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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: Databricks Academy is transitioning to a notebook-based format for classroom sessions within the Databricks environment, discontinuing the use of slide decks for lectures. You can access the lecture notebooks in the Vocareum lab environment.

Skill Level
Associate
Duration
4h
Prerequisites

In this course, the content was developed for participants with these skills/knowledge/abilities:  

• Familiarity with the Databricks Data Intelligence Platform and basic workspace operations (creating clusters and SQL warehouses, running SQL and Python in notebooks, basic notebook navigation)

• Working knowledge of Unity Catalog fundamentals, including the three-level namespace (metastore → catalog → schema → table) and the core securable objects (tables, views, volumes, models)

• Hands-on experience with Unity Catalog–enabled workspaces, including creating and managing catalogs, schemas, and tables via SQL

• Solid understanding of identity and access management concepts (users, groups, service principals, authentication, authorization) and the difference between account-level and workspace-level administration

• Working knowledge of data governance principles — access control, data classification, compliance, and audit — and how they map to platform features

• Familiarity with cloud object storage concepts (S3, ADLS, or GCS), including IAM roles, bucket-level paths, and the role of storage credentials in cross-service access

• Introductory familiarity with Delta Lake (managed vs external tables, Delta lifecycle) and Lakeflow for orchestrating data workflows

• Practical experience with enterprise architecture in larger organizations — multi-business-unit topology, regional data residency, and dev/QA/prod environment separation

Outline

Unity Catalog's Role in Cloud Architecture

• Unity Catalog in the Cloud

• From Account to Workspace Enablement

• Demo: Account Console Walkthrough: Setting up/managing a metastore


Designing & Structuring Catalogs and Namespaces

• From Org Model to Catalog Topology

• Ownership, MANAGE, and Delegation

• Privilege Inheritance That Scales

• Workspace-Catalog Binding & Read-only

• Demo: Designing Catalogs and Schemas

• Lab: Enterprise Catalog Design for a Multinational Organization


Data Management and Storage

• From Metadata to Tables & Volumes

• Managed Locations and Storage Credentials

• External Locations and Foreign Catalogs

• Demo: Storage Credentials and External Locations

• Lab: Cloud Storage Integration and Data Management


Access Patterns, Isolation Strategies and FGAC in UC

• Isolation by Environment & Business Unit

• Privileges in Practice

• Fine-grained Access with RLS & Masking

• ABAC: What Good Looks Like

• Validating Outcomes & Auditing Signals

• Demo: Catalog Isolation, Access Patterns, and FGAC

• Lab: Enterprise Data Isolation and Access Governance


Best Practices
• Best Practices

Upcoming Public Classes

Date
Time
Your Local Time
Language
Price
Nov 18
09 AM - 01 PM (America/Los_Angeles)
-
English
$750.00
Nov 25
01 PM - 05 PM (Australia/Sydney)
-
English
$750.00
Dec 16
01 PM - 05 PM (Europe/Paris)
-
English
$750.00
Jan 27
09 AM - 01 PM (Asia/Singapore)
-
English
$750.00
Jan 27
01 PM - 05 PM (America/New_York)
-
English
$750.00

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Upcoming Public Classes

Databricks Performance Optimization - Mandarin Chinese

Databricks Performance Optimization 课程向数据工程师和分析师讲授如何在 Databricks Data Intelligence Platform 上诊断、衡量和修复性能瓶颈,以及如何将性能改进与成本关联起来。本课程遵循"先衡量、先利用平台"的工作流:让平台的自动优化功能承担繁重工作,通过 Query Profile 和系统表验证已应用的优化,再在必要时进行手动调优。

学员将以 Query Profile、Performance Insights 和查询历史记录系统表为基础,建立衡量性能的能力。在此基础上,他们将探索关键优化技术,包括数据布局与自调优托管表、liquid clustering、缓存与中间结果、shuffle、数据倾斜、溢出、行爆炸、驱动程序性能、Python UDF、serverless compute、Photon 以及成本归因。

在整个课程中,学员将针对合成零售数据中刻意设计的慢查询进行探索,并观察不同优化技术对性能的影响。他们将利用文件裁剪、任务执行时间、Photon 覆盖率和成本等依据来评估改进效果。两个基于场景的实验将强化从诊断到验证的完整优化工作流。

注意:Databricks Academy 正在将 Databricks 环境中的课堂教学转为基于 notebook 的形式,不再使用幻灯片进行授课。您可以在 Vocareum 实验环境中访问课程 notebook。

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

Paid
4h
Lab
instructor-led
Professional

Questions?

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