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.

Migrating your legacy Oracle data warehouse to the Databricks Data Intelligence Platform can accelerate your data modernization journey. In this session, learn the top strategies for completing this data migration. We will cover data type conversion, basic to complex code conversions, validation and reconciliation best practices. Discover the pros and cons of using CSV files to PySpark or using pipelines to Databricks tables. See before-and-after architectures of customers who have migrated, and learn about the benefits they realized.

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.

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.

Databricks SQL is the fastest-growing data warehouse on the market, with over 10k organizations thanks to its price performance and AI innovations. See the best practices and common architectural challenges of migrating your legacy DW, including reference architectures. Learn how to easily migrate per the recently acquired the Lakebridge migration tool, and through our partners.

Storage and processing costs of your legacy Teradata data warehouses impact your ability to deliver. Migrating your legacy Teradata data warehouse to the Databricks Data Intelligence Platform can accelerate your data modernization journey. In this session, learn the top strategies for completing this data migration. We will cover data type conversion, basic to complex code conversions, validation and reconciliation best practices. How to use Databricks natively hosted LLMs to assist with migration activities. See before-and-after architectures of customers who have migrated, and learn about the benefits they realized.

As enterprises continue their journey to the cloud, data warehouse and data management modernization is essential to optimize analytics and drive business outcomes. Minimizing modernization timelines is important for reducing risk and shortening time to value – and ensuring enterprise data is clean, curated and governed is imperative to enable analytics and AI initiatives. In this session, learn how Informatica's Intelligent Data Management Cloud (IDMC) empowers analytics and AI on Databricks by helping data teams: · Develop no-code/low-code data pipelines that ingest, transform and clean data at enterprise scale · Improve data quality and extend enterprise governance with Informatica Cloud Data Governance and Catalog (CDGC) and Unity Catalog · Accelerate pilot-to-production with Mosaic AI

The Delta Lake architecture promises to provide a single, highly functional, and high-scale copy of data that can be leveraged by a variety of tools to satisfy a broad range of use cases. To date, most use cases have focused on interactive data warehousing, ETL, model training, and streaming. Real-time access is generally delegated to costly and sometimes difficult-to-scale NoSQL, indexed storage, and domain-specific specialty solutions, which provide limited functionality compared to Spark on Delta Lake. In this session, we will explore the Delta data-skipping and optimization model and discuss how Capital One leveraged it along with Databricks photon and Spark Connect to implement a real-time web application backend. We’ll share how we built a highly-functional and performant security information and event management user experience (SIEM UX) that is cost effective.

This session shows you how to gain visibility into your Databricks SQL spend and ensure cost efficiency. Learn about the latest features to gain detailed insights into Databricks SQL expenses so you can easily monitor and control your costs. Find out how you can enable attribution to internal projects, understand the Total Cost of Ownership, set up proactive controls and find ways to continually optimize your spend.

Despite the proliferation of cloud data warehousing, BI tools, and AI, spreadsheets are still the most ubiquitous data tool. Business teams in finance, operations, sales, and marketing often need to analyze data in the cloud data warehouse but don't know SQL and don't want to learn BI tools. AI tools offer a new paradigm but still haven't broadly replaced the spreadsheet. With new AI tools and legacy BI tools providing business teams access to data inside Databricks, security and governance are put at risk. In this session, Row Zero CEO, Breck Fresen, will share examples and strategies data teams are using to support secure spreadsheet analysis at Fortune 500 companies and the future of spreadsheets in the world of AI. Breck is a former Principal Engineer from AWS S3 and was part of the team that wrote the S3 file system. He is an expert in storage, data infrastructure, cloud computing, and spreadsheets.

Join us to hear about how Epsilon Data Management migrated Epsilon’s unique, AI-powered marketing identity solution from multi-petabyte on-prem Hadoop and data warehouse systems to a unified Databricks Lakehouse platform. This transition enabled Epsilon to further scale its Decision Sciences solution and enable new cloud-based AI research capabilities on time and within budget, without being bottlenecked by the resource constraints of on-prem systems. Learn how Delta Lake, Unity Catalog, MLflow and LLM endpoints powered massive data volume, reduced data duplication, improved lineage visibility, accelerated Data Science and AI, and enabled new data to be immediately available for consumption by the entire Epsilon platform in a privacy-safe way. Using the Databricks platform as the base for AI and Data Science at global internet scale, Epsilon deploys marketing solutions across multiple cloud providers and multiple regions for many customers.

In this session, we will explore the Australian Red Cross Lifeblood's approach to synchronizing an Azure SQL Datavault 2.0 (DV2.0) implementation with Unity Catalog (UC) using Lakeflow Connect. Lifeblood's DV2.0 data warehouse, which includes raw vault (RV) and business vault (BV) tables, as well as information marts defined as views, required a multi-step process to achieve data/business logic sync with UC. This involved using Lakeflow Connect to ingest RV and BV data, followed by a custom process utilizing JDBC to ingest view definitions, and the automated/manual conversion of T-SQL to Databricks SQL views, with Lakehouse Monitoring for validation. In this talk, we will share our journey, the design decisions we made, and how the resulting solution now supports analytics workloads, analysts, and data scientists at Lifeblood.

Databricks Odyssey is JLL’s bespoke training program designed to upskill and prepare data professionals for a new world of data lakehouse. Based on the concepts of learn, practice and certify, participants earn points, moving through five levels by completing activities with business application of Databricks key features. Databricks Odyssey facilitates cloud data warehousing migration by providing best practice frameworks, ensuring efficient use of pay-per-compute platforms. JLL/T Insights and Data fosters a data culture through learning programs that develop in-house talent and create career pathways. Databricks Odyssey offers: JLL-specific hands-on learning Gamified 'level up' approach Practical, applicable skills Benefits include: Improved platform efficiency Enhanced data accuracy and client insights Ongoing professional development Potential cost savings through better utilization

This session is repeated. Databricks has a free, comprehensive solution for migrating legacy data warehouses from a wide range of source systems. See how we accelerate migrations from legacy data warehouses to Databricks SQL, achieving 50% faster migration than traditional methods. We'll cover the tool’s automated migration process: Discovery: Source system profiling Assessment: Legacy code analysis Conversion: Advanced code transpilation Reconciliation: Data validation This comprehensive approach increases the predictability of migration projects, allowing businesses to plan and execute migrations with greater confidence.

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

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.

This session is repeated. If you are brand new to Databricks SQL and want to get a lightning tour of this intelligent data warehouse, this session is for you. Learn about the architecture of Databricks SQL. Then show how simple, streamlined interfaces are making it easier for analysts, developers, admins and business users to get their jobs done and questions answered. We’ll show how easy it is to create a warehouse, get data, transform it and build queries and dashboards. By the end of the session, you’ll be able to build a Databricks SQL warehouse in 5 minutes.

This 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. Pre-requisites: Basic understanding of SQL and data querying concepts General knowledge of data warehousing concepts, including tables, schemas, and ETL/ELT processes is recommended Some experience with BI and/or data visualization tools is helpful but not required Labs: Yes

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