LLM agents aren’t just answering questions — they’re running entire workflows. In this talk, we’ll show how agents can autonomously ingest, process and structure unstructured data using Unstructured, with outputs flowing directly into Databricks. Powered by the Model Context Protocol (MCP), agents can interface with Unstructured’s full suite of capabilities — discovering documents across sources, building ephemeral workflows and exporting structured insights into Delta tables. We’ll walk through a demo where an agent responds to a natural language request, dynamically pulls relevant documents, transforms them into usable data and surfaces insights — fast. Join us for a sneak peek into the future of AI-native data workflows, where LLMs don’t just assist — they operate.

As data ecosystems grow increasingly complex, the ability to share data securely, seamlessly, and in real time has become a strategic differentiator. In this session, Cigna will showcase how Delta Sharing on Databricks has enabled them to modernize data delivery, reduce operational overhead, and unlock new market opportunities. Learn how Cigna achieved significant savings by streamlining operations, compute, and platform overhead for just one use case. Explore how decentralizing data ownership—transitioning from hyper-centralized teams to empowered product owners—has simplified delivery and accelerated innovation. Most importantly, see how this modern open data-sharing framework has positioned Cigna to win contracts they previously couldn’t, by enabling real-time, cross-organizational data collaboration with external partners. Join us to hear how Cigna is using Delta Sharing not just as a technical enabler, but as a business catalyst.

In today's data-driven world, the ability to efficiently manage and transform data is crucial for any organization. This presentation will explore the process of converting a complex and messy workflow into a clean and simple Lakeflow Declarative Pipelines at a large integrated health system, Intermountain Health.Alteryx is a powerful tool for data preparation and blending, but as workflows grow in complexity, they can become difficult to manage and maintain. Lakeflow Declarative Pipelines, on the other hand, offers a more democratized, streamlined and scalable approach to data engineering, leveraging the power of Apache Spark and Delta Lake.We will begin by examining a typical legacy workflow, identifying common pain points such as tangled logic, performance bottlenecks and maintenance challenges. Next, we will demonstrate how to translate this workflow into a Lakeflow Declarative Pipelines, highlighting key steps such as data transformation, validation and delivery.

We’ll explore how CipherOwl Inc. constructed a near real-time, multi-chain data lakehouse to power anti-money laundering (AML) monitoring at a petabyte scale. We will walk through the end-to-end architecture, which integrates cutting-edge open-source technologies and AI-driven analytics to handle massive on-chain data volumes seamlessly. Off-chain intelligence complements this to meet rigorous AML requirements. At the core of our solution is ChainStorage, an OSS started by Coinbase that provides robust blockchain data ingestion and block-level serving. We enhanced it with Apache Spark™ and Arrow™, coupled for high-throughput processing and efficient data serialization, backed by Delta Lake and Kafka. For the serving layer, we employ StarRocks to deliver lightning-fast SQL analytics over vast datasets. Finally, our system incorporates machine learning and AI agents for continuous data curation and near real-time insights, which are crucial for tackling on-chain AML challenges.

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.

Table formats like Delta Lake and Iceberg have been game changers for pushing lakehouse architecture into modern Enterprises. The acquisition of Tabular added Iceberg to the Databricks ecosystem, an open format that was already well supported by processing engines across the industry. At HelloFresh we are building a lakehouse architecture that integrates many touchpoints and technologies all across the organization. As such we chose Iceberg as the table format to bridge the gaps in our decentralized managed tech landscape. We are leveraging Unity Catalog as the Iceberg REST catalog of choice for storing metadata and managing tables. In this talk we will outline our architectural setup between Databricks, Spark, Flink and Snowflake and will explain the native Unity Iceberg REST catalog, as well as catalog federation towards connected engines. We will highlight the impact on our business and discuss the advantages and lessons learned from our early adopter experience.

Learn how Danone, a global leader in the food industry, improved its data-sharing processes using Delta Sharing, an open protocol developed by Databricks. This session will explore how Danone migrated from a traditional hub-and-spoke model to a more efficient and scalable data-sharing approach that works seamlessly across regions and platforms. We’ll discuss practical concepts such as in-region and cross-region data sharing, fine-grained access control, data discovery, and the implementation of data contracts. You’ll also hear about the strategies Danone uses to deliver governed data efficiently while maintaining compliance with global regulations. Additionally, we’ll discuss a cost comparison between direct data access and replication. Finally, we’ll share insights into the challenges faced by global organizations in managing data sharing at scale and how Danone addressed these issues. Attendees will gain practical knowledge on building a reliable and secure data-sharing framework for international collaboration.

Industrial organizations are unlocking new possibilities through the partnership between AVEVA and Databricks. The seamless, no-code, zero-copy solution—powered by Delta Sharing and CONNECT—enables companies to combine IT and OT data effortlessly. By bridging the gap between operational and enterprise data, businesses can harness the power of AI, data science, and business intelligence at an unprecedented scale to drive innovation. In this session, explore real-world applications of this integration, including how industry leaders are using CONNECT and Databricks to boost efficiency, reduce costs, and advance sustainability—all without fragmented point solutions. You’ll also see a live demo of the integration, showcasing how secure, scalable access to trusted industrial data is enabling new levels of industrial intelligence across sectors like mining, manufacturing, power, and oil and gas.

Delta Lake has proven to be an excellent storage format. Coupled with the Databricks platform, the storage format has shined as a component of a distributed system on the lakehouse. The pairing of Delta and Spark provides an excellent platform, but users often struggle to perform comparable work outside of the Spark ecosystem. Tools such as delta-rs, Polars and DuckDb have brought access to users outside of Spark, but they are only building blocks of a larger system. In this 40-minute talk we will demonstrate how users can use data products on the Nextdata OS data mesh to interact with the Databricks platform to drive Delta Lake workflows. Additionally, we will show how users can build autonomous data products that interact with their Delta tables both inside and outside of the lakehouse platform. Attendees will learn how to integrate the Nextdata OS data mesh with the Databricks platform as both an external and integral component.

Media enterprises generate vast amounts of visual content, but unlocking its full potential requires multimodal AI at scale. Coactive AI and NBCUniversal’s Corporate Decision Sciences team are transforming how enterprises discover and understand visual content. We explore how Coactive AI and Databricks — from Delta Share to Genie — can revolutionize media content search, tagging and enrichment, enabling new levels of collaboration. Attendees will see how this AI-powered approach fuels AI workflows, enhances BI insights and drives new applications — from automating cut sheet generation to improving content compliance and recommendations. By structuring and sharing enriched media metadata, Coactive AI and NBCU are unlocking deeper intelligence and laying the groundwork for agentic AI systems that retrieve, interpret and act on visual content. This session will showcase real-world examples of these AI agents and how they can reshape future content discovery and media workflows.

This session will focus on the security aspects of Databricks Delta Sharing, Databricks Cleanrooms and Databricks Marketplace, providing an exploration of how these solutions enable secure and scalable data collaboration while prioritizing privacy. Highlights: Use cases — Understand how Delta Sharing facilitates governed, real-time data exchange across platforms and how Cleanrooms support multi-party analytics without exposing sensitive information Security internals — Dive into Delta Sharing's security frameworks Dynamic views — Learn about fine-grained security controls Privacy-first Cleanrooms — Explore how Cleanrooms enable secure analytics while maintaining strict data privacy standards Private exchanges — Explore the role of private exchanges using Databricks Marketplace in securely sharing custom datasets and AI models with specific partners or subsidiaries Network security & compliance — Review best practices for network configurations and compliance measures

GoTo Foods, the platform company behind brands like Auntie Anne’s, Cinnabon, Jamba, and more, set out to turn a fragmented data landscape into a high-performance customer intelligence engine. In this session, CTO Manuel Valdes and Director of Marketing Technology Brett Newcome share how they unified data using Databricks Delta Sharing and Amperity’s Customer Data Cloud to speed up time to market. As part of GoTo’s broader strategy to support its brands with shared enterprise tools, the team: Unified loyalty, catering, and retail data into one customer view Cut campaign lead times from weeks to hours Activated audiences in real time without straining engineering Unlocked new revenue through smarter segmentation and personalization

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.

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

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.

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.

In today’s rapidly evolving healthcare landscape, the ability to securely and efficiently share data is critical to driving better patient outcomes, operational efficiencies, and groundbreaking research. In this session, Komodo Health will explore how Delta sharing unlocks new opportunities across the life sciences ecosystem, with de-identified longitudinal patient data without compromising patient privacy. We will share insights into customers' experiences leveraging de-identified patient data to reduce the burden of disease while improving the overall patient experience. Attendees will learn practical approaches to compliantly share data in life sciences.

At Kaizen Gaming, data drives our decision-making, but rapid growth exposed inefficiencies in our legacy cloud setup — escalating costs, delayed insights and scalability limits. Operating in 18 countries with 350M daily transactions (1PB+), shared quotas and limited cost transparency hindered efficiency. To address this, we redesigned our cloud architecture with Data Landing Zones, a modular framework that decouples resources, enabling independent scaling and cost accountability. Automation streamlined infrastructure, reduced overhead and enhanced FinOps visibility, while Unity Catalog ensured governance and security. Migration challenges included maintaining stability, managing costs and minimizing latency. A phased approach, Delta Sharing, and DBx Asset Bundles simplified transitions. The result: faster insights, improved cost control and reduced onboarding time, fostering innovation and efficiency. We share our transformation, offering insights for modern cloud optimization.

Marketing teams struggle with ‘dirty data’ — incomplete, inconsistent, and inaccurate information that limits campaign effectiveness and reduces the accuracy of AI agents. Our AI-powered marketing data management platform, built on Databricks, solves this with anomaly detection, ML-driven transformations and the built-in Acxiom Referential Real ID Graph with Data Hygiene.We’ll showcase how Delta Lake, Unity Catalog and Lakeflow Declarative Pipelines power our multi-tenant architecture, enabling secure governance and 75% faster data processing. Our privacy-first design ensures compliance with GDPR, CCPA and HIPAA through role-based access, encryption key management and fine-grained data controls.Join us for a live demo and Q&A, where we’ll share real-world results and lessons learned in building a scalable, AI-driven marketing data solution with Databricks.