Databricks Foundational Workshops

Take a deep dive into building production-grade AI agent systems on Databricks. Using Agent Bricks, you'll construct a Knowledge Assistant grounded in real company data and extend it with an AI/BI Genie-powered structured data agent. Then orchestrate both into a multi-agent supervisor and iteratively improve response quality - all without writing a single line of code.

In 90 minutes, you'll walk away knowing how to combine unstructured and structured data, intelligently route queries across specialized agents and guide agent behaviour using natural language feedback.

You'll learn how to:

• Ground large language model (LLM) responses in living company knowledge bases and support ticket history using Vector Search

• Build a Knowledge Assistant agent using pre-built Vector Search indices with no setup required

• Query structured billing and customer data with Genie using plain natural language over SQL tables

• Register Genie as an agent and plug it into a multi-agent workflow alongside your Knowledge Assistant

• Orchestrate agents with a multi-agent supervisor that routes queries intelligently by data type

• Improve response quality through natural language feedback

Who should attend
If you're a data engineer, ML practitioner or technical architect who wants hands-on experience building multi-agent AI systems on the Databricks Data Intelligence Platform, this session is for you. No prior agent development experience required, and all lab environments are pre-configured.

Agenda

• 5 mins — Welcome

• 15 mins — Presentation: Multi-Agent Systems With Agent Bricks

• 1 hour — Hands-on Lab

  • Part 1: Build a Knowledge Assistant

  • Part 2: Expand with Genie for structured data

  • Part 3: Orchestrate with a multi-agent supervisor

  • Part 4: Improve quality with feedback

• 10 mins — Q&A 

   

This introductory, hands-on workshop is built for data engineers and data architects who want practical experience with modern data engineering on Databricks.

You’ll work directly in your own lab environment and learn how to design, build, and operate reliable data pipelines using real-world use cases. You’ll leave with hands-on experience using GitHub, ingesting data from multiple sources, and operationalizing pipelines end to end.

In this session, you will learn:

• Introduction to Lakeflow and the Data Intelligence Platform

• Getting started with Declarative Pipelines, Streaming Tables, and Materialized Views

• Working with a realistic data streaming use case

• Mastering Lakeflow Jobs with advanced control flow and triggers

• Generative AI for Data Engineers: Genie and Databricks Assistant

• Understanding data governance and lineage with Unity Catalog

• Benefits of Serverless Compute

Effective governance is essential to democratizing data and AI in the age of LLMs and generative AI. Enterprises struggle with fragmented governance across databases, warehouses, lakes, and diverse data formats, which complicates discovery, access permissions, data usage, audits, and sharing. Additionally, users often cannot access the right data from their preferred tools, leading to vendor lock-in. Managing ML models and AI tools for regulatory compliance adds another layer of complexity.

Databricks Data Intelligence Platform features Unity Catalog, a unified and open governance solution for data and AI. Unity Catalog enables organizations to seamlessly govern structured and unstructured data, machine learning models, notebooks, dashboards, and files across any cloud or platform. With wide interoperability across the Delta and Iceberg ecosystems, users can access data effortlessly from any tool of their choice, ensuring flexibility and eliminating vendor lock-in.

In this session, you will learn how to:

• Create a unified catalog for structured/unstructured data, ML models, and features, AI tools
• Utilize context-aware natural language search and discovery
• Implement access permissions and audit data access
• Access data and AI assets from any tool open APIs
• Use AI to monitor data and ML pipeline quality
• Share data effortlessly across clouds, regions, or platforms with open source Delta Sharing

Most organizations run complex cloud data architectures that silo applications, users and data. As a result, most analysis is performed on stale data and there’s no single source of truth of data for analytics - making it difficult to scale trusted insights across teams.

Join this interactive hands-on workshop to learn how Databricks Lakehouse enables a multicloud lakehouse architecture that delivers data warehouse performance at data lake economics — with up to 12x better price/performance than traditional cloud data warehouses. You’ll discover how data architects can unify infrastructure and governance, while data analysts and BI teams can work with the freshest, most complete data—and accelerate insights using Databricks’ native AI/BI capabilities along with their favorite BI tools.

Whether you’re building a scalable foundation or delivering insights to the business, this workshop is designed to help you modernize your analytics stack and make AI-powered BI a reality.

In this workshop, you’ll learn:

• How the Data Intelligence Platform and Databricks Lakehouse support a unified, multicloud lakehouse architecture
• How to manage and monitor compute, data access, and users across your lakehouse using Databricks Lakehouse
• How data teams and analysts can query directly on your data lake using the built-in SQL editor, AI/BI or their preferred BI tools.
• How to use AI-assisted features like AI-assisted code completion, automated dashboard creation and natural language querying via AI/BI Genie,to increase productivity and scale self-service analytics.

Most application stacks are fragmented. Transactions live in one system, analytics in another, and pipelines try to stitch everything together.

That architecture slows development. Teams often spend more time debugging data movement than building features.

This workshop introduces a different approach. You will work with Lakebase, a fully managed Postgres database on Databricks that sits alongside the lakehouse, allowing your applications, analytics, and AI to operate on the same data.

The session demonstrates how modern application teams can move faster when apps, analytics, and AI run on the same data. You will work with Lakebase to spin up a database, connect with Python, run queries, and power real application workflows, showing how the lakehouse can support production-ready applications.

What You'll Learn:

• Run your application and analytics on the same data
• Set up Lakebase on Databricks and start using it quickly
• Connect your application using SQL, Python, and standard tools
• Build features on live data instead of batch copies
• Push lakehouse data directly into your application
• Apply governance across both data and applications
• Get closer to something you could run in production