindering agility and productivity. To address this, we developed the Generative Insights Tool (GenIT) using Databricks Genie and Mosaic AI to create a real-time insights engine automating SQL generation, data visualization, and narrative creation. GenIT delivers instant insights, enabling faster decisions, greater productivity, and improved consistency while empowering leaders to respond to printer trends. With automated querying, AI-powered narratives, and a chatbot, GenIT reduces inefficiencies and ensures quality data and insights. Our roadmap integrates multi-modal data, enhances chatbot functionality, and scales globally. This initiative shows how HP Print leverages GenAI to improve decision-making, efficiency, and agility, and we will showcase this transformation at the Databricks AI Summit.

Discover how enterprises are turning SAP data into intelligent AI. By tapping into contextual SAP data through Delta Sharing on Databricks - no messy ETL needed - they’re accelerating AI innovation and business insights. Learn how they: - Build domain-specific AI that can reason on private SAP data- Deliver data intelligence to power insights for business leaders- Govern and secure their new unified data estate

The News Sentiment application enhances the discoverability of news content through our flagship platform, Capital IQ Pro. We processed news articles for 10,000+ public companies through entity recognition, along with a series of proprietary financial sentiment models to assess whether the news was positive or negative, as well as its significance and relevance to the company. We built a database containing over 1.5 million signals and operationalized the end-to-end ETL as a daily Workflow on Databricks. The development process included model training and selection. We utilized training data from our internal financial analysts to train Google’s T5-Flan to create our proprietary sentiment model and two additional models. Our models are deployed on Databricks Model-Serving as serverless endpoints that can be queried on-demand. The last phase of the project was to develop a UI, in which we utilized Databricks serverless SQL warehouses to surface this data in real-time.

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

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: Use campaign briefs to generate campaign ideas and taglines Do copy-writing for marketing content Verify images for messaging accuracy Answer general questions and browse the web as a generic assistant 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.

Conagra is a global food manufacturer with $12.2B in revenue, 18K+ employees, 45+ plants in US, Canada and Mexico. Conagra's Supply Chain organization is heavily focused on delivering results in productivity, waste reduction, inventory rationalization, safety and customer service levels. By migrating the Supply Chain reporting suite to Databricks over the past 2 years, Conagra's Supply Chain Analytics & Data Science team has been able to deliver new AI solutions which complement traditional BI platforms and lay the foundation for additional AI/ML applications in the future. With Databricks Genie integrated within traditional BI reports, Conagra Supply Chain users can now go from insight to action faster and with fewer clicks, enabling speed to value in a complex Supply Chain. The Databricks platform also allows the team to curate data products to be consumed by traditional BI applications today as well as the ability to rapidly scale for the AI/ML applications of tomorrow.

This session is repeated. Integrating AI agents into business systems requires tailored approaches for different maturity levels (crawl-walk-run) that balance scalability, accuracy and usability. This session addresses the critical challenge of making AI agents accessible to business users. We will explore four key integration methods: Databricks apps: The fastest way to build and run applications that leverage your data, with the full security and governance of Databricks Genie: Tool enabling non-technical users to gain data insights on Structured Data through natural language queries Chatbots: Combine real-time data retrieval with generative AI for contextual responses and process automation Batch inference: Scalable, asynchronous processing for large-scale AI tasks, optimizing efficiency and cost We'll compare these approaches, discussing their strengths, challenges and ideal use cases to help businesses select the most suitable integration strategy for their specific needs.

As data ecosystems grow more complex, organizations often struggle with siloed platforms and fragmented governance. In this session, we’ll explore how our team made Databricks the central hub for cross-platform interoperability, enabling seamless Snowflake integration through Unity Catalog and the Iceberg REST API. We’ll cover: Why interoperability matters and the business drivers behind our approach How Unity Catalog and Uniform simplify interoperability, allowing Databricks to expose an Iceberg REST API for external consumption Technical deep dive into data sharing, query performance, and access control across Databricks and Snowflake Lessons learned and best practices for building a multi-engine architecture while maintaining governance and efficiency By leveraging Uniform, Delta, and Iceberg, we created a flexible, vendor-agnostic architecture that bridges Databricks and Snowflake without compromising performance or security.

GenAI & machine learning are reshaping industries, driving innovation and redefining business strategies. As organizations embrace these technologies, they face significant challenges in managing AI initiatives effectively, such as balancing innovation with ethical integrity, operational resilience and regulatory compliance. This presentation introduces the Databricks AI Governance Framework (DAGF), a practical framework designed to empower organizations to navigate the complexities of AI. It provides strategies for building scalable, responsible AI programs that deliver measurable value, foster innovation and achieve long-term success. By examining the framework's five foundational pillars — AI organization, ethics, legal and regulatory compliance, transparency and interpretability, AI operations and infrastructure and AI security — this session highlights how AI governance aligns programs with the organization's strategic goals, mitigates risks and builds trust across stakeholders.

Demonstrating a real ROI is key to driving executive and stakeholder buy-in for major technology changes. At Veeam, we aligned our Databricks Platform change with projects to increase sales pipeline and improve customer retention. By delivering targeted improvements on those critical business metrics, we created positive ROI in short order while at the same time setting the foundation for long term Databricks Platform success. This session targets data and business leaders looking to understand how they can turn their infrastructure change into a business revenue driver.

NHS England is revolutionizing healthcare research by enabling secure, seamless access to de-identified patient data through the Federated Data Platform (FDP). Despite vast data resources spread across regional and national systems, analysts struggle with fragmented, inconsistent datasets. Enter Databricks: powering a unified, virtual data lake with Unity Catalog at its core — integrating diverse NHS systems while ensuring compliance and security. By bridging AWS and Azure environments with a private exchange and leveraging the Iceberg connector to interface with Palantir, analysts gain scalable, reliable and governed access to vital healthcare data. This talk explores how this innovative architecture is driving actionable insights, accelerating research and ultimately improving patient outcomes.

Swiggy, India's leading quick commerce platform, serves ~13 million users across 653 cities, with 196,000 restaurant partners and 17,000 SKUs. To handle this scale, Swiggy developed a secure, scalable AI platform processing millions of predictions per second. The tech stack includes Apache Kafka for real-time streaming, Apache Spark on Databricks for analytics and ML, and Apache Flink for stream processing. The Lakehouse architecture on Delta ensures data reliability, while Unity Catalog enables centralized access control and auditing. These technologies power critical AI applications like demand forecasting, route optimization, personalized recommendations, predictive delivery SLAs, and generative AI use cases.Key Takeaway:This session explores building a data platform at scale, focusing on cost efficiency, simplicity, and speed, empowering Swiggy to seamlessly support millions of users and AI use cases.

Unlock the full potential of your BI tools with Databricks. This session explores how features like Photon, Databricks SQL, Liquid Clustering, AI/BI Genie and Publish to Power BI enhance performance, scalability and user experience. Learn how Databricks accelerates query performance, optimizes data layouts and integrates seamlessly with BI tools. Gain actionable insights and best practices to improve analytics efficiency, reduce latency and drive better decision-making. Whether migrating from a data warehouse or optimizing an existing setup, this talk provides the strategies to elevate your BI capabilities.

This session is repeated. If you are brand new to Databricks SQL and want to get a lightning tour of this intelligent data warehouse, this session is for you. Learn about the architecture of Databricks SQL. Then show how simple, streamlined interfaces are making it easier for analysts, developers, admins and business users to get their jobs done and questions answered. We’ll show how easy it is to create a warehouse, get data, transform it and build queries and dashboards. By the end of the session, you’ll be able to build a Databricks SQL warehouse in 5 minutes.

In the contemporary landscape of data management, organizations are increasingly faced with the challenges of data segregation, governance and permission management, particularly when operating within complex structures such as holding companies with multiple subsidiaries. Unipol comprises seven subsidiary companies, each with a diverse array of workgroups, leading to a cumulative total of multiple operational groups. This intricate organizational structure necessitates a meticulous approach to data management, particularly regarding the segregation of data and the assignment of precise read-and-write permissions tailored to each workgroup. The challenge lies in ensuring that sensitive data remains protected while enabling seamless access for authorized users. This speech wants to demonstrate how Unity Catalog emerges as a pivotal tool in the daily use of the data platform, offering a unified governance solution that supports data management across diverse AWS environments.

Join us Tuesday June 10th, 9:10-12:10 PM PT Hosted by Databricks CMO, Rick Schultz, hear from executives and speakers at PetSmart, Valentino, Domino’s, AirTable, Dropbox, ThredUp, Grammarly, Deloitte, and more. Come for actionable strategies and real-world examples: Hear from marketing experts on how to build data and AI-driven marketing organizations. Learn how Databricks Marketing supercharges impact using the Data Intelligence Platform; scaling personalization, building more efficient campaigns, and empowering marketers to self-serve insights.