Transactional systems are a common source of data for analytics, and Change Data Capture (CDC) offers an efficient way to extract only what’s changed. However, ingesting CDC data into an analytics system comes with challenges, such as handling out-of-order events or maintaining global order across multiple streams. These issues often require complex, stateful stream processing logic.This session will explore how Lakeflow Declarative Pipelines simplifies CDC ingestion using the Apply Changes function. With Apply Changes, global ordering across multiple change feeds is handled automatically — there is no need to manually manage state or understand advanced streaming concepts like watermarks. It supports both snapshot-based inputs from cloud storage and continuous change feeds from systems like message buses, reducing complexity for common streaming use cases.

Growth in banking isn’t just about keeping pace—it’s about setting the pace. This session explores how leading banks leverage Databricks’ Data Intelligence Platform to uncover new revenue opportunities, deepen customer relationships, and expand market reach. Hear from industry leaders who have transformed their growth strategies by harnessing the power of advanced analytics and machine learning. Learn how personalized customer experiences, predictive insights and unified data platforms are driving innovation and helping banks scale faster than ever. Key takeaways: Proven strategies for identifying untapped growth opportunities using data-driven approaches Real-world examples of banks creating personalized customer journeys that boost retention and loyalty Tools and techniques to accelerate innovation while maintaining operational efficiency Join us in discovering how data intelligence is redefining growth in banking and thriving throughout uncertainty.

There’s never been a more critical time to ensure data and analytics foundations can deliver the value and efficiency needed to accelerate and scale AI. What are the most difficult challenges that organizations face with data transformation, and what technologies, processes and decisions that overcome these barriers to success? Join this session featuring executives from the Gates Foundation, the nonprofit leading change in communities around the globe, and Avanade, the joint venture between Accenture and Microsoft, in a discussion about impactful data strategy. Learn about the Gates Foundation’s approach to its enterprise data platform to ensure trusted insights at the speed of today’s business. And we’ll share lessons learned from Avanade helping organizations around the globe build with Databricks and seize the AI opportunity.

As enterprises strive to become more data-driven, SAP continues to be central to their operational backbone. However, traditional SAP ecosystems often limit the potential of AI and advanced analytics due to fragmented architectures and legacy tools. In this session, we explore four strategic options for unlocking greater value from SAP data by integrating with Databricks and cloud-native platforms. Whether you're on ECC, S4HANA, or transitioning from BW, learn how to modernize your data landscape, enable real-time insights, and power AI/ML at scale. Discover how SAP Business Data Cloud and SAP Databricks can help you build a unified, future-ready data and analytics ecosystem—without compromising on scalability, flexibility, or cost-efficiency.

The explosion of AI has helped make the enterprise data landscape more important, and complex, than ever before. Join us to learn how Databricks’ and Tableau’s platforms come together to empower users of all kinds to see, understand, and act on your data in a secure, governed, and performant way.

In today’s fast-evolving crypto landscape, organizations require fast, reliable intelligence to manage risk, investigate financial crime, and stay ahead of evolving threats. In this session we will discover how Elliptic built a scalable, high-performance Data Intelligence Platform that delivers real-time, actionable Blockchain insights to their customers. We’ll walk you through some of the key components of the Elliptic Platform, including the Elliptic Entity Graph and our User-Facing Analytics. Our focus will be put on the evolution of our User-Facing Analytics capabilities, and specifically how components from the Databricks ecosystem such as Structured Streaming, Delta Lake, and SQL Warehouse have played a vital role. We’ll also share some of the optimizations we’ve made to our streaming jobs to maximize performance and ensure Data Completeness. Whether you’re looking to enhance your streaming capabilities, expand your knowledge of how crypto analytics works or simply discover novel approaches to data processing at scale, this session will provide concrete strategies and valuable lessons learned.

Moving data between operational systems and analytics platforms is often painful. Traditional pipelines become complex, brittle, and expensive to maintain.Take Kafka and Iceberg: batching on Kafka causes ingestion bottlenecks, while streaming-style writes to Iceberg create too many small Parquet files—cluttering metadata, degrading queries, and increasing maintenance overhead. Frequent updates further strain background table operations, causing retries—even before dealing with schema evolution. But much of this complexity is avoidable. What if Kafka Topics and Iceberg Tables were treated as two sides of the same coin? By establishing a transparent equivalence, we can rethink pipeline design entirely. This session introduces Tableflow—a new approach to bridging streaming and table-based systems. It shifts complexity away from pipelines and into a unified layer, enabling simpler, declarative workflows. We’ll cover schema evolution, compaction, topic-to-table mapping, and how to continuously materialize and optimize thousands of topics as Iceberg tables. Whether modernizing or starting fresh, you’ll leave with practical insights for building resilient, scalable, and future-proof data architectures.

This session explores the strategic migration from Snowflake to Databricks, focusing on the journey of transforming a data lake to leverage Databricks’ advanced capabilities. It outlines the assessment of key architectural differences, performance benchmarks, and cost implications driving the decision. Attendees will gain insights into planning and execution, including data ingestion pipelines, schema conversion and metadata migration. Challenges such as maintaining data quality, optimizing compute resources and minimizing downtime are discussed, alongside solutions implemented to ensure a seamless transition. The session highlights the benefits of unified analytics and enhanced scalability achieved through Databricks, delivering actionable takeaways for similar migrations.

In capital markets, mitigating risk is critical to protecting the firm’s reputation, assets, and clients. This session highlights how firms use technology to enhance risk management, ensure compliance and safeguard operations from emerging threats. Learn how advanced analytics and machine learning models are helping firms detect anomalies, prevent fraud, and manage regulatory complexities with greater precision. Hear from industry leaders who have successfully implemented proactive risk strategies that balance security with operational efficiency. Key Takeaways: Techniques for identifying risks early using AI-powered anomaly detection. Best practices for achieving compliance across complex regulatory environments. Insights into building resilient operations that protect assets without compromising growth potential. Don’t miss this session to discover how data intelligence is transforming risk management in capital markets—helping firms secure their future while driving success!"

Are you striving to build a data-driven culture while managing costs and reducing reporting latency? Are your BI operations bogged down by complex data movements rather than delivering insights? Databricks IT faced these challenges in 2024 and embarked on an ambitious journey to make Databricks AI/BI our enterprise-wide reporting platform. In just two quarters, we migrated 2,000 dashboards from a traditional BI tool — without disrupting business operations. We’ll share how we executed this large-scale transition cost-effectively, ensuring seamless change management and empowering non-technical users to leverage AI/BI. You’ll gain insights into: Key migration strategies that minimized disruption and optimized efficiency Best practices for user adoption and training to drive self-service analytics Measuring success with clear adoption metrics and business impact Join us to learn how your organization can achieve the same transformation with AI-powered enterprise reporting.

The GTM team at Databricks recently launched the GTM Analytics Hub—a native AI/BI platform designed to centralize reporting, streamline insights, and deliver personalized dashboards based on user roles and business needs. Databricks Apps also played a crucial role in this integration by embedding AI/BI Dashboards directly into internal tools and applications, streamlining access to insights without disrupting workflows. This seamless embedding capability allows users to interact with dashboards within their existing platforms, enhancing productivity and collaboration. Furthermore, AI/BI Dashboards leverage Databricks' unified data and governance framework. Join us to learn how we’re using Databricks to build for Databricks—transforming GTM analytics with AI/BI Dashboards, and what it takes to drive scalable, user-centric analytics adoption across the business.

Tired of data silos and the constant need to move copies of your data across different systems? Imagine a world where all your enterprise data is readily available in Databricks without the cost and complexity of duplication and ingestion. Our vision is to break down these silos by enabling seamless, zero-copy data sharing across platforms, clouds, and regions. This unlocks the true potential of your data for analytics and AI, empowering you to make faster, more informed decisions leveraging your most important enterprise data sets. This session you will hear from Databricks, SAP, and Salesforce product leaders on how zero-copy data sharing can unlock the value of enterprise data. Explore how Delta Sharing makes this vision a reality, providing secure, zero-copy data access for enterprises.SAP Business Data Cloud: See Delta Sharing in action to unlock operational reporting, supply chain optimization, and financial planning. Salesforce Data Cloud: Enable customer analytics, churn prediction, and personalized marketing.

Are data teams ready for AI? Prophecy’s exclusive survey, “The Impact of GenAI on Data Teams”, gives the clearest picture yet of GenAI’s potential in data management, and what’s standing in the way. The top two obstacles? Poor governance and slow access to high-quality data. The message is clear: Modernizing your data platform with Databricks is essential. But it’s only the beginning. To unlock the power of AI and analytics, organizations must deliver governed, self-service access to clean, trusted data. Traditional data prep tools introduce risks around security, quality, and cost. It’s no wonder data leaders cited data transformation as the area where GenAI will make the biggest impact. To deliver what’s needed teams need a shift to governed self-service. Data analysts and scientists move fast while staying within IT’s guardrails. Join us to learn more details from the survey and how leading organizations are ahead of the curve, using GenAI to reshape how data gets done.

Join us to see how the powerful combination of ThoughtSpot's agentic analytics platform and the Databricks Data Intelligence Platform is changing the game for data-driven organizations. We'll demonstrate how DataSpot breaks down technical barriers to insight. You'll learn how to get trusted, real-time answers thanks to the seamless integration between ThoughtSpot's semantic layer and Databricks Unity Catalog. This session is for anyone looking to leverage data more effectively, whether you're a business leader seeking AI-driven insights, a data scientist building models in Python, or a product owner creating intelligent applications.

This session will explore how Adobe uses a sophisticated data security architecture built on the Databricks Data Intelligence Platform, along with the Open Cybersecurity Schema Framework (OCSF), to enable scalable, real-time threat detection across more than 10 PB of security data. We’ll compare different approaches to OCSF implementation and demonstrate how Adobe processes massive security datasets efficiently — reducing query times by 18%, maintaining 99.4% SLA compliance, and supporting 286 security users across 17 teams with over 4,500 daily queries. By using Databricks' Platform for serverless compute, scalable architecture, and LLM-powered recommendations, Adobe has significantly improved processing speed and efficiency, resulting in substantial cost savings. We’ll also highlight how OCSF enables advanced cross-tool analytics and automation, streamlining investigations. Finally, we’ll introduce Databricks’ new open-source OCSF toolkit for scalable security data normalization and invite the community to contribute.

Discover how to build and deploy AI-powered applications natively on the Databricks Data Intelligence Platform. This session introduces best practices and a standard reference architecture for developing production-ready apps using popular frameworks like Dash, Shiny, Gradio, Streamlit and Flask. Learn how to leverage agents for orchestration and explore primary use cases supported by Databricks Apps, including data visualization, AI applications, self-service analytics and data quality monitoring. With serverless deployment and built-in governance through Unity Catalog, Databricks Apps enables seamless integration with your data and AI models, allowing you to focus on delivering impactful solutions without the complexities of infrastructure management. Whether you're a data engineer or an app developer, this session will equip you with the knowledge to create secure, scalable and efficient applications within a Databricks environment.

This hands-on lab guides participants through the complete customer data analytics journey on Databricks, leveraging leading partner solutions - Fivetran, dbt Cloud, and Sigma. Attendees will learn how to:- Seamlessly connect to Fivetran, dbt Cloud, and Sigma using Databricks Partner Connect- Ingest data using Fivetran, transform and model data with dbt Cloud, and create interactive dashboards in Sigma, all on top of the Databricks Data Intelligence Platform- Empower teams to make faster, data-driven decisions by streamlining the entire analytics workflow using an integrated, scalable, and user-friendly platform

Most organizations run complex cloud data architectures that silo applications, users and data. Join this interactive hands-on workshop to learn how Databricks SQL allows you to operate a multi-cloud lakehouse architecture that delivers data warehouse performance at data lake economics — with up to 12x better price/performance than traditional cloud data warehouses.Here’s what we’ll cover: How Databricks SQL fits in the Data Intelligence Platform, enabling you to operate a multicloud lakehouse architecture that delivers data warehouse performance at data lake economics How to manage and monitor compute resources, data access and users across your lakehouse infrastructure How to query directly on your data lake using your tools of choice or the built-in SQL editor and visualizations How to use AI to increase productivity when querying, completing code or building dashboards Ask your questions during this hands-on lab, and the Databricks experts will guide you.

Data is the backbone of modern decision-making, but centralizing it is only the tip of the iceberg. Entitlements, secure sharing and just-in-time availability are critical challenges to any large-scale platform. Join Goldman Sachs as we reveal how our Legend Lakehouse, coupled with Databricks, overcomes these hurdles to deliver high-quality, governed data at scale. By leveraging an open table format (Apache Iceberg) and open catalog format (Unity Catalog), we ensure platform interoperability and vendor neutrality. Databricks Unity Catalog then provides a robust entitlement system that aligns with our data contracts, ensuring consistent access control across producer and consumer workspaces. Finally, Legend functions, integrating with Databricks User Defined Functions (UDF), offer real-time data enrichment and secure transformations without exposing raw datasets. Discover how these components unite to streamline analytics, bolster governance and power innovation.

Over the past three years, Netflix has built a catalog of 100+ mobile and cloud games across TV, mobile and web platforms. With both internal and external studios contributing to this diverse ecosystem, building a robust game analytics platform became crucial for gaining insights into player behavior, optimizing game performance and driving member engagement.In this talk, we’ll share our journey of building Netflix’s Game Analytics platform from the ground up. We’ll highlight key decisions around data strategy, such as whether to develop an in-house solution or adopt an external service. We’ll discuss the challenges of balancing developer autonomy with data integrity and the complexities of managing data contracts for custom game telemetry, with an emphasis on self-service analytics. Attendees will learn how the Games Data team navigated these challenges, the lessons learned and the trade-offs involved in building a multi-tenant data ecosystem that supports diverse stakeholders.