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

Deloitte is observing a growing trend among cybersecurity organizations to develop big data management and analytics solutions beyond traditional Security Information and Event Management (SIEM) systems. Leveraging Databricks to extend these SIEM capabilities, Deloitte can help clients lower the cost of cyber data management while enabling scalable, cloud-native architectures. Deloitte helps clients design and implement cybersecurity data meshes, using Databricks as a foundational data lake platform to unify and govern security data at scale. Additionally, Deloitte extends clients’ cybersecurity capabilities by integrating advanced AI and machine learning solutions on Databricks, driving more proactive and automated cybersecurity solutions. Attendees will gain insight into how Deloitte is utilizing Databricks to manage enterprise cyber risks and deliver performant and innovative analytics and AI insights that traditional security tools and data platforms aren’t able to deliver.

SQL warehouses in Databricks can run more than just SQL. Join this session to learn how to get more out of your SQL warehouses and any tools built on top of it by leveraging Python. After attending this session, you will be familiar with Python user-defined functions and how to bring in custom dependencies from PyPi, as a custom wheel or even securely invoke cloud services with performance at scale.

Join this 20-minute session to learn how Informatica CDGC integrates with and leverages Unity Catalog metadata to provide end-to-end governance and security across an enterprise data landscape. Topics covered will include: Comprehensive data lineage that provides complete data transformation visibility across multicloud and hybrid environments -Broad data source support to facilitate holistic cataloging and a centralized governance framework Centralized access policy management and data stewardship to enable compliance with regulatory standards Rich data quality to ensure data is cleansed, validated and trusted for analytics and AI

Leveraging Databricks as a platform, we facilitate the sharing of anonymized datasets across various Databricks workspaces and accounts, spanning multiple cloud environments such as AWS, Azure, and Google Cloud. This capability, powered by Delta Sharing, extends both within and outside Sleep Number, enabling accelerated insights while ensuring compliance with data security and privacy standards. In this session, we will showcase our architecture and implementation strategy for data sharing, highlighting the use of Databricks’ Unity Catalog and Delta Sharing, along with integration with platforms like Jira, Jenkins, and Terraform to streamline project management and system orchestration.

Ensuring data security & meeting compliance requirements are critical priorities for businesses operating in regulated industries, where the stakes are high and the standards are stringent. We will showcase how CoorsTek, a global leader in technical ceramics MFG, partnered with Databricks to leverage the power of UC for addressing regulatory challenges while achieving significant operational efficiency gains. We'll dive into the migration journey, highlighting the adoption of key features such as RBAC, comprehensive data lineage tracking and robust auditing capabilities. Attendees will gain practical insights into the strategies and tools used to manage sensitive data, ensure compliance with industry standards and optimize cloud data architectures. Additionally, we’ll share real-world lessons learned, best practices for integrating compliance into a modern data ecosystem and actionable takeaways for leveraging Databricks as a catalyst for secure and compliant data innovation.

As connected vehicles generate vast amounts of personal and sensitive data, ensuring privacy and security in machine learning (ML) processes is essential. This session explores how Trusted Execution Environments (TEEs) and Azure Confidential Computing can enable privacy-preserving ML in cloud environments. We’ll present a method to recreate a vehicle environment in the cloud, where sensitive data remains private throughout model training, inference and deployment. Attendees will learn how Mercedes-Benz R&D North America builds secure, privacy-respecting personalized systems for the next generation of connected vehicles.

This session is repeated. Peek behind the curtain to learn how Databricks processes hundreds of petabytes of data across every region and cloud where we operate. Learn how Databricks leverages Data and AI to scale and optimize every aspect of the company. From facilities and legal to sales and marketing and of course product research and development. This session is a high-level tour inside Databricks to see how Data and AI enable us to be a better company. We will go into the architecture of things for how Databricks is used for internal use cases like business analytics and SIEM as well as customer-facing features like system tables and assistant. We will cover how data production of our data flow and how we maintain security and privacy while operating a large multi-cloud, multi-region environment.

Databricks Odyssey is JLL’s bespoke training program designed to upskill and prepare data professionals for a new world of data lakehouse. Based on the concepts of learn, practice and certify, participants earn points, moving through five levels by completing activities with business application of Databricks key features. Databricks Odyssey facilitates cloud data warehousing migration by providing best practice frameworks, ensuring efficient use of pay-per-compute platforms. JLL/T Insights and Data fosters a data culture through learning programs that develop in-house talent and create career pathways. Databricks Odyssey offers: JLL-specific hands-on learning Gamified 'level up' approach Practical, applicable skills Benefits include: Improved platform efficiency Enhanced data accuracy and client insights Ongoing professional development Potential cost savings through better utilization

As cybersecurity threats grow in volume and complexity, organizations must efficiently process security telemetry for best-in-class detection and mitigation. Barracuda’s XDR platform is redefining security operations by layering advanced detection methodologies over a broad range of supported technologies. Our vision is to deliver unparalleled protection through automation, machine learning and scalable detection frameworks, ensuring threats are identified and mitigated quickly. To achieve this, we have adopted Databricks as the foundation of our security analytics platform, providing greater control and flexibility while decoupling from traditional SIEM tools. By leveraging Lakeflow Declarative Pipelines, Spark Structured Streaming and detection-as-code CI/CD pipelines, we have built a real-time detection engine that enhances scalability, accuracy and cost efficiency. This session explores how Databricks is shaping the future of XDR through real-time analytics and cloud-native security.

Delta Lake is a fantastic technology for quickly querying massive data sets, but first you need those massive data sets! In this session we will dive into the cloud-native architecture Scribd has adopted to ingest data from AWS Aurora, SQS, Kinesis Data Firehose and more. By using off-the-shelf open source tools like kafka-delta-ingest, oxbow and Airbyte, Scribd has redefined its ingestion architecture to be more event-driven, reliable, and most importantly: cheaper. No jobs needed! Attendees will learn how to use third-party tools in concert with a Databricks and Unity Catalog environment to provide a highly efficient and available data platform. This architecture will be presented in the context of AWS but can be adapted for Azure, Google Cloud Platform or even on-premise environments.

SAP and Databricks have formed a landmark partnership that brings together SAP's deep expertise in mission-critical business processes and semantically rich data with Databricks' industry-leading capabilities in AI, machine learning, and advanced data engineering. From curated, SAP-managed data products to zero-copy Delta Sharing integration, discover how SAP Business Data Cloud empowers data and AI professionals to build AI solutions that unlock unparalleled business insights using trusted business data.

There are many challenges to making a data platform actually a platform, something that hides complexity. Data engineers and scientists are looking for a simple and intuitive abstraction to focus on their work, not where it runs to maintain compliance, what credentials it uses to access data or how it generates operational telemetry. At Databricks we’ve developed a data-centric approach to workload development and deployment that enables data workers to stop doing migrations and instead develop with confidence. Attend this session to learn how to run simple, secure and compliant global multi-cloud workloads at scale on Databricks.

Join us to explore how Standard Chartered Bank's (SCB) groundbreaking strategy is reshaping the future of the cybersecurity landscape by replacing traditional SIEM with a cutting-edge Databricks solution, achieving remarkable business outcomes: 80% Reduction in time to detect incidents 92% Faster threat investigation 35% Cost reduction 60% Better detection accuracy Significant enhancements in threat detection and response metrics Substantial increase in ML-driven use cases This session unveils SCB's journey to a distributed, multi-cloud lakehouse architecture that unlocks unprecedented performance and commercial optimization. Explore why a unified data and AI platform is becoming the cornerstone of next-generation, self-managed SIEM solutions for forward-thinking organizations in this era of AI-powered banking transformation.

Airflow 3 is here, bringing a new era of flexibility, scalability, and security to data orchestration. This release makes building, running, and managing data pipelines easier than ever. In this session, we will cover the key benefits of Airflow 3, including: (1) Ease of Use: Airflow 3 rethinks the user experience—from an intuitive, upgraded UI to DAG Versioning and scheduler-integrated backfills that let teams manage pipelines more effectively than ever before (2) Stronger Security: By decoupling task execution from direct database connections, Airflow 3 enforces task isolation and minimal-privilege access. This meets stringent compliance standards while reducing the risk of unauthorized data exposure. (3) Ultimate Flexibility: Run tasks anywhere, anytime with remote execution and event-driven scheduling. Airflow 3 is designed for global, heterogeneous modern data environments with an architecture that facilitates edge and hybrid-cloud to GPU-based deployments.

Deloitte and GM (General Motors) Financial have collaborated to design and implement a cutting-edge cloud analytics platform, leveraging Databricks. In this session, we will explore how we overcame challenges including dispersed and limited data capabilities, high-cost hardware and outdated software, with a strategic and comprehensive approach. With the help of Deloitte and Databricks, we were able to develop a unified Customer360 view, integrate advanced AI-driven analytics, and establish robust data governance and cyber security measures. Attendees will gain valuable insights into the benefits realized, such as cost savings, enhanced customer experiences, and broad employee upskilling opportunities. Unlock the impact of cloud data modernization and advanced analytics in the automotive finance industry and beyond with Deloitte and Databricks.