Supporting the scale of Public Sector data and the need to protect sensitive information is essential to public sector organizations.

Learn how Confluent simplifies real-time streaming of your SAP data into AI-ready Delta tables on Databricks. In this session, you'll see how Confluent’s fully managed data streaming platform—with unified Apache Kafka® and Apache Flink®—connects data from SAP S/4HANA, ECC, and 120+ other sources to enable easy development of trusted, real-time data products that fuel highly contextualized AI and analytics. With Tableflow, you can represent Kafka topics as Delta tables in just a few clicks—eliminating brittle batch jobs and custom pipelines. You’ll see a product demo showcasing how Confluent unites your SAP and Databricks environments to unlock ERP-fueled AI, all while reducing the total cost of ownership (TCO) for data streaming by up to 60%.

With expensive contracts up for renewal, Evri faced the challenge of migrating 1,000 SAP HANA assets and 200+ Talend jobs to Databricks. This talk will cover how we transformed SAP HANA and Talend workflows into modern Databricks pipelines through AI-powered translation and validation -- without months of manual coding. We'll cover:- Techniques for handling SAP HANA's proprietary formats- Approaches for refactoring incremental pipelines while ensuring dashboard stability- The technology enabling automated translation of complex business logic- Validation strategies that guarantee migration accuracye'll share real examples of SAP HANA stored procedures transformed into Databricks code and demonstrate how we maintained 100% uptime of critical dashboards during the transition. Join us to discover how AI is revolutionizing what's possible in enterprise migrations from GUI-based legacy systems to modern, code-first data platforms.

In regulated industries like finance, agility can't come at the cost of compliance. Morgan Stanley found the answer in combining Dataiku and Databricks to create a governed, collaborative ecosystem for machine learning and predictive analytics. This session explores how the firm accelerated model development and decision-making, reducing time-to-insight by 50% while maintaining full audit readiness. Learn how no-code workflows empowered business users, while scalable infrastructure powered Terabyte-scale ML. Discover best practices for unified data governance, risk automation, and cross-functional collaboration that unlock innovation without compromising security. Ideal for data leaders and ML practitioners in regulated industries looking to harmonize speed, control, and value.

As a leading personalized product retailer, Shutterfly needed a modern, secure, and performant data foundation to power GenAI-driven customer experiences. However, their existing stack was creating roadblocks in performance, governance, and machine learning scalability. In partnership with Impetus, Shutterfly embarked on a multi-phase migration to Databricks Unity Catalog. This transformation not only accelerated Shutterfly’s ability to provide AI-driven personalization at scale but also improved governance, reduced operational overhead, and laid a scalable foundation for GenAI innovation. Join experts from Databricks, Impetus, and Shutterfly to discover how this collaboration enabled faster data-driven decision-making, simplified compliance, and unlocked the agility needed to meet evolving customer demands in the GenAI era. Learn from their journey and take away best practices for your own modernization efforts.

Behind every powerful AI system lies a critical foundation: fresh, high-quality web data. This session explores the symbiotic relationship between web scraping and artificial intelligence that's transforming how technical teams build data-intensive applications. We'll showcase how this partnership enables crucial use cases: analyzing trends, forecasting behaviors, and enhancing AI models with real-time information. Technical challenges that once made web scraping prohibitively complex are now being solved through the very AI systems they help create. You'll learn how machine learning revolutionizes web data collection, making previously impossible scraping projects both feasible and maintainable, while dramatically reducing engineering overhead and improving data quality. Join us to explore this quiet but critical partnership that's powering the next generation of AI applications.

Join Sandy Steiger, Head of Advanced Analytics & Automation (formerly at TQL), as she walks through how her team tackled one of the most common and least talked about problems in data teams: report bloat, data blind spots, and broken trust with the business. You’ll learn how TQL went from 3,000 reports to fewer than 500 while gaining better visibility, faster data issue resolution, and cloud agility through practical use of lineage, automated detection, and surprising outcomes from implementing Pantomath (an automated data operations platform). Sandy will share how her team identified upstream issues (before Microsoft did), avoided major downstream breakages, and built the credibility every data team needs to earn trust from the business. Walk away with a playbook for using automation to drive smarter, faster decisions across your organization.

AI initiatives often stall when data teams can’t keep up with business demand for ad hoc, self-service data. Whether it’s AI agents, BI tools, or business users—everyone needs data immediately, but the pipeline-centric modern data stack is not built for this scale of agility. Promethium enables the data teams to generate instant, contextual data products called Data Answers based on rapid, exploratory questions from the business. Data Answers empower data teams for AI-scale collaboration with the business. We will demo Promethium’s new agent capability to build data answers on Databricks for self-service data. The Promethium agent leverages and extends Genie with context from other enterprise data and applications to ensure accuracy and relevance.

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.

Customer Data Platforms (CDPs) promise better engagement, higher operational efficiency, and revenue growth by centralizing and streamlining access to customer data. However, consolidating sensitive information from a variety of sources creates complex challenges around data governance, security, and privacy. We’ve studied, built, and managed data protection strategies at some of the world’s biggest retailers. We’ll showcase business requirements, common architectural components, and best practices to deploy data protection solutions at scale, protecting billions of sensitive records across regions and countries. Learn how a data vault pattern with granular, policy-based access control and monitoring can improve organizational privacy posture and help meet regulatory requirements (e.g., GDPR, CCPA, e-Privacy). Walk away with a clear framework to deploy such architecture and knowledge of real-world issues, performance optimizations, and design trade-offs

In this session, we take you inside a new kind of data foundation—one that moves beyond traditional tables and metrics to create meaning. We’ll explore how turning metadata into a system of understanding, not just record-keeping, can unlock powerful agentic workflows. You’ll see how business terms like "cost variance" or "lead time" become executable, traceable, and reusable assets that guide AI agents, ensure consistency, and restore trust across decentralized teams. Drawing on real challenges from the CPG world, we’ll walk through how companies are moving from governance battles to collaborative ownership, from static reports to living data definitions, and from disconnected data to decisions that act with confidence and speed. The future of AI isn't just about smarter models—it’s about smarter context. This is your roadmap for transforming your data foundation into a shared language between people and machines.

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.

Come learn about the latest tools to keep your team under budget and optimize your data ROI. Managing the costs of data consumption, attributing to projects, and understanding TCO is top of mind for customers. We will discuss implementing FinOps, observability around cost management. Workloads, consumption, users, what they worked on - and how to optimize it all.

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.