In PacificSource Health Plans, a health insurance company in the US, we are on a successful multi-year journey to migrate all of our data and analytics ecosystem to Databricks Enterprise Data Warehouse (lakehouse). A particular obstacle on this journey was a reporting data mart which relied on copious amounts of legacy SAS code that applied sophisticated business logic transformations for membership, claims, premiums and reserves. This core data mart was driving many of our critical reports and analytics. In this session we will share the unique and somewhat unexpected challenges and complexities we encountered in migrating this legacy SAS code. How our partner (T1A) leveraged automation technology (Alchemist) and some unique approaches to reverse engineer (analyze), instrument, translate, migrate, validate and reconcile these jobs; and what lessons we learned and carried from this migration effort.

The last year has seen the rapid progress of Open Source GenAI models and frameworks. This talk covers best practices for custom training and OSS GenAI finetuning on Databricks, powered by the newly announced Serverless GPU Compute. We’ll cover how to use Serverless GPU compute to power AI training/GenAI finetuning workloads and framework support for libraries like LLM Foundry, Composer, HuggingFace, and more. Lastly, we’ll cover how to leverage MLFlow and the Databricks Lakehouse to streamline the end to end development of these models. Key takeaways include: How Serverless GPU compute saves customers valuable developer time and overhead when dealing with GPU infrastructure Best practices for training custom deep learning models (forecasting, recommendation, personalization) and finetuning OSS GenAI Models on GPUs across the Databricks stack Leveraging distributed GPU training frameworks (e.g. Pytorch, Huggingface) on Databricks Streamlining the path to production for these models Join us to learn about the newly announced Serverless GPU Compute and the latest updates to GPU training and finetuning on Databricks!

Toyota, the world’s largest automaker, sought to accelerate time-to-data and empower business users with secure data collaboration for faster insights. Partnering with Cognizant, they established a Unified Data Lake, integrating SOX principles, Databricks Unity Catalog to ensure compliance and security. Additionally, they developed a Data Scanner solution to automatically detect non-sensitive data and accelerate data ingestion. Join this dynamic session to discover how they achieved it.

In today's data landscape, organizations often grapple with fragmented data spread across various databases, data warehouses and catalogs. Lakehouse Federation addresses this challenge by enabling seamless discovery, querying, and governance of distributed data without the need for duplication or migration. This session will explore how Lakehouse Federation integrates external data sources like Hive Metastore, Snowflake, SQL Server and more into a unified interface, providing consistent access controls, lineage tracking and auditing across your entire data estate. Learn how to streamline analytics and AI workloads, enhance compliance and reduce operational complexity by leveraging a single, cohesive platform for all your data needs.

The most common RAG systems rely on a frozen RAG system — one where there’s a single embedding model and single vector index. We’ve achieved a modicum of success with that, but when it comes to increasing accuracy for production systems there is only so much this approach solves. In this session we will explore how to move from the frozen systems to adaptive RAG systems which produce more tailored outputs with higher accuracy. Databricks services: Lakehouse, Unity Catalog, Mosaic, Sweeps, Vector Search, Agent Evaluation, Managed Evaluation, Inference Tables

NHS England is revolutionizing healthcare research by enabling secure, seamless access to de-identified patient data through the Federated Data Platform (FDP). Despite vast data resources spread across regional and national systems, analysts struggle with fragmented, inconsistent datasets. Enter Databricks: powering a unified, virtual data lake with Unity Catalog at its core — integrating diverse NHS systems while ensuring compliance and security. By bridging AWS and Azure environments with a private exchange and leveraging the Iceberg connector to interface with Palantir, analysts gain scalable, reliable and governed access to vital healthcare data. This talk explores how this innovative architecture is driving actionable insights, accelerating research and ultimately improving patient outcomes.

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.

Auto Loader is the definitive tool for ingesting data from cloud storage into your lakehouse. In this session, we’ll unveil new features and best practices that simplify every aspect of cloud storage ingestion. We’ll demo out-of-the-box observability for pipeline health and data quality, walk through improvements for schema management, introduce a series of new data formats and unveil recent strides in Auto Loader performance. Along the way, we’ll provide examples and best practices for optimizing cost and performance. Finally, we’ll introduce a preview of what’s coming next — including a REST API for pushing files directly to Delta, a UI for creating cloud storage pipelines and more. Join us to help shape the future of file ingestion on Databricks.

This course is designed to introduce three primary machine learning deployment strategies and illustrate the implementation of each strategy on Databricks. Following an exploration of the fundamentals of model deployment, the course delves into batch inference, offering hands-on demonstrations and labs for utilizing a model in batch inference scenarios, along with considerations for performance optimization. The second part of the course comprehensively covers pipeline deployment, while the final segment focuses on real-time deployment. Participants will engage in hands-on demonstrations and labs, deploying models with Model Serving and utilizing the serving endpoint for real-time inference. By mastering deployment strategies for a variety of use cases, learners will gain the practical skills needed to move machine learning models from experimentation to production. This course shows you how to operationalize AI solutions efficiently, whether it's automating decisions in real-time or integrating intelligent insights into data pipelines. Pre-requisites: Familiarity with Databricks workspace and notebooks, familiarity with Delta Lake and Lakehouse, intermediate level knowledge of Python (e.g. common Python libraries for DS/ML like Scikit-Learn, awareness of model deployment strategies) Labs: Yes Certification Path: Databricks Certified Machine Learning Associate

Organizations face the challenge of managing vast amounts of data to combat emerging threats. The Databricks Data Intelligence platform represents a paradigm shift in cybersecurity at State Street, providing a comprehensive solution for managing and analyzing diverse security data. Through its partnership with Databricks, State Street has created a capability to: Efficiently manage structured and unstructured data. Scale up to analyze 50 petabytes of data in real-time. Ingest and parse data for critical security data streams. Build advanced cybersecurity data products and use automation & orchestration to streamline cybersecurity operations. By leveraging these capabilities, State Street has positioned itself as a leader in the financial services industry when it comes to cybersecurity.

This presentation outlines Rabobank Credit analytics transition to a secure, audit-ready data architecture using Unity Catalog (UC), addressing critical regulatory challenges in credit analytics for IRB and IFRS9 regulatory modeling. Key technical challenges included legacy infrastructure (Hive metastore, ADLS mounts using Service Principals and Credential passthrough) lacking granular access controls, data access auditing and limited visibility into lineage, creating governance and compliance gaps. Details cover a framework for phased migration to UC. Outcomes include data lineage mapping demonstrating compliance with regulatory requirements, granular role based access control and unified audit trails. Next steps involve a lineage visualization toolkit (custom app for impact analysis and reporting) and lineage expansion to incorporate upstream banking systems.

As data architectures evolve to meet the demands of real-time GenAI applications, organizations increasingly need systems that unify streaming and batch processing while maintaining compatibility with existing tools. The Ursa Engine offers a Kafka-API-compatible data streaming engine built on Lakehouse (Iceberg and Delta Lake). Designed to seamlessly integrate with data lakehouse architectures, Ursa extends your lakehouse capabilities by enabling streaming ingestion, transformation and processing — using a Kafka-compatible interface. In this session, we will explore how Ursa Engine augments your existing lakehouses with Kafka-compatible capabilities. Attendees will gain insights into Ursa Engine architecture and real-world use cases of Ursa Engine. Whether you're modernizing legacy systems or building cutting-edge AI-driven applications, discover how Ursa can help you unlock the full potential of your data.

The course is designed to cover advanced concepts and workflows in machine learning operations. It starts by introducing participants to continuous integration (CI) and continuous development (CD) workflows within machine learning projects, guiding them through the deployment of a sample CI/CD workflow using Databricks in the first section. Moving on to the second part, participants delve into data and model testing, where they actively create tests and automate CI/CD workflows. Finally, the course concludes with an exploration of model monitoring concepts, demonstrating the use of Lakehouse Monitoring to oversee machine learning models in production settings. Pre-requisites: Familiarity with Databricks workspace and notebooks; knowledge of machine learning model development and deployment with MLflow (e.g. intermediate-level knowledge of traditional ML concepts, development with CI/CD, the use of Python and Git for ML projects with popular platforms like GitHub) Labs: Yes Certification Path: Databricks Certified Machine Learning Professional

In this course, you’ll learn how to develop traditional machine learning models on Databricks. We’ll cover topics like using popular ML libraries, executing common tasks efficiently with AutoML and MLflow, harnessing Databricks' capabilities to track model training, leveraging feature stores for model development, and implementing hyperparameter tuning. Additionally, the course covers AutoML for rapid and low-code model training, ensuring that participants gain practical, real-world skills for streamlined and effective machine learning model development in the Databricks environment. Pre-requisites: Familiarity with Databricks workspace and notebooks, familiarity with Delta Lake and Lakehouse, intermediate level knowledge of Python (e.g. common Python libraries for DS/ML like Scikit-Learn, fundamental ML algorithms like regression and classification, model evaluation with common metrics) Labs: Yes Certification Path: Databricks Certified Machine Learning Associate

This course offers a deep dive into designing data models within the Databricks Lakehouse environment, and understanding the data products lifecycle. Participants will learn to align business requirements with data organization and model design leveraging Delta Lake and Unity Catalog for defining data architectures, and techniques for data integration and sharing. Prerequisites: Foundational knowledge equivalent to Databricks Certified Data Engineer Associate and familiarity with many topics covered in Databricks Certified Data Engineer Professional. Experience with: Basic SQL queries and table creation on Databricks Lakehouse architecture fundamentals (medallion layers) Unity Catalog concepts (high-level) [Optional] Familiarity with data warehousing concepts (dimensional modeling, 3NF, etc.) is beneficial but not mandatory. Labs: Yes

Data lakehouses continue to be hyped but do they replace or complement data lakes and data warehouses? Where do we stand from an architectural perspective? What is hype and what is real? What should be expected in the coming years?

Lakehouse has become a cornerstone in managing large and heterogeneous data by providing capabilities that simplify organizational data architecture, unify data assets, and help streamline, harmonize, and enhance data processes, operations and governance. Lakehouse provides high value to the organization, reduces technical debt, and prepares the organization for new frontiers like AI.

🎙️ Future of Data and AI Podcast: Episode 06 with Robin Sutara What do Apache, Excel, Microsoft, and Databricks have in common? Robin Sutara! From being a technician for Apache helicopters to leading global data strategy at Microsoft and now Databricks, Robin Sutara’s journey is anything but ordinary. In this episode, she shares how enterprises are adopting AI in practical, secure, and responsible ways—without getting lost in the hype. We dive into how Databricks is evolving beyond the Lakehouse to power the next wave of enterprise AI—supporting custom models, Retrieval-Augmented Generation (RAG), and compound AI systems that balance innovation with governance, transparency, and risk management. Robin also breaks down the real challenges to AI adoption—not technical, but cultural. She explains why companies must invest in change management, empower non-technical teams, and embrace diverse perspectives to make AI truly work at scale. Her take on job evolution, bias in AI, and the human side of automation is both refreshing and deeply relevant. A sharp, insightful conversation for anyone building or scaling AI inside the enterprise—especially in regulated industries where trust and explainability matter as much as innovation.

Lakehouse has become a cornerstone in managing large and heterogeneous data by providing capabilities that simplify organizational data architecture, unify data assets, and help streamline, harmonize, and enhance data processes, operations and governance. Lakehouse provides high value to the organization, reduces technical debt, and prepares the organization for new frontiers like AI.