No Code ETL with Lakeflow Designer

No-Code ETL with Lakeflow Designer shows how to build a complete medallion ETL workflow, from bronze to silver to gold, visually and without writing pipeline code. You will use Databricks Lakeflow Designer and its built-in AI authoring assistant, Genie Code.

In Lakeflow Designer, you build workflows on a visual canvas using operators. Operators are the building blocks of a data pipeline, with each operator performing an action such as adding a source, filtering or transforming data, aggregating data, joining datasets, or creating an output table.

Throughout the course, you will build workflows in two ways:

• Add and configure operators using the point-and-click interface.

• Describe transformations in natural language and use Genie Code to generate operators and workflows.

Using a realistic meeting and session dataset that includes sessions, participants, and feedback, the course introduces the medallion architecture in Unity Catalog and shows how to add bronze sources to the canvas, clean and reshape data into silver tables, and aggregate and join data into analytics-ready gold tables.

You will also learn how to schedule the workflow and deliver insights to business users through an AI/BI dashboard, a Genie Agent (Space), and Genie One. An optional bonus topic extends the workflow with the AI Function operator to derive sentiment from free-form feedback.

By the end of the course, you will understand how raw data moves through a governed and scheduled medallion architecture to produce analytics-ready gold tables and consumer-ready insights using a visual, AI-assisted experience on the Databricks Lakehouse.

Skill Level

Professional

Duration

Prerequisites

• Explain what Lakeflow Designer is and navigate its visual canvas and operators.

• Describe the different ways to add data sources to the canvas.

• Describe how to use Lakeflow Designer operators to prepare and transform data into analytics-ready tables.

• Explain how Genie Code builds workflows from natural-language prompts and why it is important to review what it generates.

• Describe how to schedule a Lakeflow Designer workflow and where its other capabilities fit.

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By the end of this course, you will be equipped to automate Databricks project deployments with Declarative Automation Bundles, improving efficiency through DevOps practices.

Note:

1. Databricks Academy is transitioning from video lectures to a more streamlined PDF format with slides and notes for all self-paced courses. Please note that demo videos will still be available in their original format. We would love to hear your thoughts on this change, so please share your feedback through the course survey at the end. Thank you for being a part of our learning community!

2. This course is the fourth in the 'Advanced Data Engineering with Databricks' series.

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Note: For SCORM lecture files, please ensure that you close the SCORM window after completing the content. Do not click the ‘Next Lesson’ button, as doing so may prevent the SCORM module from being marked as complete.

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