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.

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.

Self-Paced

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

Customer registration Partner registration

See all our registration options

Registration options

Databricks has a delivery method for wherever you are on your learning journey

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

Blended Learning

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

Purchase now

Skills@Scale

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

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

Catalog Management and Data Organization

In this course, you will learn how to design, implement, and govern catalog structures and large-scale data organization on the Databricks Data Intelligence Platform. It offers a comprehensive view of Unity Catalog as the centralized governance layer for an enterprise lakehouse. Divided into five modules, it begins by placing Unity Catalog within the cloud deployment model — covering the Account Console, metastore creation, and the administrator role hierarchy. You will then translate organizational topology (business units, regions, and dev/QA/prod environments) into a scalable catalog and schema design using naming conventions, ownership patterns, and MANAGE delegation. The course then covers secure storage integration with storage credentials, external locations, the managed-storage hierarchy, managed versus external tables, and UC Volumes for non-tabular data. Next, you will apply access patterns and isolation strategies — the three-level GRANT chain, workspace-catalog binding, and schema-level Attribute-Based Access Control (ABAC) policies — to enforce fine-grained data protection at scale. Finally, the course closes with best practices for catalog design, automation, least-privilege permissions, and group-based access management.

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

Data Architect

Data Modeling Strategies

This course walks practitioners through the full spectrum of data modelling approaches on the Databricks Data Intelligence Platform - from classical data warehouse techniques (Inmon, Kimball, Data Vault 2.0), through Feature Store-driven ML use cases, to productising data via Data Products on Unity Catalog.

Each modelling approach is introduced with a lecture, then reinforced with a hands-on demo against a shared dataset (TPC-H samples). The course finishes with a comprehensive end-to-end lab that exercises ERM, dimensional modelling, Data Vault 2.0, and the Feature Store in a single integrated workflow.

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

Free

Associate