delivery

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industry

Executive, Data Delivery

Joanna Gurry

“Dusting off the Cobwebs”— Moving off a 26-year-old Heritage Platform to Databricks [Teradata]

This session will explore how Adobe uses sophisticated data security architecture, the new Databricks security lakehouse and the Open Cybersecurity Schema Framework (OCSF) for scalable, real-time threat detection across 10 PB+ of security data. We’ll compare different approaches to OCSF implementation and demonstrate how Adobe processes vast security datasets efficiently — reducing query times by 18%, maintaining 99.4% SLA compliance and supporting 286 security users across 17 teams with 4.53K+ daily queries. By leveraging Databricks’ security lakehouse, serverless scaling and LLM-powered recommendations, Adobe has significantly improved processing efficiency and speed, resulting in major cost savings. We’ll discuss how OCSF enables advanced cross-tool analytics and automation, improving investigative efficiency. Finally, we’ll introduce Databricks’ new open-source OCSF toolkit, which simplifies security data normalization at scale, and invite the community to contribute.

Sr. Manager, Security Software Engineering

Karthik Venkatesan

Andrew Krioukov

Adobe’s Security Lakehouse: OCSF, Data Efficiency and Threat Detection at Scale

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: YesCertification Path: Databricks Certified Machine Learning Professional

Advanced Machine Learning Operations

Improving healthcare impacts us all. We highlight how Premier Inc. took risk-adjusted patient data from more than 1,300 member hospitals across America, applying a natural language interface using AI/BI Genie, allowing our users to discover new insights. The stakes are high, new insights surfaced represent potential care improvement and lives positively impacted. Using Genie and our AI-ready data in Unity Catalog, our team was able to stand up a Genie instance in three short days, bypassing costs and time of custom modeling and application development. Additionally, Genie allowed our internal teams to generate complex SQL, as much as 10 times faster than writing it by hand. As Genie and lakehouse apps continue to advance rapidly, we are excited to leverage these features by introducing Genie to as many as 20,000 users across hundreds of hospitals. This will support our members’ ongoing mission to enhance the care they provide to the communities they serve.

Senior Director, Analytics

Timothy Riddle

Software Architect

Jay Sharma

AI and Genie: Analyzing Healthcare Improvement Opportunities

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 Delta Live Tables 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.

Principal Product Manager

Leon Bovett

VP, Cloud Product Management & UX

Steven Kostrzewski

AI-Powered Marketing Data Management: Solving the Dirty Data Problem with Databricks

Databricks announced two new features in 2024: AI/BI Dashboards and AI/BI Genie. Dashboards is a redesigned dashboarding experience for your regular reporting needs, while Genie provides a natural language experience for your last-mile analytics. In this session, Databricks Solutions Architect and content creator Youssef Mrini will present alongside Databricks MVP and content creator Josue A. Bogran on how you can get the most value from these tools for your organization.  Content covered includes:<ul>	<li>Setup necessary, including Unity Catalog, permissions and compute</li>	<li>Building out a dashboard with AI/BI Dashboards</li>	<li>Creating and training an AI/BI Genie workspace to reliably deliver answers</li>	<li>When to use Dashboards, Genie, and when to use other tools such as PBI, Tableau, Sigma, ChatGPT, etc.</li></ul> Fluff-free, full of practical tips, and geared to help you deliver immediate impact with these new Databricks capabilities.

Senior Solution Engineer

Youssef Mrini

Solutions Architect Manager

Josue Bogran

AI/BI Dashboards and AI/BI Genie: Dashboards and Last-Mile Analytics Made Simple

AI/BI for Data Analysts

In this course, you will learn how to self-serve business insights from your company’s Databricks Data Intelligence Platform using AI/BI. After a tour of the fundamental components of the platform, you’ll learn how to interact with pre-created AI/BI Dashboards to explore your company’s data through existing charts and visualizations. You’ll also learn how to use AI/BI Genie to go beyond dashboards by asking follow-up questions in natural language to self-serve new insights, create visualizations, and share them with your colleagues. Pre-requisites: A working understanding of your organization’s business and key performance indicators. Labs: NoCertification Path: N/A

AI/BI for Self-Service Analytics

We are challenged with delivering TV viewership data in a way that satisfies customers needs and data privacy regulations. After POC’ing two other solutions, we went with Databricks Clean Rooms as it was the only one that could programmatically enforce k-anonymization constraints and differential privacy. While other solutions created bottlenecks through approval processes and rigid templates blocking our customers from writing arbitrary SQL queries, Clean Rooms allows them to explore the data while implicitly filtering out the data that does not meet the approval criteria. Clean Rooms has eliminated the need to replicate data, simplifying data management, improving efficiency and streamlining workflows. The implementation of Clean Rooms has allowed us to monetize our data and improve our internal processes. Clean Rooms is able to handle complex and diverse workloads without compromising on functionality, performance or security. It's built not only for our needs today but also for future use cases we plan to deploy.

Software Engineering Manager

Richard Sharrott

Sr Solutions Engineer

Johanna Hughes

Allowing Arbitrary SQL Queries of TV Viewership Data With Privacy Compliance Through Clean Rooms

Taxonomy generation is a challenge across industries such as retail, manufacturing and e-commerce. Incomplete or inconsistent taxonomies can lead to fragmented data insights, missed monetization opportunities and stalled revenue growth. In this session, we will explore a modern approach to solving this problem by leveraging Databricks platform to build a scalable compound AI architecture for automated taxonomy generation. The first half of the session will walk you through the business significance and implications of taxonomy, followed by a technical deep dive in building an architecture for taxonomy implementation on the Databricks platform using a compound AI architecture. We will walk attendees through the anatomy of taxonomy generation, showcasing an innovative solution that combines multimodal and text-based LLMs, internal data sources and external API calls. This ensemble approach ensures more accurate, comprehensive and adaptable taxonomies that align with business needs.

Managing Director of AI

Sudhir Gajre

Managing Director - Industry

Bill Martin

Automating Taxonomy Generation With Compound AI on Databricks

The insurance industry is at the crossroads of digital transformation, facing challenges from market competition and customer expectations. While conventional ML applications have historically provided capabilities in this domain, the emergence of Agentic AI frameworks presents a revolutionary opportunity to build truly autonomous insurance applications. We will address issues related to data governance and quality while discussing how to monitor/evaluate fine-tune models. We'll demonstrate the application of the agentic framework in the insurance context and how these autonomous agents can work collaboratively to handle complex insurance workflows — from submission intake and risk evaluation to expedited quote generation. This session demonstrates how to architect intelligent insurance solutions using Databricks Mosaic AI agentic core components including Unity Catalog, Playground, model evaluation/guardrails, privacy filters, AI functions and AI/BI Genie.

Sr. Delivery Solutions Architect

Amit Kumar Jha

Love Bajpai

Beyond Chatbots: Building Autonomous Insurance Applications With Agentic AI Framework

As organizations increasingly leverage sensitive data for AI applications, generating high quality synthetic data with mathematical guarantees of privacy has become crucial. This talk explores the use of Gretel Navigator to generate differentially private synthetic data that maintains high fidelity to the source data and high utility on downstream tasks across heterogeneous datasets. Our analysis covers a framework for privacy-preserving synthetic data generation with two use cases: patient events and e-commerce reviews. We reveal nuanced strategies for: calibrating privacy parameters ε and δ for mixed-modal data, leveraging both record-level and user-level differential privacy depending on which entity in the dataset requires protection, maintaining statistical properties and high utility on downstream classification tasks under stringent privacy constraints (e.g., <0.05 difference in AUC when using DP), and quantifying resilience to membership inference and attribute inference attacks.

Lipika Ramaswamy

Beyond the Privacy-Utility Tradeoff: Differential Privacy in Tabular Data Synthesis

In this course, you’ll learn how to define and schedule data pipelines that incrementally ingest and process data through multiple tables on the Data Intelligence Platform, using LakeFlow Pipelines (previously Delta Live Tables) in Spark SQL and Python. We’ll cover topics like how to get started with LakeFlow Pipelines, how LakeFlow Pipelines tracks data dependencies in data pipelines, how to configure and run data pipelines using the LakeFlow Pipelines UI, how to use Python or Spark SQL to define data pipelines that ingest and process data through multiple tables on the Data Intelligence Platform, using Auto Loader and LakeFlow Pipelines, how to use APPLY CHANGES INTO syntax to process Change Data Capture feeds, and how to review event logs and data artifacts created by pipelines and troubleshoot syntax. Pre-requisites: Beginner familiarity with the Databricks Data Intelligence Platform (selecting clusters, navigating the Workspace, executing notebooks), cloud computing concepts (virtual machines, object storage, etc.), production experience working with data warehouses and data lakes, intermediate experience with basic SQL concepts (select, filter, groupby, join, etc), beginner programming experience with Python (syntax, conditions, loops, functions), beginner programming experience with the Spark DataFrame API (Configure DataFrameReader and DataFrameWriter to read and write data, Express query transformations using DataFrame methods and Column expressions, etc.) Labs: YesCertification Path: Databricks Certified Data Engineer Associate

Build Data Pipelines with LakeFlow Pipelines (previously Delta Live Tables)

At Zillow, we have accelerated the volume and quality of our dashboards by leveraging a modern SDLC with version control and CI/CD. In the past three months, we have released 32 production-grade dashboards and shared them securely across the organization while cutting error rates in half over that span. In this session, we will provide an overview of how we utilize Databricks asset bundles and GitLab CI/CD to create performant dashboards that can be confidently used for mission-critical operations. As a concrete example, we'll then explore how Zillow's Data Platform team used this approach to automate our on-call support analysis, leveraging our dashboard development strategy alongside Databricks LLM offerings to create a comprehensive view that provides actionable performance metrics alongside AI-generated insights and action items from the hundreds of requests that make up our support workload.

Senior Software Development Engineer

Caleb Priester

Building Dashboards as a Production-Grade Data Product

In the dynamic world of sports betting, precision and adaptability are key. Sports traders must navigate risk management, limitations of data feeds, and much more to prevent small model miscalculations from causing significant losses. To ensure accurate real-time pricing of hundreds of interdependent markets, traders provide key inputs such as player skill-level adjustments, whilst maintaining precise correlations. Black-box models aren’t enough— constant feedback loops drive informed, accurate decisions. Join DraftKings as we showcase how we expose real-time metrics from our simulation engine, to empower traders with deeper insights into how their inputs shape the model. Using Spark Structured Streaming, Kafka, and Databricks dashboards, we transform raw simulation outputs into actionable data. This transparency into our engines enables fine-grained control over pricing― leading to more accurate odds, a more efficient sportsbook, and an elevated customer experience.

Senior Data science Engineer

Aaron Hope

Senior Data Science Engineer

Ethan Summers

Building Real-Time Sport Model Insights with Spark Structured Streaming

Seven years ago, Chick-fil-A set out to build a self-service, cloud-based data ecosystem to democratize data and analytics by integrating different technological components into an internal product that we called the Chick-fil-A Data Exchange, powered by AWS technologies. The adoption of Databricks Unity Catalog marks a transformative milestone in our journey. By integrating its advanced capabilities, we have bridged the gap between business intelligence (BI) and artificial intelligence (AI), enabling the seamless convergence of use cases across the organization. Unity Catalog’s features have revolutionized how we approach data management, ensuring data-driven insights are within reach for every member of our organization. Automations allowed us to seamlessly migrate a vast number of datasets while maintaining high standards of accuracy and governance. Our team demonstrated exceptional commitment and skill, completing the migration across the entire organization in just a few months.

Sr. Principal Team Leader

Alberto Rama

Chick-fil-A Unity Catalog Adoption

For most companies, building compound AI systems remains aspirational. LLMs are powerful, but imperfect, and their non-deterministic nature makes steering them to high accuracy a challenge. In this session, we’ll demonstrate how to build compound AI systems using SLMs and highly accurate mini-agents that can be integrated into agentic workflows. You'll learn about breakthrough techniques, including: memory RAG, an embedding algorithm that reduces hallucinations using embed-time compute to generate contextual embeddings, improving indexing and retrieval, and memory tuning, a finetuning algorithm that reduces hallucinations using a Mixture of Memory Experts (MoME) to specialize models with proprietary data. We’ll also share real-world examples (text-to-SQL, factual reasoning, function calling, code analysis and more) across various industries. With these building blocks, we’ll demonstrate how to create high accuracy mini-agents that can be composed into larger AI systems.

CEO & Cofounder

Sharon Zhou

Composing High-Accuracy AI Systems With SLMs and Mini-Agents

The convergence of cloud, data and AI is revolutionizing the pharmaceutical industry, creating a powerful ecosystem that drives innovation, efficiency and precision across the entire value chain. At Gilead, teams harness these technologies on a unified platform, accelerating drug discovery, streamlining clinical trials and enabling smarter manufacturing processes. Our approach leverages Databricks and data mesh to manage vast amounts of heterogeneous data, eliminating silos and enhancing collaboration. This integration facilitates personalized medicine, targeted marketing and optimized supply chains. This session will showcase use cases of traditional AI/ML and Generative AI, demonstrating how this cloud-based, AI-powered platform is transforming drug discovery, development, manufacturing and commercialization. Join us to explore the future of pharmaceutical innovation, where the synergy of cloud, data and AI is unlocking new possibilities for a healthier world.

Head of Cloud, Data, & Analytics

Murali Vridhachalam

Convergence of Cloud, Data and AI Ignites Innovation at Gilead

In this presentation, we'll show how we achieved a unified development experience for teams working on Mercedes-Benz Data Platforms in AWS and Azure. We will demonstrate how we implemented Azure to AWS and AWS to Azure data product sharing (using Delta Sharing and Cloud Tokens), integration with AWS Glue Iceberg tables through UniForm and automation to drive everything using Azure DevOps Pipelines and DABs.  We will also show how to monitor and track cloud egress costs and how we present a consolidated view of all the data products and relevant cost information. The end goal is to show how customers can offer the same user experience to their engineers and not have to worry about which cloud or region the Data Product lives in. Instead, they can enroll in the data product through self-service and have it available to them in minutes, regardless of where it originates.

Sr. Resident Solutions Architect

Aleksandar Dragojevic

Architect

Alexander Summa

Cross-Cloud Data Mesh with Delta Sharing and UniForm in Mercedes-Benz

Modern data organizations have moved beyond big data analytics to also incorporate advanced AI/ML data workloads. These workflows often involve multimodal datasets containing documents, images, long-form text, embeddings, URLs and more. Unity Catalog is an ideal solution for organizing and governing this data at scale. When paired with the Daft open source data engine, you can build a truly multimodal, AI-ready data lakehouse. In this session, we’ll explore how Daft integrates with Unity Catalog’s core features (such as volumes and functions) to enable efficient, AI-driven data lakehouses. You will learn how to ingest and process multimodal data (images, text and videos), run AI/ML transformations and feature extractions at scale, and maintain full control and visibility over your data with Unity Catalog’s fine-grained governance.

Co-Founder

Jay Chia

Daft and Unity Catalog: A Multimodal/AI-Native Lakehouse

<ol>	<li>Key Message: FrieslandCampina aims to be the top performer in dairy by combining 150 years of craftsmanship with data, analytics and AI to address shifting consumer preferences, complex supply chains and mandatory sustainability.</li>	<li>Data & AI-centric vision</li>	<li>Deep Dive: Backbone of data-centric FrieslandCampina	<ul>		<li>Technology — Fully leverage our global Data Platform on Databricks for high-impact use cases and AI-driven decision-making.</li>		<li>Organization — Mature hub & spoke model and data democratization with a data lakehouse approach. Future: Citizen engineers and inner sourcing.</li>		<li>Governance & Responsible AI — Unified governance model with Databricks Unity Catalog. Establishing trust in AI & data. Future: Responsible AI as an enabler for success, guiding scalability and innovation.</li>	</ul>	</li>	<li>Closing: Data & AI is the future of FrieslandCampina. Transformation requires courage, leadership, and a shared vision. Every data leader plays a role in this revolution. "What role will YOU play?"</li></ol>

Director Architecture and Engineering

Mayank Srivastava

Head of Analytics

Joëlle van der Bijl

Data Drives Dairy: Royal FrieslandCampina's Data-Centric Transformation With Databricks

In this course, you’ll learn how to have efficient data ingestion with LakeFlow Connect and manage that data with Databricks. We’ll cover topics such as ingestion with built-in connectors for popular SaaS applications, databases and file sources, as well as ingestion from cloud object storage, and batch and streaming ingestion. LakeFlow Connect is fully integrated with the Data Intelligence Platform including unified governance, observability, and Delta Lake for the foundation of a data lakehouse architecture. We'll cover the new connector components, setting up the pipeline, validating the source and mapping to the destination for each type of connector. We'll also cover how to ingest data with Batch to Streaming ingestion into Delta tables, using the UI with Auto Loader, automating ETL with Lakeflow Pipelines (previously Delta Live Tables) or using the API. Pre-requisites: Beginner familiarity with the Databricks Data Intelligence Platform (selecting clusters, navigating the Workspace, executing notebooks), cloud computing concepts (virtual machines, object storage, etc.), production experience working with data warehouses and data lakes, intermediate experience with basic SQL concepts (select, filter, groupby, join, etc), beginner programming experience with Python (syntax, conditions, loops, functions), beginner programming experience with the Spark DataFrame API (Configure DataFrameReader and DataFrameWriter to read and write data, Express query transformations using DataFrame methods and Column expressions, etc.) Labs: YesCertification Path: Databricks Certified Data Engineer Associate

Data Ingestion with LakeFlow Connect

In this course, you'll learn concepts and perform labs that showcase workflows using Unity Catalog - Databricks' unified and open governance solution for data and AI. We'll start off with a brief introduction to Unity Catalog, discuss fundamental data governance concepts, and then dive into a variety of topics including using Unity Catalog for data access control, managing external storage and tables, data segregation, and more. Pre-requisites: Beginner familiarity with the Databricks Data Intelligence Platform (selecting clusters, navigating the Workspace, executing notebooks), cloud computing concepts (virtual machines, object storage, etc.), production experience working with data warehouses and data lakes, intermediate experience with basic SQL concepts (select, filter, groupby, join, etc), beginner programming experience with Python (syntax, conditions, loops, functions), beginner programming experience with the Spark DataFrame API (Configure DataFrameReader and DataFrameWriter to read and write data, Express query transformations using DataFrame methods and Column expressions, etc.). Labs: YesCertification Path: Databricks Certified Data Engineer Associate

Data Management and Governance with UC

Data Modeling Strategies

Deep Sync, a leader in deterministic identity solutions, leverages the Databricks Lakehouse to manage an identity spine containing the most accurate and up-to-date attribute data for over 98% of U.S. households. In partnership with T-Mobile, Deep Sync is building a Data as a Service (DaaS) business on Databricks, enabling the use of consented subscriber data to create hyper-personalized experiences, measure marketing impact and more. This collaboration represents the first time T-Mobile’s consented data will be brought to market. The technical presentation, jointly delivered by Deep Sync and T-Mobile, will showcase the Databricks solutions powering this initiative, including Lakehouse (DBSQL, GenieRooms), Unity Catalog, Delta Sharing and multi-party Data Clean Rooms. The session will focus on Databricks solutions used to enable management, governance, security and collaboration between T-Mobile, Deep Sync and third-party customers.

Paul Turner

Data Monetization Through Delta Sharing and Data Clean Rooms

In this course, you’ll learn the fundamentals of preparing data for machine learning using Databricks. We’ll cover topics like exploring, cleaning, and organizing data tailored for traditional machine learning applications. We’ll also cover data visualization, feature engineering, and optimal feature storage strategies. Pre-requisites: Familiarity with Databricks workspace, notebooks, as well as Unity Catalog. An intermediate level knowledge of Python (scikit-learn, Matplotlib), Pandas, and PySpark. As well as with concepts of exploratory data analysis, feature engineering, standardization, and imputation methods). Labs: YesCertification Path: Databricks Certified Machine Learning Associate

Data Preparation for Machine Learning

In this course, you’ll learn how to apply patterns to securely store and delete personal information for data governance and compliance on the Data Intelligence Platform. We’ll cover topics like storing sensitive data appropriately to simplify granting access and processing deletes, processing deletes to ensure compliance with the right to be forgotten, performing data masking, and configuring fine-grained access control to configure appropriate privileges to sensitive data. Pre-requisites: Ability to perform basic code development tasks using the Databricks workspace (create clusters, run code in notebooks, use basic notebook operations, import repos from git, etc), intermediate programming experience with SQL and PySpark (extract data from a variety of file formats and data sources, apply a number of common transformations to clean data, reshape and manipulate complex data using advanced built-in functions), intermediate programming experience with Delta Lake (create tables, perform complete and incremental updates, compact files, restore previous versions etc.). Beginner experience with Delta Live Tables and streaming workloads. Labs: YesCertification Path: Databricks Certified Data Engineer Professional

Databricks Data Privacy

Databricks Devops for Pipelines

In this course, you’ll learn how to optimize workloads and physical layout with Spark and Delta Lake and and analyze the Spark UI to assess performance and debug applications. We’ll cover topics like streaming, liquid clustering, data skipping, caching, photons, and more. Pre-requisites: Ability to perform basic code development tasks using the Databricks workspace (create clusters, run code in notebooks, use basic notebook operations, import repos from git, etc), intermediate programming experience with SQL and PySpark (extract data from a variety of file formats and data sources, apply a number of common transformations to clean data, reshape and manipulate complex data using advanced built-in functions), intermediate programming experience with Delta Lake (create tables, perform complete and incremental updates, compact files, restore previous versions etc.) Labs: YesCertification Path: Databricks Certified Data Engineer Professional

Databricks Performance Optimization

In this course, you’ll learn how to define and schedule data pipelines that incrementally ingest and process data through multiple tables on the Data Intelligence Platform, using LakeFlow Pipelines (previously Delta Live Tables)in Spark SQL and Python. We’ll cover topics like how to get started with LakeFlow Pipelines, how LakeFlow Pipelines tracks data dependencies in data pipelines, how to configure and run data pipelines using the LakeFlow Pipelines UI, how to use Python or Spark SQL to define data pipelines that ingest and process data through multiple tables on the Data Intelligence Platform, using Auto Loader and LakeFlow Pipelines, how to use APPLY CHANGES INTO syntax to process Change Data Capture feeds, and how to review event logs and data artifacts created by pipelines and troubleshoot syntax. Pre-requisites: Beginner familiarity with the Databricks Data Intelligence Platform (selecting clusters, navigating the Workspace, executing notebooks), cloud computing concepts (virtual machines, object storage, etc.), production experience working with data warehouses and data lakes, intermediate experience with basic SQL concepts (select, filter, groupby, join, etc), beginner programming experience with Python (syntax, conditions, loops, functions), beginner programming experience with the Spark DataFrame API (Configure DataFrameReader and DataFrameWriter to read and write data, Express query transformations using DataFrame methods and Column expressions, etc.) Labs: YesCertification Path: Databricks Certified Data Engineer Associate

Databricks Streaming and LakeFlow Pipelines (previously Delta Live Tables)

In this course, you’ll learn how to orchestrate data pipelines with LakeFlow Jobs (previously Databricks Workflows) and schedule dashboard updates to keep analytics up-to-date. We’ll cover topics like getting started with LakeFlow Jobs, how to use Databricks SQL for on-demand queries, and how to configure and schedule dashboards and alerts to reflect updates to production data pipelines. Pre-requisites: Beginner familiarity with the Databricks Data Intelligence Platform (selecting clusters, navigating the Workspace, executing notebooks), cloud computing concepts (virtual machines, object storage, etc.), production experience working with data warehouses and data lakes, intermediate experience with basic SQL concepts (select, filter, groupby, join, etc), beginner programming experience with Python (syntax, conditions, loops, functions), beginner programming experience with the Spark DataFrame API (Configure DataFrameReader and DataFrameWriter to read and write data, Express query transformations using DataFrame methods and Column expressions, etc.) Labs: YesCertification Path: Databricks Certified Data Engineer Associate

Deploy Workloads with Lakeflow Jobs (previously Databricks Workflows)

Step into the world of Disney Streaming as we unveil the creation of our Foundational Medallion, a cornerstone in our architecture that redefines how we manage data at scale. In this session, we'll explore how we tackled the multi-faceted challenges of building a consistent, self-service surrogate key architecture — a foundational dataset for every ingested stream powering Disney Streaming's data-driven decisions. Learn how we streamlined our architecture and unlocked new efficiencies by leveraging cutting-edge Databricks features such as liquid clustering, Photon with dynamic file pruning, Delta's identity column, Unity Catalog and more — transforming our implementation into a simpler, more scalable solution. Join us on this thrilling journey as we navigate the twists and turns of designing and implementing a new Medallion at scale — the very heartbeat of our streaming business!

Director, Data Engineering

Mark Senerth

Disney's Foundational Medallion: A Journey Into Next-Generation Data Architecture

In this session, we'll explore how Rooms To Go enhances organizational collaboration by integrating AI/BI Genie with Microsoft Teams. Genie enables warehouse employees and members of the sales team to interact with data using natural language, simplifying data exploration and analysis. By connecting Genie to Microsoft Teams, we bring real-time data insights directly to a user’s phone. We'll provide a comprehensive overview on setting up this integration as well as a demo of how the team uses it daily. Attendees will gain practical knowledge to implement this integration, empowering their teams to access and interact with data seamlessly within Microsoft Teams.

Manager Data Engineering & Integration

Nathan Sundararajan

Solutions Architect

Ryan Bates

Empowering Business Users With Databricks — Integrating AI/BI Genie With Microsoft Teams

The new Budget Execution Validation process has transformed how the Navy reviews unspent funds. Powered by Databricks Workflows, MLflow, Delta Lake and Apache Spark™, this data-driven model predicts which financial transactions are most likely to have errors, streamlining reviews and increasing accuracy. In FY24, it helped review $40 billion, freeing $1.1 billion for other priorities, including $260 million from active projects. By reducing reviews by 80%, cutting job runtime by over 50% and lowering costs by 60%, it saved 218,000 work hours and $6.7 million in labor costs. With automated workflows and robust data management, this system exemplifies how advanced tools can improve financial decision-making, save resources and ensure efficient use of taxpayer dollars.

Data Science Director

Teneika Askew

Empowering the Warfighter With AI

This session will showcase Bosch’s journey in consolidating supply chain information using the Databricks platform. It will dive into how Databricks not only acts as the central data lakehouse but also integrates seamlessly with transformative components such as dbt and Large Language Models (LLMs). The talk will highlight best practices, architectural considerations, and the value of an interoperable platform in driving actionable insights and operational excellence across complex supply chain processes. Key Topics and Sections<ul>	<li>Introduction & Business Context</li>	<li>Brief Overview of Bosch’s Supply Chain Challenges and the Need for a Consolidated Data Platform.</li>	<li>Strategic Importance of Data-Driven Decision-Making in a Global Supply Chain Environment.</li>	<li>Databricks as the Core Data Platform</li>	<li>Integrating dbt for Transformation</li>	<li>Leveraging LLM Models for Enhanced Insights</li></ul>

Development Lead

Satish Karunakaran

Project Lead Logistics Innovations

Marc-Alexander Frey

End-to-End Interoperable Data Platform: How Bosch Leverages Databricks Supply Chain Consolidation

Mastercard is a global technology company whose role is anchored in trust. It supports 3.4 billion cards and over 143 billion transactions annually. To address customers’ increasing data volume and complex privacy needs, Mastercard has developed a novel service atop Databricks’ Clean Rooms and broader Data Intelligence Platform. This service combines several Databricks components with Mastercard’s IP, providing an evolved method for data-driven insights and value-added services while ensuring a unique standalone turnkey service. The result is a secure environment where multiple parties can collaborate on sensitive data without directly accessing each other’s information. After this session, attendees will understand how Mastercard used its expertise in privacy-enhancing technologies to create collaboration tools powered by Databricks’ Clean Rooms, AI/BI, Apps, Unity Catalog, Workflows and DatabricksIQ — as well as how to take advantage of this new privacy-enhancing service directly.

Lead Solutions Architect

Spencer Cook

VP Data Strategy

John Derrico

Evolving Data Insights With Privacy at Mastercard

Integrating external vendor data often means facing fragmented pipelines, costly ETL, and lengthy onboarding cycles. Join us to learn how a customer partnered with FactSet, using Delta Sharing to break through these barriers and significantly elevate their analytics capabilities. Why Attend? Efficiency gains: see how onboarding delays, redundant engineering tasks, and inefficiencies in sharing data across teams were resolved Real solutions: uncover how Delta Sharing and powerful analytics capabilities transform workflows, reducing complex tasks from days to minutes Valuable outcomes: learn about simplified data pipelines, accelerated insights, and faster onboarding achieved through curated notebooks We'll explore how your organization can overcome similar challenges and set the stage for future innovation. Discover practical ways to enhance your data capabilities, advance strategic initiatives, and achieve faster insights with seamless integration of FactSet data on Databricks.

Senior Product Manager

Kristen Clark

FactSet in Collaboration with a Leading Customer: Harnessing Delta Sharing for Accelerated Insights

As language models have advanced, they have moved beyond code completion and are beginning to tackle software engineering tasks in a more autonomous, agentic way. However, evaluating agentic capabilities is challenging. To address this, we first introduce SWE-bench, a benchmark built from real GitHub issues that has become the standard for assessing AI’s ability to resolve complex software tasks in large codebases. We will discuss the current state of the field, the limitations of today’s models, and how far we still are from truly autonomous AI developers. Next, we will explore the fundamentals of agents based on hands-on demonstrations with SWE-agent, a simple yet powerful agent framework designed for software engineering but adaptable to a variety of domains. By the end of this session, you will have a clear understanding of the current frontier of agentic AI in software engineering, the challenges ahead and how you can experiment with AI agents in your own workflows.

Kilian Lieret

From Code Completion to Autonomous Software Engineering Agents

In this talk, we will explore how AI and advanced infrastructure are transforming Insulet's development and operations.We'll highlight how our innovations have reduced scrap part costs through manufacturing analytics, showcasing efficiency and cost savings. On leveraging Databricks AI solutions and productivity, it not only identifies errors but also fixes code and assists in writing complex queries. This goes beyond suggestions, providing actual solutions. On the infrastructure side, integrating Spark with Databricks simplifies setup and reduces costs. Additionally Databricks LakeFlow connectors enable real-time updates without much coding and much simplification as we integrate Salesforce.  We'll also discuss real-time processing of patient data, demonstrating how Databricks drives efficiency and productivity. Join us to learn how these innovations enhance efficiency, cost savings and performance.

Director

Shweta Shetty

From Code to Insights: Leveraging Advanced Infrastructure and AI Capabilities.

In today’s fast-paced business environment, access to comprehensive, credible, real-time and historical data in one place is essential for effective decision-making and risk management. LSEG’s Machine Readable News (MRN) solution transforms unstructured data into actionable insights, with ultra-low latency delivery and extensive historical news archives powered by AI and natural language processing (NLP). Leveraging Databricks Delta Sharing, businesses can seamlessly integrate this valuable data with their own internal datasets, additional LSEG data and third-party sources. This combination enables clients to optimize workflows, enhance risk models and make smarter, faster decisions across diverse use cases — from event-based trading to risk management.  In this session, we’ll explore how Delta Sharing facilitates the integration of diverse, trustworthy data, unlocking new opportunities and driving smarter investment strategies.

Global Head of Cloud Partners

Naftali Cohen

Director, Data Distribution

Simon Gauld

From News to Insights: Unlock Actionable Outcomes With LSEG’s Machine Readable News and DeltaSharing

This course provides participants with information and practical experience in building advanced LLM (Large Language Model) applications using multi-stage reasoning LLM chains and agents. In the initial section, participants will learn how to decompose a problem into its components and select the most suitable model for each step to enhance business use cases. Following this, participants will construct a multi-stage reasoning chain utilizing LangChain and HuggingFace transformers. Finally, participants will be introduced to agents and will design an autonomous agent using generative models on Databricks. Pre-requisites: Solid understanding of natural language processing (NLP) concepts, familiarity with prompt engineering and prompt engineering best practices, experience with the Databricks Data Intelligence Platform, experience with retrieval-augmented generation (RAG) techniques including data preparation, building RAG architectures, and concepts like embeddings, vectors, and vector databases Labs: YesCertification Path: Databricks Certified Generative AI Engineer Associate

Gen AI Application Development

This course introduces learners to deploying, operationalizing, and monitoring generative artificial intelligence (AI) applications. First, learners will develop knowledge and skills in deploying generative AI applications using tools like Model Serving. Next, the course will discuss operationalizing generative AI applications following modern LLMOps best practices and recommended architectures. Finally, learners will be introduced to the idea of monitoring generative AI applications and their components using Lakehouse Monitoring. Pre-requisites: Familiarity with prompt engineering and retrieval-augmented generation (RAG) techniques, including data preparation, embeddings, vectors, and vector databases. A foundational knowledge of Databricks Data Intelligence Platform tools for evaluation and governance (particularly Unity Catalog). Labs: YesCertification Path: Databricks Certified Generative AI Engineer Associate

Gen AI Deployment and Monitoring

This course introduces learners to evaluating and governing GenAI (generative artificial intelligence) systems. First, learners will explore the meaning behind and motivation for building evaluation and governance/security systems. Next, the course will connect evaluation and governance systems to the Databricks Data Intelligence Platform. Third, learners will be introduced to a variety of evaluation techniques for specific components and types of applications. Finally, the course will conclude with an analysis of evaluating entire AI systems with respect to performance and cost. Pre-requisites: Familiarity with prompt engineering, and experience with the Databricks Data Intelligence Platform. Additionally, knowledge of retrieval-augmented generation (RAG) techniques including data preparation, embeddings, vectors, and vector databases Labs: YesCertification Path: Databricks Certified Generative AI Engineer Associate

Gen AI Evaluation and Governance

This course is designed to introduce participants to contextual GenAI (generative artificial intelligence) solutions using the retrieval-augmented generation (RAG) method. Firstly, participants will be introduced to the RAG architecture and the significance of contextual information using Mosaic AI Playground. Next, the course will demonstrate how to prepare data for GenAI solutions and connect this process with building an RAG architecture. Finally, participants will explore concepts related to context embedding, vectors, vector databases, and the utilization of the Mosaic AI Vector Search product. Pre-requisites: Familiarity with embeddings, prompt engineering best practices, and experience with the Databricks Data Intelligence Platform Labs: YesCertification Path: Databricks Certified Generative AI Engineer Associate

Gen AI Solution Development

Prada has developed a complex solution, leveraging MosaicAI which powers its e-commerce search bar. The backbone is a 70B model and a Vector Store, which collaborates with additional filterings and AI solutions to suggest not only the perfect outfit for each occasion, but also provide alternative solutions and similar items.

data scientist leader

Andrea Adami

Senior Data Scientist

Maria Paola Tatulli

GenAI-Powered Shopping Assistant for Prada e-Commerce Search Bar

Databricks Unity Catalog and AI/BI Genie are helping us make our data AI ready. Unity Catalog provides a comprehensive governance framework, enabling our team  to implement secure data organization, access control, and metadata management. Genie spaces help us leverage this foundation to create domain-specific AI environments, allowing business users to interact with data using natural-language queries. We were able to stand up a LLM-powered conversational interface for our business users on our Gold standard leasing data products using AI/BI Genie which also enabled our business to add domain knowledge in instructions, add certified queries, and influence the context to the LLM― all using the same interface, enabling business users with self-service of AI on qualified datasets. UC and AI/BI Genie significantly reduced the complexity of building AI for BI and helped integrate GenAI into our text-to-SQL use cases possible, accessible, and quantifiable.

Arvind Krishnamoorthy

Sr Business Intelligence Mgr

Brian Schober

Getting Data AI Ready: How Good Governance Practices Led to Accurate NLQ Through Genie Rooms

Discover how Stack Overflow optimized its data engineering workflows using Databricks Asset Bundles (DABs) for scalable and efficient pipeline deployments. This session explores the structured pipeline architecture, emphasizing code reusability, modular design and bundle variables to ensure clarity and data isolation across projects. Learn how the data team leverages enterprise infrastructure to streamline deployment across multiple environments. Key topics include DRY-principled modular design, essential DAB features for automation and data security strategies using Unity Catalog. Designed for data engineers and teams managing multi-project workflows, this talk offers actionable insights on optimizing pipelines with Databricks evolving toolset.

Senior Data Engineer

Chelsea Zhang

Harnessing Databricks Asset Bundles: Transforming Pipeline Management at Scale at Stack Overflow

Feastables, founded by YouTube sensation MrBeast, partnered with Engine to build a modern, AI-enabled BI ecosystem that transforms complex, disparate data into actionable insights, driving smarter decision-making across the organization. In this session, learn how Engine, a Built-On Databricks Partner, brought expertise combined with strategic partnerships that enabled Feastables to rapidly stand up a secure, modern data estate to unify complex internal and external data sources into a single, permissioned analytics platform. Feastables unlocked the power of cross-functional collaboration by democratizing data access throughout their enterprise and seamlessly integrating financial, retailer, supply chain, syndicated, merchandising and e-commerce data. Discover how a scalable analytics framework combined with advanced AI models and tools empower teams with Smarter BI across sales, marketing, supply chain, finance and executive leadership to enable real-time decision-making at scale.

SVP of Data Science

Daniel Palmer

How Feastables Partners With Engine to Leverage Advanced Data Models and AI for Smarter BI

FedEx, a global leader in transportation and logistics, faced a common challenge in the era of big data: how to democratize data and foster data-driven decision making with thousands of data practitioners at FedEx wanting to build models, get real-time insights, explore enterprise data, and build enterprise-grade solutions to run the business. This breakout session will highlight how FedEx overcame challenges in data governance and security using Unity Catalog, ensuring that sensitive information remains protected while still allowing appropriate access across the organization. We'll share their approach to building intuitive self-service interfaces, including the use of natural-language processing to enable non-technical users to query data effortlessly. The tangible outcomes of this initiative are numerous, but chiefly: increased data literacy across the company, faster time-to-insight for business decisions, and significant cost-savings through improved operational efficiency.

Patrick Brown

How FedEx Achieved Self-Serve Analytics and Data Democratization on Databricks

This talk takes you through the Nationwide Security and Infrastructure data team's journey of migrating from HMS to UC. Discover how HMS federation simplified our transition to UC, allowing for an incremental migration that minimized disruption to data consumers while optimizing our data layout. We’ll share the key technical decisions, challenges faced and lessons learned along the way. The migration process wasn’t without its hurdles, so we’ll walk you through our detailed, step-by-step approach covering planning, execution and validation. We will also showcase the benefits realized, such as improved data governance, more efficient data access and enhanced operational performance. Join us to gain practical insights into executing complex data migrations with a focus on security, flexibility and long-term scalability.

Praveena Edward Panneer Selvam

How HMS Federation Powered Nationwide’s Seamless and Efficient Unity Catalog Migration

Don't miss this session where we demonstrate how the Texas Rangers baseball team is staying one step ahead of the competition by going back to the basics. After implementing a modern data strategy with Databricks and winnng the 2023 World Series the rest of the league quickly followed suit. Now more than ever, data and AI are a central pillar of every baseball team's strategy driving profound insights into player performance and game dynamics. With a 'fundamentals win games' back to the basics focus, join us as we explain our commmitment to world-class data quality, engineering, and MLOPS by taking full advantage of the Databricks Data Intelligence Platform. From system tables to federated querying, find out how the Rangers use every tool at their disposal to stay one step ahead in the hyper competitive world of baseball.

Senior Analyst

Michael Topol

Data Engineer

Oliver Dykstra

How the Texas Rangers Use a Unified Data Platform to Drive World Class Baseball Analytics

Unlock the full potential of your BI tools with Databricks. This session explores how features like Photon, Databricks SQL, Liquid Clustering, AI/BI Genie and Publish to Power BI enhance performance, scalability and user experience. Learn how Databricks accelerates query performance, optimizes data layouts and integrates seamlessly with BI tools. Gain actionable insights and best practices to improve analytics efficiency, reduce latency and drive better decision-making. Whether migrating from a data warehouse or optimizing an existing setup, this talk provides the strategies to elevate your BI capabilities.

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