Empower AI and agents with trusted data and metadata from an end-to-end unified system. Discover how Salesforce Data Cloud, Agentforce, and Databricks work together to fuel automation, AI, and analytics through a unified data strategy—driving real-time intelligence, enabling zero-copy data sharing, and unlocking scalable activation across the enterprise.

This session will explore how developers can easily select, extract, filter, and control data pre-ingestion to accelerate safe AI. Learn how the Securiti and Databricks partnership empowers Databricks users by providing the critical foundation for unlocking scalability and accelerating trustworthy AI development and adoption.Key Takeaways:● Understand how to leverage data intelligence to establish a foundation for frameworks like OWASP top 10 for LLM’s, NIST AI RMF and Gartner’s TRiSM.● Learn how automated data curation and synching address specific risks while accelerating AI development in Databricks.● Discover how leading organizations are able to apply robust access controls across vast swaths of mostly unstructured data● Learn how to maintain data provenance and control as data is moved and transformed through complex pipelines in the Databricks platform.

The migration to the Databricks Data Intelligence Platform has enabled Techcombank to more efficiently unify data from over 50 systems, improve governance, streamline daily operational analytics pipelines and use advanced analytics tools and AI to create more meaningful and personalized experiences for customers. With Databricks, Techcombank has also introduced key solutions that are reshaping its digital banking services: AI-driven lead management system: Techcombank's internally developed AI program called 'Lead Allocation Curated Engine' (LACE) optimizes lead management and provides relationship managers with enriched insights for smarter lead allocation to drive business growth. AI-powered program for digital banking inclusion of small businesses: An AI-powered GeoSense assists frontline workers with analytics-driven insights about which small businesses and merchants to engage in the bank's digital ecosystem. And more examples, which will be presented.

Join us to learn how the UK's Department for Environment, Food & Rural Affairs (DEFRA) transformed data use with Databricks’ Unity Catalog, enabling nationwide projects through secure, scalable analytics. DEFRA safeguards the UK's natural environment. Historical fragmentation of data, talent and tools across siloed platforms and organizations, made it difficult to fully exploit the department’s rich data. DEFRA launched its Data Analytics & Science Hub (DASH), powered by the Databricks Data Intelligence Platform, to unify its data ecosystem. DASH enables hundreds of users to access and share datasets securely. A flagship example demonstrates its power, using Databricks to process aerial photography and satellite data to identify peatlands in need of restoration — a complex task made possible through unified data governance, scalable compute and AI. Attendees will hear about DEFRA’s journey, learn valuable lessons about building a platform crossing organizational boundaries.

Change data feeds are a common tool for synchronizing changes between tables and performing data processing in a scalable fashion. Serverless architectures offer a compelling solution for organizations looking to avoid the complexity of managing infrastructure. But how can you bring CDFs into a serverless environment? In this session, we'll explore how to integrate Change Data Feeds into serverless architectures using Delta-rs and Delta-kernel-rs—open-source projects that allow you to read Delta tables and their change data feeds in Rust or Python. We’ll demonstrate how to use these tools with Lakestore’s serverless platform to easily stream and process changes. You’ll learn how to: Leverage Delta tables and CDFs in serverless environments Utilize Databricks and Unity Catalog without needing Apache Spark

The Pacers Sports Group has had an amazing year. The Indianapolis Pacers in the NBA finals for the first time in 25 years. The Fever are setting attendance and viewership records with WNBA celebrity Caitlin Clark. Hear how they have transformed their data and AI capabilities for marketing, fan behavior insights, season ticket propensity models, and democratization to their non-technical personas. And receiving a 12,000x cost reduction down to just $8 a year switching to Databricks.

PySpark supports many data sources out of the box, such as Apache Kafka, JDBC, ODBC, Delta Lake, etc. However, some older systems, such as systems that use JMS protocol, are not supported by default and require considerable extra work for developers to read from them. One such example is ActiveMQ for streaming. Traditionally, users of ActiveMQ have to use a middle-man in order to read the stream with Spark (such as writing to a MySQL DB using Java code and reading that table with Spark JDBC). With PySpark 4.0’s custom data sources (supported in DBR 15.3+) we are able to cut out the middle-man processing using batch or Spark Streaming and consume the queues directly from PySpark, saving developers considerable time and complexity in getting source data into your Delta Lake and governed by Unity Catalog and orchestrated with Databricks Workflows.

Step into the world of Disney Streaming as we unveil the creation of our Foundational Medallion, a cornerstone in our architecture that redefines how we manage data at scale. In this session, we'll explore how we tackled the multi-faceted challenges of building a consistent, self-service surrogate key architecture — a foundational dataset for every ingested stream powering Disney Streaming's data-driven decisions. Learn how we streamlined our architecture and unlocked new efficiencies by leveraging cutting-edge Databricks features such as liquid clustering, Photon with dynamic file pruning, Delta's identity column, Unity Catalog and more — transforming our implementation into a simpler, more scalable solution. Join us on this thrilling journey as we navigate the twists and turns of designing and implementing a new Medallion at scale — the very heartbeat of our streaming business!

Modern data science teams face the challenge of navigating complex landscapes of languages, tools and infrastructure. Positron, Posit’s next-generation IDE, offers a powerful environment tailored for data science, seamlessly integrating with Databricks to empower teams working in Python and R. Now integrated within Posit Workbench, Positron enables data scientists to efficiently develop, iterate and analyze data with Databricks — all while maintaining their preferred workflows. In this session, we’ll explore how Python and R users can develop, deploy and scale their data science workflows by combining Posit tools with Databricks. We’ll showcase how Positron simplifies development for both Python and R and how Posit Connect enables seamless deployment of applications, reports and APIs powered by Databricks. Join us to see how Posit + Databricks create a frictionless, scalable and collaborative data science experience — so your teams can focus on insights, not infrastructure.

In a world where data collaboration is essential but trust is scarce, Databricks Clean Rooms delivers a game-changing model: no data shared, all value gained. Discover how data providers can unlock new revenue streams by launching subscription-based analytics and “Built-on-Databricks” services that run on customer data — without exposing raw data or violating compliance. Clean Rooms integrates Unity Catalog’s governance, Delta Sharing’s secure exchange and serverless compute to enable true multi-party collaboration — without moving data. See how privacy-preserving models like fraud detection, clinical analytics and ad measurement become scalable, productizable and monetizable across industries. Walk away with a proven pattern to productize analytics, preserve compliance and turn trustless collaboration into recurring revenue.

Coinbase leverages Databricks to scale ML on blockchain data, turning vast transaction networks into actionable insights. This session explores how Databricks’ scalable infrastructure, powered by Delta Lake, enables real-time processing for ML applications like NFT floor price predictions. We’ll show how GraphFrames helps us analyze billion-node transaction graphs (e.g., Bitcoin) for clustering and fraud detection, uncovering structural patterns in blockchain data. But traditional graph analytics has limits. We’ll go further with Graph Neural Networks (GNNs) using Kumo AI, which learn from the transaction network itself rather than relying on hand-engineered features. By encoding relationships directly into the model, GNNs adapt to new fraud tactics, capturing subtle relationships that evolve over time. Join us to see how Coinbase is advancing blockchain ML with Databricks and deep learning on graphs.

An intelligent, action-driven approach to bridge Data Engineering and AI/ML workflows, delivering continuous data trust through comprehensive monitoring, validation, and remediation across the entire Databricks data lifecycle. Learn how Acceldata’s Agentic Data Management (ADM) platform: Ensures end-to-end data reliability across Databricks from ingestion, transformation, feature engineering, and model deployment. Bridges data engineering and AI teams by providing unified insights across Databricks jobs, notebooks and pipelines with proactive data insights and actions. Accelerates the delivery of trustworthy enterprise AI outcomes by detecting multi-variate anomalies, monitoring feature drift, and maintaining lineage within Databricks-native environments.

Riot Games reduced its Databricks compute spend and accelerated development cycles by transforming its data engineering workflows—migrating from bespoke Databricks notebooks and Spark pipelines to a scalable, testable, and developer-friendly dbt-based architecture. In this talk, members of the Developer Experience & Automation (DEA) team will walk through how they designed and operationalized dbt to support Riot’s evolving data needs.

Enterprise customers need a powerful and adaptable data foundation to navigate demands of AI and multi-cloud environments. This session dives into how Google Cloud Storage serves as a unified platform for modern analytics data lakes, together with Databricks. Discover how Google Cloud Storage provides key innovations like performance optimizations for Apache Iceberg, Anywhere Cache as the easiest way to colocate storage and compute, Rapid Storage for ultra low latency object reads and appends, and Storage Intelligence for vital data insights and recommendations. Learn how you can optimize your infrastructure to unlock the full value of your data for AI-driven success.

Join us for this session on how to build AI finance agents with Databricks and LangChain. This session introduces a powerful approach to building AI agents by combining a modular framework that integrates LangChain, retrieval-augmented generation (RAG), and Databricks' unified data platform to build intelligent, adaptable finance agents. We’ll walk through the architecture and key components, including Databricks Unity Catalog, ML Flow, and Mosaic AI involved in building a system tailored for complex financial tasks like portfolio analysis, reporting automation, and real-time risk insights. We’ll also showcase a demo of one such agent in action - a Financial Analyst Agent. This agent emulates the expertise of a seasoned data analyst, delivering in-depth analysis in seconds - eliminating the need to wait hours or days for manual reports. The solution provides organizations with 24/7 access to advanced data analysis, enabling faster, smarter decision-making.

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: To decommission legacy data platforms and reduce data sprawl by migrating 20+ core banking systems to a multi-tenant Azure Databricks architecture To leverage Databrick’s delta-share capabilities to address SMBC’s unique global footprint and data sharing needs To govern data by design using Unity Catalog To achieve global adoption of the frameworks, accelerators, architecture and tool stack to support similar implementations across EMEA Deloitte and SMBC leveraged the Brickbuilder asset “Data as a Service for Banking” to accelerate this highly strategic transformation.

American Airlines migrated from Hive Metastore to Unity Catalog using automated processes with Databricks APIs and GitHub Actions. This automation streamlined the migration for many applications within AA, ensuring consistency, efficiency and minimal disruption while enhancing data governance and disaster recovery capabilities.

Ready to go beyond the basics of Mosaic AI? This session will walk you through how to architect and scale production-grade AI systems on the Databricks Data Intelligence Platform. We’ll cover practical techniques for building end-to-end AI pipelines — from processing structured and unstructured data to applying Mosaic AI tools and functions for model development, deployment and monitoring. You’ll learn how to integrate experiment tracking with MLflow, apply performance tuning and use built-in frameworks to manage the full AI lifecycle. By the end, you’ll be equipped to design, deploy and maintain AI systems that deliver measurable outcomes at enterprise scale.

Want to create AI agents that can do more than just generate text? Join us to explore how combining Databricks' Mosaic AI Agent Framework with the Model Context Protocol (MCP) unlocks powerful tool-calling capabilities. We'll show you how MCP provides a standardized way for AI agents to interact with external tools, data and APIs, solving the headache of fragmented integration approaches. Learn to build agents that can retrieve both structured and unstructured data, execute custom code and tackle real enterprise challenges. Key takeaways: Implementing MCP-enabled tool-calling in your AI agents Prototyping in AI Playground and exporting for deployment Integrating Unity Catalog functions as agent tools Ensuring governance and security for enterprise deployments Whether you're building customer service bots or data analysis assistants, you'll leave with practical know-how to create powerful, governed AI agents.

ClickHouse is a C++ based, column-oriented database built for real-time analytics. While it has its own internal storage format, the rise of open lakehouse architectures has created a growing need for seamless interoperability. In response, we have developed integrations with your favorite lakehouse ecosystem to enhance compatibility, performance and governance. From integrating with Unity Catalog to embedding the Delta Kernel into ClickHouse, this session will explore the key design considerations behind these integrations, their benefits to the community, the lessons learned and future opportunities for improved compatibility and seamless integration.