The Databricks EMEA Learning Festival

Models are a commodity, the agent around them is your product. In this practitioner-led session, we unpack the most impactful announcements from DAIS 2026 — Agent Bricks, Multi-Agent Supervisor with MCP, Unity AI Gateway, and Omnigent — and show how they combine into a governed, production-grade GenAI agent.

A live demo walks through the exact stack you'll build in the hands-on training: AI Search, agent-as-code RAG, MLflow tracing & evaluation, Unity Catalog registration, and deployment with the Review App. 

You'll leave with a clear blueprint: start from the question (not the model), trace and evaluate from day one, and govern early so your agent is production-ready, not just demo-ready.

3.5 hours with Labs | Intermediate (L200)

This course offers a practical introduction to the Mosaic AI platform, focusing on its key components and features for building and deploying generative AI systems. Participants will learn how Databricks facilitates the development of scalable generative AI solutions and explore Mosaic AI tools such as Vector Search, the Agent Framework, and MLflow's generative AI capabilities for model tracking and logging. This course includes hands-on experience in constructing and evaluating Retrieval-Augmented Generation (RAG) pipelines, deploying generative AI agents, and leveraging evaluation frameworks to optimise performance. By the end of the course, learners will be equipped with the skills to design, deploy, and monitor common generative AI applications using Mosaic AI.

TBC Bank, one of the largest banking groups in the Caucasus, faced month-long analyses and 14-week credit risk model deployments across a fragmented on-premises estate.
The bank built a governed Lakehouse on Databricks and adopted Lakebase as the operational layer connecting trusted data with transactional applications. Their DevOps team deployed Lakebase in hours, using its Postgres compatibility to power reverse ETL, AI-driven risk scoring and AML investigation through Databricks Apps.

The results: analyses in minutes, credit risk model deployment in two days, including model risk validation, and more than 600 users querying governed data through Genie. Hear from TBC Bank’s Head of Data Engineering about building a production-ready foundation for AI-driven banking.
 

3.5 hours with Labs | Intermediate (L200)

This course introduces Databricks Lakebase, a fully managed PostgreSQL service built into the Databricks Data + AI Platform that brings operational (OLTP) and analytical (OLAP) workloads closer together.

The course begins with a conceptual lecture that compares OLTP and OLAP systems, explaining their different performance characteristics, storage models, and typical use cases. You will also explore the challenges organisations face when maintaining separate transactional databases and analytical platforms, including data movement, latency, and architectural complexity.

You will then learn how Databricks Lakebase helps address these challenges by providing a PostgreSQL-compatible operational database that integrates directly with the Databricks Lakehouse, enabling operational applications and analytics to work together within a unified platform.

This is a Get Started course, so the focus is on understanding the core concepts and basic workflows for working with Lakebase. Building full production applications on top of Lakebase is outside the scope of this course.

AI has evolved from bespoke models and coarse data boundaries to dynamic, context-aware applications that can retrieve information and take action. This keynote follows that journey through the latest capabilities in Unity Catalog and Lakebase, including vector and text search, fine-grained governance, and governed operational state.

The story moves from analytical data to AI applications and back again: Reverse ETL and LTAP bring trusted silver and gold data into Lakebase-backed experiences, while agent actions become signals in the lakehouse. Together, Unity Catalog and Lakebase point toward AI applications that are trusted, connected, and ready for production.

3.5 hours with Labs | Intermediate (L200)

In this course, you will explore Unity Catalog and fine-grained access controls on Databricks with hands-on demos and a capstone lab. You will learn about table types, catalog and schema configuration, group-based access management, and access control migration strategies. The course includes demos on applying Fine-Grained Access Controls with Row Level Security and Column Masking, Attribute based access control, combining controls, migrating controls, and a lab for comprehensive governance implementation.

TBC Bank, one of the largest banking groups in the Caucasus, faced month-long analyses and 14-week credit risk model deployments across a fragmented on-premises estate.
The bank built a governed Lakehouse on Databricks and adopted Lakebase as the operational layer connecting trusted data with transactional applications. Their DevOps team deployed Lakebase in hours, using its Postgres compatibility to power reverse ETL, AI-driven risk scoring and AML investigation through Databricks Apps.

The results: analyses in minutes, credit risk model deployment in two days, including model risk validation, and more than 600 users querying governed data through Genie. Hear from TBC Bank’s Head of Data Engineering about building a production-ready foundation for AI-driven banking.
 

3.5 hours with Labs | Intermediate (L200)

This course introduces Databricks Lakebase, a fully managed PostgreSQL service built into the Databricks Data + AI Platform that brings operational (OLTP) and analytical (OLAP) workloads closer together.

The course begins with a conceptual lecture that compares OLTP and OLAP systems, explaining their different performance characteristics, storage models, and typical use cases. You will also explore the challenges organisations face when maintaining separate transactional databases and analytical platforms, including data movement, latency, and architectural complexity.

You will then learn how Databricks Lakebase helps address these challenges by providing a PostgreSQL-compatible operational database that integrates directly with the Databricks Lakehouse, enabling operational applications and analytics to work together within a unified platform.

This is a Get Started course, so the focus is on understanding the core concepts and basic workflows for working with Lakebase. Building full production applications on top of Lakebase is outside the scope of this course.

Writing the query is no longer the hard part. Genie will draft it for you, and your colleagues will start asking questions without you. What is scarce now sits underneath the answer: the definitions, the ontology, the curation and the governance that decide whether the answer is right.

This session opens the Analytics track. We start with what was announced at Data + AI Summit - Genie One, Genie Agents, Genie Ontology, Genie Code and AI/BI Dashboards - and what each one changes for an analyst. Then we go under the hood, where you will watch how just a few questions can accelerate what would be weeks of chasing cross-functional teams for an action plan.

You will leave with habits you can start in the Day 1 lab, and a clear reason to finish the Day 2 training on building reliable Genie Agents.

3.5 hours with Labs | Intermediate (L200)

This course introduces you to the Databricks Data + AI Platform, covering essential skills for data analytics and warehousing. Learn to navigate the workspace, work with data objects, run SQL queries, use Delta Lake features, and create dashboards.

Mercedes-Benz Korea needed every business user — from Sales VP to CFO — to ask questions in plain language and get consistent, trusted answers. But their KPI semantics were scattered across BI reports, so the same question could return different results depending on which report handled it.

The team built an AI-ready semantic layer on Databricks and put Genie at the centre, so every persona gets a governed "Talk to Data" experience. Their key principle: conversational AI doesn't automatically mean trusted AI; trust has to be engineered underneath it.

In this session, we'll walk you through the 5-phase playbook Mercedes-Benz Korea now scales to markets worldwide.

3.5 hours with Labs | Intermediate (L200)

This course teaches you how to design, build, and maintain a Databricks Genie Space, a natural language interface that enables business users to ask questions about governed data and receive SQL-backed answers without writing code.

You will learn how Genie fits into the Databricks AI/BI product family and how it translates natural language into reliable SQL queries. The course focuses on what it takes to create a Genie Space that delivers accurate, consistent, and trustworthy results.

You will follow a complete end-to-end workflow, from understanding source data and defining benchmarks to configuring and refining a Genie Space using the full set of Knowledge Store curation tools. These include metadata, synonyms, prompt matching, SQL logic, example queries, and text instructions.

You will also learn how to share Genie Spaces with business users through Databricks One, understand how Unity Catalog governance is automatically enforced, and use monitoring and user feedback to continuously improve quality over time.

By the end of the course, you will be able to create and manage a production-ready Genie Space that delivers governed, self-service conversational analytics at scale.