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

• Basic understanding of data concepts, including tables, rows, columns, and data types.

• Familiarity with basic data transformations, such as filtering, joining, and aggregating data.

• Basic familiarity with navigating the Databricks workspace.

• Foundational understanding of ETL processes and data pipelines.

Self-Paced

Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos

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Self-Paced

Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos

Instructor-Led

Public and private courses taught by expert instructors across half-day to two-day courses

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Self-paced and weekly instructor-led sessions for every style of learner to optimize course completion and knowledge retention. Go to Subscriptions Catalog tab to purchase

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Comprehensive training offering for large scale customers that includes learning elements for every style of learning. Inquire with your account executive for details

Upcoming Public Classes

Catalog Management and Data Organization - Mandarin Chinese

本课程将介绍如何在 Databricks Data and AI Platform 上使用 Unity Catalog 设计、实施和管理目录结构及大规模数据组织。课程共分五个模块：首先将 Unity Catalog 置于云平台部署模型的背景下，涵盖账户控制台、区域元存储以及实现其运作的管理员角色；随后，您将学习如何把组织拓扑结构（业务单元、地区及开发/QA/生产环境）转化为可扩展的目录和 schema 设计，并制定命名规范、所有权及 MANAGE 委派策略；接下来，课程将介绍安全存储集成，包括存储凭证、外部位置、托管存储层级、托管表与外部表、Volumes 以及 Lakehouse Federation；之后，您将应用访问模式和隔离策略，包括 workspace-目录绑定、权限继承、行过滤器、列掩码以及基于属性的访问控制（ABAC）策略，并通过审计信号验证结果；最后，课程以设计、管理和运营 Unity Catalog 资产的最佳实践作为总结。实操演示和实验在 serverless compute 上运行，并包含简短的 Genie Code 步骤。

注：SCORM講義ファイルの場合、コンテンツを完了したらSCORMウィンドウを閉じてください。「次のレッスン」ボタンはクリックしないでください。クリックすると、SCORMモジュールが完了として記録されない場合があります。

Paid & Subscription

Lab

Associate

Catalog Management and Data Organization

In this course, you will learn how to design, implement, and govern catalog structures and large-scale data organization with Unity Catalog on the Databricks Data and AI Platform. Divided into five modules, it begins by placing Unity Catalog within the cloud deployment model, covering the account console, regional metastores, and the administrator roles that enable it. You will then translate organizational topology (business units, regions, and dev/QA/prod environments) into a scalable catalog and schema design with naming conventions, ownership, and MANAGE delegation. Next, the course covers secure storage integration with storage credentials, external locations, the managed-storage hierarchy, managed versus external tables, volumes, and Lakehouse Federation. You will then apply access patterns and isolation strategies, including workspace-catalog binding, privilege inheritance, row filters, column masks, and attribute-based access control (ABAC) policies, and validate the outcomes with audit signals. Finally, the course closes with best practices for designing, governing, and operating a Unity Catalog estate. The hands-on demos and labs run on serverless compute and include short Genie Code steps.

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.