delivery

areasofinterest

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Director

Tim Hanchin

Lead Data Scientist

Gregory Islas

 How Databricks Apps Facilitated Model Retraining

In early 2025, Alpura faced fragmented data, multiple BI tools, weak governance, and no scalable AI foundation. In just 120 days, we rebuilt the entire data platform on Databricks. Using the Medallion Architecture (Bronze, Silver, Gold), Unity Catalog for enterprise governance, and Databricks Metrics for a centralized semantic layer, we created a governed, scalable lakehouse foundation.We consolidated more than five legacy platforms without disrupting operations and eliminated KPI inconsistencies by standardizing business logic. On top of this foundation, we rapidly delivered 100+ AI/BI assets, including 10+ production-grade applications such as a Walmart Scintilla analytics app and an Ambassador Program app—built in days, not months. Leveraging Genie AI, we enabled conversational BI to accelerate adoption.This session shares a practical blueprint for building an AI/BI factory at enterprise speed.

CTDO

Marvin Nahmias

Javier Hauss

0 to 100 AI/BI Apps in 120 Days: Building an Enterprise Data & AI Platform from Scratch @ Alpura

Connected vehicle data is utilized in various services that benefit society, such as disaster response and road condition monitoring. However, processing connected data through high-speed streaming while ensuring privacy and governance is not straightforward, due to the inherent characteristics of connected data and the need to handle PB-scale volumes.Toyota similarly found that its existing architecture, which utilized Snowflake and AWS services, could not meet all its requirements. By adopting Lakeflow Spark Declarative Pipeline with Liquid Clustering, they were able to solve this challenge, achieving a 25% performance improvement and 24% cost reduction compared to the existing service.We will share the results of this trial and present our future outlook toward autonomous driving and zero accidents, enabled by these achievements.

Manager, Field Engineering

Satoshi Kuramitsu

General Manager

Yoshihiro Oe

Accelerating the Future of Mobility: Unlock Real-Time Data Streaming for Connected Vehicle Platform

This hands-on course introduces learners to evaluating, governing, and securing agentic AI systems on Databricks. You'll begin by exploring the motivation for evaluation and governance frameworks and how they connect to the Databricks Data Intelligence Platform. Next, you'll learn how to apply MLflow evaluation metrics and a variety of evaluation techniques, including online evaluation, synthetic evaluation, and building evaluation datasets from MLflow traces. The course then examines how Unity Catalog governance extends to agents' functions, models, and tools, ensuring proper access control, auditability, and compliance. Finally, you'll learn Databricks AI Security Framework (DASF) and how to secure agents with Mosaic AI Gateway, applying guardrails, rate limits, and policy filters to enforce safe and reliable usage.

Agent Evaluations

Maintenance technicians at 7-Eleven rely on preventive maintenance, catalogue, and parts documents for equipment like HVACs and ovens—often using only their phones. These documents span thousands of files in formats like PDFs and spreadsheets stored in shared drive, making it hard to find the right information quickly. High attrition and missed training sessions compounds knowledge gaps. To address this, 7-Eleven developed a GenAI agentic system using Mosaic AI, Agent Bricks and Databricks Hybrid Search that enables technicians to 1) Retrieve relevant maintenance documents with concise answers 2) Capture equipment images and receive troubleshooting solutions 3) Access parts information by equipment brand 4) Get solutions from the internet. This chatbot bridges knowledge gaps with instant access to institutional knowledge. We will walk through how we built this system, our evaluation methodologies and the 3-step prompt framework including guardrails for safe internet searches.

Staff Engineer

Sumedh Datar

AI Agents for the Frontline: 7-Eleven's GenAI Maintenance Assistant

This hands-on course covers DevOps principles, including CI/CD and testing, applied to Databricks data engineering pipelines. It focuses on continuous deployment using the Databricks REST API, SDK, and CLI, specifically incorporating Databricks Asset Bundles (DABs) for streamlined deployment across environments. You will learn to modify, validate, deploy, and execute DABs for multiple configurations using the Databricks CLI. The course also introduces using Visual Studio Code as an IDE for local development and concludes with automating deployment using GitHub Actions and DABs. By the end, you'll be able to automate Databricks project deployments with DABs, improving efficiency through DevOps practices. Note: Hands-on training courses will be updated to reflect the newest product and feature announcements from Data + AI Summit in June 2026.

Automate Deployment with Databricks Asset Bundles

Is your data engineering team trapped in brittle schema fixes and rising costs? As data sources multiply, traditional ingestion stalls enterprise insights. At Capital One Software, we’ve moved beyond static ETL to a "self-evolving" framework that treats data as a dynamic stream. This session reveals our modular architecture using Databricks Auto Loader to bridge multi-platform data into S3 with zero manual overhead. We’ll dive into how Schema Evolution and Rescue Columns handle upstream drift automatically, keeping pipelines live when source systems change.Key Takeaways:<ul>	<li>Modular Design: Decouple ingestion from transformation for reusable, enterprise-scale patterns.</li>	<li>Dynamic Schema Management: Strategies to detect and adapt to drift without breaking dependencies.</li>	<li>Cost Optimization: Real-world tactics for balancing trigger intervals and compute for peak efficiency. Transition from reactive maintenance to automated data empowerment.</li></ul>

Distinguished Engineer

Yudhish Batra

Director of Software Engineering & Data 

Syed Mehmood

Beyond Batch: Engineering Self-Evolving Ingestion with Databricks Auto Loader

Your agent answered but can you prove why, what it cost, and what breaks when a tool or prompt changes? AI spend is forecast near $1.5T in 2025 and GenAI at $644B, yet Gartner predicts >40% of agentic AI projects will be canceled by end-2027 as costs rise and controls lag. At adidas (200+ serving endpoints, 300+ data & AI products, 6k+ registered models, 600k+ pipeline runs), we go beyond per-run traces to an Agent Digital Twin: a lakehouse control plane mapping agent→tool→prompt→retrieval→model call→post-processing, then rolling up risk, quality, and unit economics across a fleet. You’ll see a Databricks pattern with MLflow Tracing, Unity Catalog + system tables for audit evidence and DBU/cost signals, plus eval/optimization loops with governance guardrails. Key highlights:<ul>	<li>Digital-twin control plane for fleets</li>	<li>Per-hop unit economics + cost leak forensics</li>	<li>Continuous audit evidence by design</li>	<li>ROI attribution by agent behavior</li></ul>

Senior Solution Architect

Pandey, Rahul

Resident Solutions Architect

Mahavir Teraiya

Beyond the Trace: adidas’ Agent Digital Twin for Governance, Cost, and ROI

Traditional "data-to-compute" models create latency and fragmented governance that hinder Agentic AI. To solve this, Workday and Databricks advocate "bringing agents to data" via the Databricks Lakehouse. By operating on data in situ, autonomous agents maintain real-time consistency without the risks of replication. This architecture enables reliable multi-agent coordination at scale while ensuring unified data integrity and reduced operational costs across the enterprise.The technical core uses Apache Iceberg for ACID transactions and snapshot isolation, providing a stable foundation for agent reasoning. Databricks Unity Catalog adds a unified governance layer, enforcing fine-grained security, lineage, and auditability. This production-grade approach significantly cuts query latency and storage expenses, allowing organizations to scale from single-agent pilots to hundreds of coordinated agents across multiple departments like, Customer Experience, RevOps, and Analytics departments.

Director, Product

Jason Reid

Senior Director, Data and AI Platforms

Phoenix U Majumder

Bringing Agents to Data: How Workday Architects Governed Agentic AI with Iceberg and Unity Catalog

This hands-on course introduces users to the essential concepts and skills needed to build data pipelines using Lakeflow Spark Declarative Pipelines (SDP) in Databricks for incremental batch or streaming ingestion and processing through multiple streaming tables and materialized views. Designed for data engineers new to Spark Declarative Pipelines, the course provides a comprehensive overview of core components such as incremental data processing, streaming tables, materialized views, and temporary views, highlighting their specific purposes and differences. Note: Hands-on training courses will be updated to reflect the newest product and feature announcements from Data + AI Summit in June 2026.

Build Data Pipelines with Lakeflow Spark Declarative Pipelines

This hands-on course introduces how to build single-agent, multi-agent, and multi-model systems on Agent Bricks, including key components, architectures, and use cases. You'll learn how to create agent tools using Unity Catalog functions to extend agent capabilities. You'll begin by using Genie to build agents that can interact with structured data, before extending these concepts to create a second agent designed for working with unstructured data and external resources. Finally, you'll explore how Agent Bricks provides a declarative approach to building multi-agent applications, offering a streamlined alternative to manual development. Note: Hands-on training courses will be updated to reflect the newest product and feature announcements from Data + AI Summit in June 2026.

Building Agentic Applications on Databricks

Many organizations use Databricks for analytics and AI, but still build business applications on separate stacks, disconnected from the Lakehouse.We share how we use Databricks as a full application platform, combining analytics, operational data, and AI into production-grade systems.We will show how we built DNA Port and Marvis AI using Databricks Apps, Lakebase, Lakeflow Sync Tables, and MLflow for governed model lifecycle management. We will also demonstrate how MCP securely exposes trusted Lakehouse data to AI copilots. Through real-world demos, we show how user actions write to Lakebase, trigger AI workflows, and surface insights in real time.Today, this platform supports 200+ vessels and 300+ on-shore employees, reducing manual reporting by 40% and improving application time to market by 50%.Attendees will learn practical architecture patterns, governance with Unity Catalog, and lessons learned in building scalable, AI-powered enterprise applications on Databricks.

Simon Fassot

Building Agentic Business Applications on Databricks with Lakebase, MCP, and Marvis AI

This hands-on course introduces Databricks Apps, a new feature that empowers developers to build and deploy secure data and AI applications directly on the Databricks platform. It is primarily designed for data scientists, data engineers, and developers who need to create custom applications that make data insights accessible to non-technical users within their organizations. The course begins with an overview of the main components of Databricks Apps, followed by the deployment of a simple app using available templates. Next, you will learn how to build and deploy a data-driven application that integrates with other platform features. The course then covers how to set up an IDE for Databricks Apps, and how to edit and deploy an app using an IDE. It concludes with how to implement security and best practices for an enterprise app.

Building Enterprise Applications with Databricks Apps

This hands-on course teaches you how to build retrieval-augmented generation (RAG) agents on Databricks using Agent Bricks’ tooling—including Vector Search, AI Playground, and the Foundation Model APIs. First, you'll begin with prompt engineering and the fundamentals of retrieval and augmented generation. Next, you'll learn how to ingest, process, and prepare documents for use in a retrieval pipeline. Then you'll explore embedding vectors, vector database design, and how to use Vector Search in a Databricks environment. Finally, you will build a retrieval agent end-to-end and trace its behavior using MLflow Tracing for observability and debugging. Note: Hands-on training courses will be updated to reflect the newest product and feature announcements from Data + AI Summit in June 2026.

Building RAG Agents with Agent Bricks

AstraZeneca modernized its Commercial data estate, transitioning from legacy EMR to a high-performance Global Commercial Lakehouse. At the core lies Unity Catalog (UC), established as the unified governance layer. We demonstrate how UC enables a true open architecture, orchestrating seamless federation for legacy Glue workloads and utilizing Iceberg REST integration to provide zero-copy access for iceberg clients eg Snowflake.The transformation was powered by a multi-agent AI migration system, utilizing LLMs and testing agents to automate the conversion of 250+ pipelines into a rigorous Medallion architecture. Key components include Databricks Asset Bundles for versioned CI/CD, Liquid Clustering for storage optimization.Processing complex external data sources (Claims, CRM, Digital), this platform delivered 50% cost savings. This session covers the multi-agent migration architecture, UC-centric interoperability design patterns, and lessons from modernizing commercial data at scale.

Commercial Data Lake Capability Lead

Paul Kuntz

Building the Commercial Lakehouse: AI-Driven, Open, and Interoperable

CMSPI built a Databricks-native analytics app that automates payments optimization at enterprise scale—replacing spreadsheets with a governed platform that processes billions of transactions and delivers measurable time and cost savings for global merchants.Lakebase powers real-time scenario configuration, while Delta tables and SQL Warehouses enable interactive analytics. Databricks Workflows orchestrate a mathematical solver (PROM) that evaluates billions of routing combinations and returns results in 30–40 minutes—eliminating 30+ hours of manual work per initial analysis and uncovering ~10% incremental savings opportunities.This session shares CMSPI’s path to productization: access control with Entra ID groups, MLOps for approval-rate prediction models, and external monetization via Delta Sharing to non-Databricks consumers. The architecture demonstrates the “built on Databricks” pattern for secure, governed apps—with plans to expand collaboration using Databricks Clean Rooms.

VP of data

Jordan Pierce

Solutions Architect

Sanjay Ashok

Built on Databricks: How CMSPI Scaled Payments Routing and Optimization Using LakeBase and DBSQL

This hands-on course equips learners with hands-on expertise to implement robust, enterprise-scale data governance in Databricks using Unity Catalog. Implement access control at scale with ABAC, enable comprehensive observability and auditing, monitor and assure data quality, visualize and manage complete data and model lineage, and effectively track usage, costs, and data quality across your platform. Note: Hands-on training courses will be updated to reflect the newest product and feature announcements from Data + AI Summit in June 2026.

Data Governance at Scale

Atlassian’s internal data platform is the backbone for analytics, product insights, and business decisions across the company. Managing data access policies at this scale is complex. In this talk, we’ll share how Atlassian embeds robust data governance policies, balancing agility with compliance, and innovation with security. Learn how we handle access management, data classification, and policy enforcement across diverse teams and tools. A key part of our approach is leveraging Databricks Attribute-Based Access Control (ABAC) to manage and abstract complex business policies. ABAC enables us to flexibly enforce fine-grained access controls (FGAC) using data tagging along with tag-driven policies, adapting quickly to changing requirements while keeping implementation logic abstracted from users. Join us for insights into the challenges, lessons learned, and solutions that keep our data platform secure and well-governed.

Senior Software Engineer

Gerald Nakhle

Data Governance at Scale with ABAC at Atlassian

This hands-on course explores how to achieve seamless data interoperability between systems using Databricks Unity Catalog as a unified governance layer, using both Delta and Iceberg open table formats. Participants will learn to implement, manage and optimize a data environment where tables can be accessed by both Delta and Iceberg clients, enabling cross platform analytics and eliminating data silos.

Data Interoperability with Unity Catalog

Most data quality tools don’t fail because the checks are wrong; they fail because users don’t understand how to use them.In this talk, we’ll share how we rebuilt our internal data quality platform using Databricks Apps and Databricks Workflows table triggers to prioritize usability and holistic visibility. We moved away from a config-file-driven model owned by a small group of platform engineers toward a user-centric approach that empowers teams to create, test, and manage their own data quality rules.With a Databricks App as the central interface, users can define and test rules, inspect production run history, and track data quality trends across the tables and teams they own. Combined with table-triggered workflows, checks run automatically as data changes without added operational complexity.This session covers our architecture, key design decisions, and lessons learned, and shows how Databricks Apps can turn data infrastructure into a user-friendly platform.

Sr Software Engineer

Trinity Xia

Databricks Apps: A Magic Wand for Driving Data Quality Adoption and Observability

In this hands-on 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, Photon, and more. Note: Hands-on training courses will be updated to reflect the newest product and feature announcements from Data + AI Summit in June 2026.

Databricks Performance Optimization

This hands-on course guides you through deploying and monitoring agentic AI systems in the Databricks environment. You will start by learning how to deploy agents in batch mode using AI Functions and integrate them into data pipelines. Next, you'll explore real-time deployment using Model Serving, including deploying agents as REST endpoints and managing autoscaling, versioning, and governance via Mosaic AI Model Serving. You'll then turn to observability and monitoring, using MLflow tracing and scoring functions to capture metrics, analyze requests and responses, and detect anomalies in production. Finally, you'll learn best practices for deploying agents with built-in trace collection, inference logging, and feedback loops to support ongoing evaluation and robustness. Note: Hands-on training courses will be updated to reflect the newest product and feature announcements from Data + AI Summit in June 2026.

Deploying and Monitoring Agents on Databricks

In this hands-on course, you’ll learn how to use the features Databricks provides for business intelligence needs: AI/BI Dashboards and AI/BI Genie. As a Databricks Data Analyst, you will be tasked with creating AI/BI Dashboards and AI/BI Genie Spaces within the platform, managing the access to these assets by stakeholders and necessary parties, and maintaining these assets as they are edited, refreshed, or decommissioned over the course of their lifespan. This course intends to instruct participants on how to design dashboards for business insights, share those with collaborators and stakeholders, and maintain those assets within the platform. Participants will also learn how to utilize AI/BI Genie Spaces to support self-service analytics through the creation and maintenance of these environments powered by the Databricks Data Intelligence Platform.

Developing BI Dashboards with AI/BI

Mobile game studios run multiple experiments simultaneously, but daily insights are often delayed by manual data preparation, inconsistent methods, and ad hoc notebooks. This presentation shows how SEGA HARDlight migrated from Tableau to Databricks and built an automated framework for experiment analysis: it ingests definitions and telemetry, applies uniform statistical models, and publishes a daily dashboard with an LLM-generated summary and KPI diagnostics. Unity Catalog manages assets and lineage, while Lakeflow streamline pipelines. MLflow ensures consistent experiment tracking, and AI/BI dashboards with Genie enable natural language exploration. The dashboard locks in results and decisions, creating an auditable record of truth. Early estimates show over eight hours saved weekly for the data team and roughly half a day saved per new query through automation, aiming to double testing capacity—while also boosting trust and consistency across experiments.

Senior Data Scientist

Joel Dias

From 8 Hours to 8 Minutes: Automating A/B Test Analysis on Databricks

As enterprises race to operationalize AI at scale, legacy data warehouses have become a critical bottleneck. In this session, Databricks and McKinsey share proven, real world best practices for modernizing data platforms to unlock analytics, GenAI, and agentic workloads with speed, governance, and cost efficiency. Drawing from large scale transformations including McKinsey’s Nebula platform, we outline a pragmatic modernization blueprint that balances risk, payback period, and business impact. Attendees will learn how to migrate from brittle legacy architectures to an open Lakehouse and federated data mesh, reduce total cost of ownership by up to 40%, accelerate time to insight, and enable secure self service analytics and AI across the enterprise. This talk delivers concrete lessons for executives and practitioners navigating modernization in the AI era.

Strategic Account Exec

Kelly Hall

From Legacy to Lakehouse:  Proven Best Practices from Databricks and McKinsey on Modernization

Modern airlines need more than dashboards, they need intelligence at speed. Etihad Airways is transforming its analytics landscape by moving from traditional BI platforms to Databricks AI/BI, redefining how data is accessed, understood, and acted upon across the enterprise.This session tells the story behind that shift, beginning with a bold pilot in Catering that demonstrated the power of generative, natural-language insights and sparked immediate executive engagement from day one. We’ll explore Etihad’s journey from complex, siloed reporting to an AI-first, self-service experience powered by Databricks Genie, turning data into an always-on business co-pilot.Attendees will learn how this transformation accelerated speed-to-insight, reduced technical debt, and scaled rapidly across diverse business units. We’ll conclude with a demo of our current Genie implementation and a forward look at Etihad’s evolving roadmap in the Databricks ecosystem, offering practical lessons for anyone.

Head of Enterprise Data Management

Evan Digney

Ketan Patel

Grounding Legacy BI: Etihad Airways’ Transition to AI-Powered Self-Service with Genie

At Nubank, we successfully migrated from BigQuery to Databricks SQL over 12 months, addressing the needs of our large-scale data environment with 17k users, 40MM monthly queries, and operations globally serving 100MM+ customers. Main goals: improve cost efficiency; enhance scalability; and maintain business continuity during explosive growth.Key aspects:<ul>	<li>Zero-downtime throughout the 12-month migration period</li>	<li>Successfully scaling platform to handle 100% query growth (20MM to 40MM monthly queries)</li>	<li>Comprehensive workload migration supporting 17k users globally</li>	<li>+40% annual cost savings compared to BQ</li>	<li>Leverage of UC for centralized data governance in highly regulated FSI industry</li>	<li>This migration significantly improved our cost structure, enhanced our ability to support business growth, and provided a scalable foundation for serving Latam's largest fintech, ultimately demonstrating that platform transformation and business velocity can succeed simultaneously in highly regulated environments.</li></ul>

Senior Machine Learning Engineer

Vinicius Pessoa Duarte

How Nubank Migrated from BigQuery to Databricks SQL Reducing Costs by 40% and Doubling Queries

This hands-on course equips learners with the skills to design, implement, and manage catalog structures and large-scale data organization using Unity Catalog in Databricks. Participants will learn to configure secure and scalable storage integrations, as well as implement access controls and isolation strategies for effective data governance.

Implementing Data Governance with Unity Catalog

This hands-on course teaches Databricks application developers how to use Lakebase as the operational database for intelligent, data-driven applications on Databricks. Learners explore how Lakebase enables applications powered by analytics, BI, and AI. Through guided exercises, the course covers Lakebase core concepts and architecture, provisioning and managing database instances, connecting from Databricks SQL, notebooks, and external clients, and applying best practices for connection management, permissions, and environment configuration. By the end of the course, developers will be able to confidently integrate Lakebase into production-ready data and AI applications, safely manage database resources, and unlock unified analytics and AI on live operational data. Note: Hands-on training courses will be updated to reflect the newest product and feature announcements from Data + AI Summit in June 2026.

Integrating Lakebase Databases into Intelligent Applications

We run a data platform at Thales IFE that tracks how millions of passengers interact with in-flight entertainment at 35,000 feet. While the data was widely available, business teams still depended on analysts to answer questions or build yet another dashboard.This session shows how we made an AI assistant the primary interface for business and operational insight using Databricks Apps and how it was integrated with existing business systems and portals. We walk through a real scenario: a business user opens a Databricks App, interacts with the AI agent, and explores passenger usage & system performance data. The same agent can also be embedded into existing business applications, bringing consistent data access directly into daily workflows.The talk focuses on outcomes and lessons learned: what Databricks simplified, where challenges appeared, and what it takes to move AI agents from experiment to production for our business users.

Pr Data Scientist

Simon Prevoteaux

Let Your Agent Pilot Your Fleet: Business Insights From 35,000 Feet

This hands-on course is designed for data professionals who want to explore the data warehousing capabilities of Databricks. Assuming no prior knowledge of Databricks, it provides an introduction to leveraging Databricks as a modern cloud-based data warehousing solution. Learners will explore how use the Databricks Data Intelligence Platform to ingest, transform, govern, and analyze data efficiently. Learners will also explore Genie, an innovative Databricks feature that simplifies data exploration through natural language queries. By the end of this course, participants will be equipped with the foundational skills to implement and optimize a data warehouse using Databricks.

Modern Data Warehousing

P&C insurance data platform must deliver analytics rapidly while meeting stringent governance and regulatory requirements. Although Databricks is widely adopted as the analytics backbone, many P&C organizations continue to struggle with inconsistent deployments, manual promotions, and fragmented DevOps practices.This lightning talk demonstrates how a P&C organization implemented true end-to-end DevOps on Databricks using Asset Bundles, Databricks Apps, and GitHub Actions.Learn how a Git-driven, declarative deployment model uses Asset Bundles to package notebooks, workflows and DLT pipelines with environment-aware configuration, enabling consistent promotion across multiple environments, while GitHub Actions orchestrates CI/CD pipelines for validation, automated deployment and auditability.The session highlights how Databricks Apps are integrated into the same pipeline to operationalize analytics use cases as secure, production-ready data product governed by Unity Catalog.

Sr Consultant Data Engineer

Harshit Mishra

Pipeline to Production in Minutes: How a Leading P&C Insurer Achieved End-to-End DevOps on Databricks

Modern enterprise data and AI platforms rely on a suite of tools to ensure governance, standardization, and smooth onboarding. However, maintaining developer portals, managing documentation, and handling operation requests often burdens platform teams with repetitive tasks.In this session, we’ll explore how AI Agents combined with (Custom) MCP tools on Databricks can automate these operational workflows, freeing up teams to focus on innovation and strategic goals. Through real-world examples and a step-by-step implementation guide, attendees will gain practical insights into deploying AI agents to streamline platform operations.Key Takeaways:<ul>	<li>Real-world use cases for AI agents in data platform ops like Unity Catalogs creation, Delta Shares management, Custom tool invocations, etc</li>	<li>How to integrate MCP tools with Databricks for seamless automation</li>	<li>Pitfalls to avoid and best practices for successful adoption (including authentication, GDPR compliance, and guardrails)</li></ul>

Vehicle Data Analytics & AI Lead

Yevheniy Eydelman

Lead AI Engineer

Sasho Nedelkoski

Reinventing Data Platform Operations and Governance: AI Agents as Your New DevOps Teammates

At S&P Global Energy, we transformed our geospatial analytics architecture to handle billions of records with query results in seconds—removing the need for separate geospatial databases and reducing costs using our Oil & Gas well production dataArchitecture Evolution:• Unified single-source-of-truth on Delta Lake without redundant geospatial systems• Databricks Spatial SQL with native spatial types enabling performant queries• Databricks SQL Serverless delivering on-demand H3 aggregationsInteractive Databricks App Demo for large well datasets.What's Next - AI Geospatial Agent (Work in Progress):• Natural language map interactions powered by Agent Bricks• Genie-driven Text-to-SQL for dynamic map layer creation• 50+ analytical tools like chart generation from spatial queriesAttendees will learn practical patterns for scaling geospatial workloads, leveraging Spatial SQL with native spatial types, H3 indexing, and see how AI agents are reshaping spatial analytics workflows.

Lead Geospatial Product Specialist

Michael Johns

Software Engineer

Hubert Boguski

Scaling Geospatial Analytics at S&P Global Energy: From Billions of Points to AI-Powered Map Agents with Databricks

In this hands-on course, you will explore core SQL programming and procedural logic skills within Databricks, focusing on how to build modular, maintainable, and production-ready analytics workflows. You’ll start by learning about key SQL constructs—such as Common Table Expressions, temporary views, and User Defined Functions—that make your SQL development cleaner and more reusable. The course then guides you through advanced SQL scripting techniques using compound statements, variables, control flow, conditionals, loops, and robust error handling to create structured and flexible SQL processes. Finally, you’ll discover how to orchestrate and modernize complex workflows with Lakeflow Jobs and migration strategies, transforming legacy tasks into modular, automated pipelines that leverage the full capabilities of the Databricks platform. Note: Hands-on training courses will be updated to reflect the newest product and feature announcements from Data + AI Summit in June 2026.

SQL Programming and Procedural Logic

Client demand is shifting: they don't want our data; they want it in their platform. We solved the "Vendor Lock-in" crisis by building an Open Data Passport. This session deconstructs how we leveraged Delta Sharing and Apache Iceberg UniForm to deliver high-scale healthcare products to clients without moving a single byte of raw data. We’ll share the engineering "scars" of managing cross-cloud permissions and how we turned an interoperability headache into a competitive product advantage for our global ecosystem. Demo: A live cross-platform "handshake" between a Databricks source and a non-Databricks consumer with zero ETL. Takeaways: Architecting zero-copy delivery via Delta Sharing. Managing multi-platform governance. Building vendor-agnostic data products.

Director, Product Management

Ravi Purohit

Director of Engineering

Vinaya Thimmappa

The Open Handshake: Scaling Multi-Cloud Delivery via Delta Sharing and Iceberg

As decision-making at Virgin Atlantic sped up, our BI stack creaked. Leaders wanted answers in hours and questions increasingly cut across commercial, operational, and customer data. Like many teams, we turned to Gen BI to close that gap. Our first attempt didn’t go well. Answers arrived faster, but confidence dropped. Without shared metric definitions, visible lineage, or governance designed in from the start, ambiguity scaled faster than trust. “Is this right?” became the default question again.In this session, we share how we rebuilt Gen BI on trusted foundations using Databricks. We’ll show how Unity Catalog and UC Metrics Views anchor AI answers to signed-off metrics, how Databricks Genie enables users to interrogate and explain results, and why explainability became part of the user experience.Attendees will leave with practical lessons for moving to decision-grade Gen BI, drawn from real production use cases and delivered as a visual, story-led case study.

PREVIEW THE SESSIONS

Agenda at a Glance

SESSION PREVIEWS