Peter Styliadis

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. Prerequisites - Familiarity with the Databricks Data Intelligence Platform, including workspaces, Apache Spark, Delta Lake, Medallion Architecture, Lakeflow Jobs, and Unity Catalog - Experience ingesting raw data into Delta tables using the read_files SQL function (CSV, JSON, TXT, Parquet) - Intermediate SQL proficiency, including intermediate-level queries and joins - Understanding of ETL concepts and batch/streaming workflows

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 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. Prerequisites General…

Lakeflow Designer is Databricks' AI-native, no-code experience for building data preparation and transformation workflows directly in the lakehouse. Built into the platform and governed by Unity Catalog, it allows analysts and business users to create reliable pipelines without leaving Databricks or managing separate tools.In this session, we will explore what Lakeflow Designer is, how it fits into the Databricks ecosystem, and how to get started using natural language with Genie Code to build workflows with little to no coding experience. We will cover data ingestion, built-in and custom operators, output options, and how visual workflows translate into production-ready code that can be versioned with Git, exported to notebooks, and added to larger enterprise pipelines.