Get Started with Lakehouse Architecture on Databricks

In this course, you will explore the Databricks Data Intelligence Platform from the perspective of platform architecture, specifically related to the platform foundation in lakehouse architecture. You will learn about the scope, vision, and capabilities of a lakehouse-based platform, examine how Databricks integrates with major cloud providers, and discover the key features of a successful lakehouse implementation through the well-architected lakehouse framework. The course also covers essential architectural principles, best practices, and data architecture strategies to help accelerate your organization’s data and AI initiatives using Databricks.

Languages Available: English | 日本語 | Português BR | 한국어

Skill Level

Onboarding

Duration

Prerequisites

The content was developed for participants with these skills/knowledge/abilities:

• Familiarity with traditional data management architectures, specifically the distinctions between data warehouses and data lakes.

• Beginner familiarity with cloud computing concepts such as object storage (S3, ADLS, GCS) and cloud provider environments (AWS, Azure, GCP).

• Intermediate experience with SQL concepts, including ANSI SQL commands, views, and database management functionality.

• A basic understanding of data engineering principles and topics such as data collection, extraction, ingestion, and transformation.

• Basic understanding of data governance principles, including access control, data lineage, and auditing.

• Basic knowledge of artificial intelligence and machine learning workflows, including Generative AI concepts and MLOps.

• Understanding of core data team personas and their responsibilities, such as Data Engineers, Data Scientists, and Business Analysts.

• Familiarity with open data standards and file formats, such as Apache Parquet, Delta Lake, and Apache Iceberg.

• Prior experience in data platform solutions architecture or similar areas of study.

Outline

Databricks Overview
Databricks Infrastructure
Databricks Data Intelligence Platform
Unity Catalog Overview
Databricks Workspace Walkthrough

Exploring Lakehouse Architecture
The Scope of Lakehouse Architecture
The Modern Data and AI Platform
Exploring the Guiding Principles of the Lakehouse

Building a Well-Architected Lakehouse
Integrating Databricks with Cloud Provider Environments
A Well-Architected Lakehouse Framework
Data Architecture Strategies

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Upcoming Public Classes

Data Engineer

Get Started with Databricks for Data Engineering

This course introduces the foundational skills needed to perform a basic data engineering workflow on the Databricks Data Intelligence Platform. You will explore the workspace, work with Unity Catalog, and learn the day-to-day building blocks a data engineer uses on Databricks.

The course follows a hands-on path. You start by getting oriented in the workspace, then work through paired Demo and Lab notebooks for each topic. Demos walk you through a concept with the instructor or a guided notebook.

The content covers:

• The Databricks Data Intelligence Platform and how the Databricks workspace, Unity Catalog, and notebooks fit together.

• Creating and managing Delta Lake tables.

• Modifying data with INSERT, UPDATE, and DELETE.

• Exploring version history and time travel on Delta tables.

• Ingesting data with Lakeflow Connect options: CTAS, the Upload UI, and COPY INTO.

• Building a Medallion Architecture pipeline that transforms data through Bronze, Silver, and Gold layers.

• Automating pipelines with Lakeflow Jobs.

• (Bonus) Building a declarative pipeline with Spark Declarative Pipelines.

Free

Onboarding

Data Analyst

AI/BI for Data Analysts

This course teaches data analysts how to design, build, publish, and operate AI/BI Dashboards in Databricks. AI/BI Dashboards combine governed Unity Catalog data with interactive visualizations, filters, and Genie integration so business users can explore answers without writing code.

The course follows a single end-to-end build. You start with source tables in Unity Catalog and finish with a published, monitored multi-page dashboard. Along the way you learn how dashboards fit into the broader Databricks AI/BI product family and where Genie, datasets, visualizations, and filters each fit in the workflow.

The content covers:

• AI/BI Dashboard fundamentals and how they relate to Genie and the rest of the Databricks platform.

• Exploring source data in Unity Catalog and designing reusable dashboard datasets with SQL.

• Authoring visualizations (KPIs, trends, breakdowns) and laying out a clean multi-page dashboard.

• Using Genie Code to draft SQL, charts, and filters from natural language prompts.

• Adding filters to make dashboards interactive and responsive to viewer questions.

• Publishing, sharing, and managing permissions so the right people can view and edit the dashboard.

• Running the dashboard in production with scheduled refresh, caching, and usage monitoring.

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.

Free

Associate

Get Started with AI Agents on Databricks - Mandarin Chinese

本课程是 AI Agent 的入门介绍，内容涵盖 AI Agent 在现代 AI 应用中的作用，以及如何在 Databricks 平台上构建 AI Agent 应用。您将学习 AI Agent 的基本原理、其与传统 AI 系统的区别，并深入了解其关键组成部分。通过交互式演示和实操实验，您将了解如何在 Databricks 上使用 Mosaic AI 与 Agent Bricks 构建、部署并评估 AI Agent。完成本课程后，您将掌握如何使用 Databricks Data Intelligence Platform 创建并部署 AI Agent。

可用语言： English | 日本語 | 한국어

Free

Onboarding