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