This presentation explores how Databricks' Data Intelligence Platform supports the development and deployment of responsible AI in credit decisioning, ensuring fairness, transparency and regulatory compliance. Key areas include bias and fairness monitoring using Lakehouse Monitoring to track demographic metrics and automated alerts for fairness thresholds. Transparency and explainability are enhanced through the Mosaic AI Agent Framework, SHAP values and LIME for feature importance auditing. Regulatory alignment is achieved via Unity Catalog for data lineage and AIBI dashboards for compliance monitoring. Additionally, LLM reliability and security are ensured through AI guardrails and synthetic datasets to validate model outputs and prevent discriminatory patterns. The platform integrates real-time SME and user feedback via Databricks Apps and AI/BI Genie Space.

Unlock the full potential of Delta Lake, the open-source storage framework for Apache Spark, with this session focused on its latest and most impactful features. Discover how capabilities like Time Travel, Column Mapping, Deletion Vectors, Liquid Clustering, UniForm interoperability, and Change Data Feed (CDF) can transform your data architecture. Learn not just what these features do, but when and how to use them to maximize performance, simplify data management, and enable advanced analytics across your lakehouse environment.

Redox & Databricks direct integration can streamline your interoperability workflows from responding in record time to preauthorization requests to letting attending physicians know about a change in risk for sepsis and readmission in near real time from ADTs. Data engineers will learn how to create fully-streaming ETL pipelines for ingesting, parsing and acting on insights from Redox FHIR bundles delivered directly to Unity Catalog volumes. Once available in the Lakehouse, AI/BI Dashboards and Agentic Frameworks help write FHIR messages back to Redox for direct push down to EMR systems. Parsing FHIR bundle resources has never been easier with SQL combined with the new VARIANT data type in Delta and streaming table creation against Serverless DBSQL Warehouses. We'll also use Databricks accelerators dbignite and redoxwrite for writing and posting FHIR bundles back to Redox integrated EMRs and we'll extend AI/BI with Unity Catalog SQL UDFs and the Redox API for use in Genie.

In the competitive gaming industry, understanding player behavior is key to delivering engaging experiences. Supercell, creators of Clash of Clans and Brawl Stars, faced challenges with fragmented data and limited visibility into user journeys. To address this, they partnered with Snowplow and Databricks to build a scalable, privacy-compliant data platform for real-time insights. By leveraging Snowplow’s behavioral data collection and Databricks’ Lakehouse architecture, Supercell achieved: Cross-platform data unification: A unified view of player actions across web, mobile and in-game Real-time analytics: Streaming event data into Delta Lake for dynamic game balancing and engagement Scalable infrastructure: Supporting terabytes of data during launches and live events AI & ML use cases: Churn prediction and personalized in-game recommendations This session explores Supercell’s data journey and AI-driven player engagement strategies.

ClickHouse is a C++ based, column-oriented database built for real-time analytics. While it has its own internal storage format, the rise of open lakehouse architectures has created a growing need for seamless interoperability. In response, we have developed integrations with your favorite lakehouse ecosystem to enhance compatibility, performance and governance. From integrating with Unity Catalog to embedding the Delta Kernel into ClickHouse, this session will explore the key design considerations behind these integrations, their benefits to the community, the lessons learned and future opportunities for improved compatibility and seamless integration.

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 Introduction & Business Context Brief Overview of Bosch’s Supply Chain Challenges and the Need for a Consolidated Data Platform. Strategic Importance of Data-Driven Decision-Making in a Global Supply Chain Environment. Databricks as the Core Data Platform Integrating dbt for Transformation Leveraging LLM Models for Enhanced Insights

The lakehouse is built for storage flexibility, but what about compute? In this session, we’ll explore how Unity Catalog enables you to connect and govern multiple compute engines across your data ecosystem. With open APIs and support for the Iceberg REST Catalog, UC lets you extend access to engines like Trino, DuckDB, and Flink while maintaining centralized security, lineage, and interoperability. We will show how you can get started today working with engines like Apache Spark and Starburst to read and write to UC managed tables with some exciting demos. Learn how to bring flexibility to your compute layer—without compromising control.

Most organizations run complex cloud data architectures that silo applications, users and data. Join this interactive hands-on workshop to learn how Databricks SQL allows you to operate a multi-cloud lakehouse architecture that delivers data warehouse performance at data lake economics — with up to 12x better price/performance than traditional cloud data warehouses. Here’s what we’ll cover: How Databricks SQL fits in the Data Intelligence Platform, enabling you to operate a multicloud lakehouse architecture that delivers data warehouse performance at data lake economics How to manage and monitor compute resources, data access and users across your lakehouse infrastructure How to query directly on your data lake using your tools of choice or the built-in SQL editor and visualizations How to use AI to increase productivity when querying, completing code or building dashboards Ask your questions during this hands-on lab, and the Databricks experts will guide you.

HP Print's data platform team took on a migration from a monolithic, shared resource of AWS Redshift, to a modular and scalable data ecosystem on Databricks lakehouse.​ The result was 30–40% cost savings, scalable and isolated resources for different data consumers and ETL workloads, and performance optimization for a variety of query types.​ Through this migration, there were technical challenges and learnings relating to the ETL migrations with DBT, new Databricks features like Liquid Clustering, predictive optimization, Photon, SQL serverless warehouses, managing multiple teams on Unity Catalog, and others.​ This presentation dives into both the business and technical sides of this migration. Come along as we share our key takeaways from this journey.​

Analytical and operational use cases are starting to converge, and AI-assisted applications are accelerating the trend. Most applications require a transactional, OLTP database to power data. Hear from a Databricks expert on the latest developments and our strategy for operational data integrated into the lakehouse.

Databricks’ Serverless compute streamlines infrastructure setup and management, delivering unparalleled performance and cost optimization for Data and BI workflows. In this presentation, we will explore how Nationwide is leveraging Databricks’ serverless technology and unified governance through Unity Catalog to build scalable, world-class BI solutions. Key features like AI/BI Dashboards, Genie, Materialized Views, Lakehouse Federation and Lakehouse Apps, all powered by serverless, have empowered business teams to deliver faster, scalable and smarter insights. We will show how Databricks’ serverless technology is enabling Nationwide to unlock new levels of efficiency and business impact, and how other organizations can adopt serverless technology to realize similar benefits.

“Once an idea has taken hold of the brain it's almost impossible to eradicate. An idea that is fully formed — fully understood — that sticks, right in there somewhere.” The Data Scientists and Engineers at 84.51˚ utilize the Databricks Lakehouse for a wide array of tasks, including data exploration, analysis, machine learning operations, orchestration, automated deployments and collaboration. In this talk, 84.51˚’s Data Science Learning Lead, Michael Carrico, will share their approach to upskilling a diverse workforce to support the company’s strategic initiatives. This approach includes creating tailored learning experiences for a variety of personas using content curated in partnership with Databricks’ educational offerings. Then he will demonstrate how he puts his 11 years of data science and engineering experience to work by using the Databricks Lakehouse not just as a subject, but also as a tool to create impactful training experiences and a learning culture at 84.51˚.

DoorDash Data organization actively adopts LakeHouse paradigm. This presentation describes the methodology which allows to migrate the classic Data Warehouse and Data Lake platforms to unified LakeHouse solution.The objective of this effort include Elimination of excessive data movement.Seamless integration and consolidation of the query engine layers, including Snowflake, Databricks, EMR and Trino.Query performance optimization.Abstracting away complexity of underlying storage layers and table formatsStrategic and justified decision on the Unified Metadata catalog used across varios compute platforms

Multi-statement transactions bring the atomicity and reliability of traditional databases to modern data warehousing on the lakehouse. In this session, we’ll explore real-world patterns enabled by multi-statement transactions — including multi-table updates, deduplication pipelines and audit logging — and show how Databricks ensures atomicity and consistency across complex workflows. We’ll also dive into demos and share tips to getting started and migrations with this feature in Databricks SQL.

AI agent systems hold immense promise for automating complex tasks and driving intelligent decision‑making, but only when they are engineered to be both resilient and transparent. In this session we will explore how Dataiku’s LLM Mesh pairs with Databricks Mosaic AI to streamline the entire lifecycle: ingesting and preparing data in the Lakehouse, prompt engineering LLMs hosted on Mosaic AI Model Serving Endpoints, visually orchestrating multi‑step chains, and monitoring them in real time. We’ll walk through a live demo of a Dataiku flow that connects to a Databricks hosted model, adds automated validation, lineage, and human‑in‑the‑loop review, then exposes the agent via Dataiku's Agent Connect interface. You’ll leave with actionable patterns for setting guardrails, logging decisions, and surfacing explanations—so your organization can deploy trustworthy domain‑specific agents faster & safer.

SAP is the world's 3rd-largest publicly traded software company by revenue, and recently launched the joint SAP Databricks "Business Data Cloud". See how it all works from a practitioner's perspective, including reference architecture, demo, and example customers. See firsthand how the powerful suite of SAP applications benefits from a joint Databricks solution - with data being more easily governed, discovered, shared, and used for AI/ML..

In an era of skyrocketing content volumes, companies are sitting on huge libraries — of video, images and audio — just waiting to be leveraged to power targeted advertising and recommendations, as well as reinforce brand safety. Coactive AI will show how fast and accurate AI-driven metadata enrichment, combined with Databricks Unity Catalog and lakehouse, is accelerating and optimizing media workflows. Learn how leading brands are using content metadata to: Unlock new revenue through contextual advertising Drive personalization at scale Enhance brand safety with detailed, scene-level analysis Build unified taxonomies that fuel cross-functional insights Transform content from a static asset into a dynamic engine for growth, engagement and compliance.

This session will explore Databricks Unity Catalog (UC) implementation by P&G to enhance data governance, reduce data redundancy and improve the developer experience through the enablement of a Lakehouse architecture. The presentation will cover: The distinction between data treated as a product and standard application data, highlighting how UC's structure maximizes the value of data in P&G's data lake. Real-life examples from two years of using Unity Catalog, demonstrating benefits such as improved governance, reduced waste and enhanced data discovery. Challenges related to disaster recovery and external data access, along with our collaboration with Databricks to address these issues. Sharing our experience can provide valuable insights for organizations planning to adopt Unity Catalog on an enterprise scale.

This session will explore how Adobe uses a sophisticated data security architecture built on the Databricks Data Intelligence Platform, along with the Open Cybersecurity Schema Framework (OCSF), to enable scalable, real-time threat detection across more than 10 PB of security data. We’ll compare different approaches to OCSF implementation and demonstrate how Adobe processes massive security datasets efficiently — reducing query times by 18%, maintaining 99.4% SLA compliance, and supporting 286 security users across 17 teams with over 4,500 daily queries. By using Databricks' Platform for serverless compute, scalable architecture, and LLM-powered recommendations, Adobe has significantly improved processing speed and efficiency, resulting in substantial cost savings. We’ll also highlight how OCSF enables advanced cross-tool analytics and automation, streamlining investigations. Finally, we’ll introduce Databricks’ new open-source OCSF toolkit for scalable security data normalization and invite the community to contribute.

Lakeflow Jobs is the production-ready fully managed orchestrator for the entire Lakehouse with 99.95% uptime. Join us for a dive into how you can orchestrate your enterprise data operations, from triggering your jobs only when your data is ready to advanced control flow with conditionals, looping and job modularity — with demos! Attendees will gain practical insights into optimizing their data operations by orchestrating with Lakeflow Jobs: New task types: Publish AI/BI Dashboards, push to Power BI or ingest with Lakeflow Connect Advanced execution control: Reference SQL Task outputs, run partial DAGs and perform targeted backfills Repair runs: Re-run failed pipelines with surgical precision using task-level repair Control flow upgrades: Native for-each loops and conditional logic make DAGs more dynamic + expressive Smarter triggers: Kick off jobs based on file arrival or Delta table changes, enabling responsive workflows Code-first approach to pipeline orchestration