Join our 2024 Databricks Disruptor award winners for a session on how they leveraged the Databricks and AWS platforms to build an internal technology marketplace in the highly regulated banking industry empowering end-users to innovate and own their data sets while maintaining strict compliance. In this talk, leaders from the J.P. Morgan Payments Data team share how they’ve done it — from keeping customer needs at the center of all decision-making to promoting a culture of experimentation. They’ll also expand upon how J.P. Morgan Payments products team now leverages the data platform they’ve built to create customer products including Cash Flow Intelligence.

This session is repeated. You’ve seen your usage grow on Databricks, across departments, use cases, product lines and users. What can you do to ensure your end-users (data practitioners) of the platform remain cost-efficient and productive, while staying accountable to your budget? We’ll discuss spend monitoring, chargeback models and developing a culture of cost efficiency by using Databricks tools.

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.

This session is repeated. Power BI has long been the dominant BI tool in the market. In this session, we'll discuss how to get the most out of PowerBI and Databricks, beginning with high-level architecture and moving down into detailed how-to guides for troubleshooting common failure points. At the end, you'll receive a cheat-sheet which summarizes those best practices into an easy-to-reference format.

At Nikon, camera accessories are essential in meeting the diverse needs of professional photographers worldwide, making their timely availability a priority. Forecasting accessories, however, presents unique challenges including dependencies on parent products, sparse demand patterns, and managing predictions for thousands of items across global subsidiaries. To address this, we leveraged Databricks' unified data and AI platform to develop and deploy an automated, scalable solution for accessory sales planning. Our solution employs a hybrid approach that auto-selects best algorithm from a suite of ML and time-series models, incorporating anomaly detection and methods to handle sparse and low-demand scenarios. MLflow is utilized to automate model logging and versioning, enabling efficient management, and scalable deployment. The framework includes data preparation, model selection and training, performance tracking, prediction generation, and output processing for downstream systems.

Lely, a Dutch company specializing in dairy farming robotics, helps farmers with advanced solutions for milking, feeding and cleaning. This session explores Lely’s implementation of an Internal Data Marketplace, built around Databricks' Private Exchange Marketplace. The marketplace serves as a central hub for data teams and business users, offering seamless access to data, analytics and dashboards. Powered by Delta Sharing, it enables secure, private listing of data products across business domains, including notebooks, views, models and functions. This session covers the pros and cons of this approach, best practices for setting up a data marketplace and its impact on Lely’s operations. Real-world examples and insights will showcase the potential of integrating data-driven solutions into dairy farming. Join us to discover how data innovation drives the future of dairy farming through Lely’s experience.

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 this session, we will share how we are transforming the way organizations process unstructured and non-standard documents using Mosaic AI and agentic patterns within the Databricks ecosystem. We have developed a scalable pipeline that turns complex legal and regulatory content into structured, tabular data.We will walk through the full architecture, which includes Unity Catalog for secure and governed data access, Databricks Vector Search for intelligent indexing and retrieval and Databricks Apps to deliver clear insights to business users. The solution supports multiple languages and formats, making it suitable for teams working across different regions. We will also discuss some of the key technical challenges we addressed, including handling parsing inconsistencies, grounding model responses and ensuring traceability across the entire process. If you are exploring how to apply GenAI and large language models, this session is for you. Audio for this session is delivered in the conference mobile app, you must bring your own headphones to listen.

In the rapidly evolving life sciences and healthcare industry, leveraging data-as-a-product is crucial for driving innovation and achieving business objectives. Join us to explore how Deloitte is revolutionizing data strategy solutions by overcoming challenges such as data silos, poor data quality, and lack of real-time insights with the Databricks Data Intelligence Platform. Learn how effective data governance, seamless data integration, and scalable architectures support personalized medicine, regulatory compliance, and operational efficiency. This session will highlight how these strategies enable biopharma companies to transform data into actionable insights, accelerate breakthroughs and enhance life sciences outcomes.

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.

As global energy demands continue to rise, organizations must boost efficiency while staying environmentally responsible. Flogistix uses Sigma and Databricks to build a unified data architecture for real-time, data-driven decisions in vapor recovery systems. With Sigma on the Databricks Data Intelligence Platform, Flogistix gains precise operational insights and identifies optimization opportunities that reduce emissions, streamline workflows, and meet industry regulations. This empowers everyone, from executives to field mechanics, to drive sustainable resource production. Discover how advanced analytics are transforming energy practices for a more responsible future.

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.

We present AT&T AutoClassify, built jointly between AT&T's Chief Data Office (CDO) and Databricks professional services, a novel end-to-end system for automatic multi-head binary classifications from unlabeled text data. Our approach automates the challenge of creating labeled datasets and training multi-head binary classifiers with minimal human intervention. Starting only from a corpus of unlabeled text and a list of desired labels, AT&T AutoClassify leverages advanced natural language processing techniques to automatically mine relevant examples from raw text, fine-tune embedding models and train individual classifier heads for multiple true/false labels. This solution can reduce LLM classification costs by 1,000x, making it an efficient solution in operational costs. The end result is a highly optimized and low-cost model servable in Databricks capable of taking raw text and producing multiple binary classifications. An example use case using call transcripts will be examined.

Bayada is transforming its data ecosystem by consolidating Matillion+Snowflake and SSIS+SQL Server into a unified Enterprise Data Platform powered by Databricks. Using Databricks' Medallion architecture, this platform enables seamless data integration, advanced analytics and machine learning across critical domains like general ledger, recruitment and activity-based costing. Databricks was selected for its scalability, real-time analytics and ability to handle both structured and unstructured data, positioning Bayada for future growth. The migration aims to reduce data processing times by 35%, improve reporting accuracy and cut reconciliation efforts by 40%. Operational costs are projected to decrease by 20%, while real-time analytics is expected to boost efficiency by 15%. Join this session to learn how Bayada is leveraging Databricks to build a high-performance data platform that accelerates insights, drives efficiency and fosters innovation organization-wide.

This session is repeated. Mosaic AI Vector Search is powering high-accuracy retrieval systems in production across a wide range of use cases — including RAG applications, entity resolution, recommendation systems and search. Fully integrated with the Databricks Data Intelligence Platform, it eliminates pipeline maintenance by automatically syncing data from source to index. Over the past year, customers have asked for greater scale, better quality out-of-the-box and cost-efficient performance. This session delivers on those needs — showcasing best practices for implementing high-quality retrieval systems and revealing major product advancements that improve scalability, efficiency and relevance. What you’ll learn: How to optimize Vector Search with hybrid retrieval and reranking for better out-of-the-box results Best practices for managing vector indexes with minimal operational overhead Real-world examples of how organizations have scaled and improved their search and recommendation systems

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.

Ingesting data from SaaS systems sounds straightforward—until you hit API limits, miss SLAs, or accidentally ingest PII. Sound familiar? In this talk, we’ll share how Databricks evolved from scrappy ingestion scripts to a unified, secure, and scalable ingestion platform. Along the way, we’ll highlight the hard lessons, the surprising pitfalls, and the tools that helped us level up. Whether you’re just starting to wrangle third-party data or looking to scale while handling governance and compliance, this session will help you think beyond pipelines and toward platform thinking.

At Haleon, we've leveraged Databricks APIs and serverless compute to develop customer-facing applications for our business. This innovative solution enables us to efficiently deliver SAP invoice and order management data through front-end applications developed and served via our API Gateway. The Databricks lakehouse architecture has been instrumental in eliminating the friction associated with directly accessing SAP data from operational systems, while enhancing our performance capabilities. Our system acheived response times of less than 3 seconds from API call, with ongoing efforts to optimise this performance. This architecture not only streamlines our data and application ecosystem but also paves the way for integrating GenAI capabilities with robust governance measures for our future infrastructure. The implementation of this solution has yielded significant benefits, including a 15% reduction in customer service costs and a 28% increase in productivity for our customer support team.