Data Management in Databricks

This chapter explores table management in Databricks, focusing on managed vs. unmanaged tables and how they handle storage and lifecycle. You'll learn to create and refresh persistent views and dive into Delta Lake features like ACID transactions, schema enforcement, and time travel for reliable data management. You will also gain a deeper look into the mechanics of data organization and access within Databricks.

This chapter delves into creating and managing views and temp views in Databricks. You'll explore how persistent views save query logic for reuse across sessions, while temp views are suited for quick, session-specific tasks. The discussion also highlights practical scenarios where each type can enhance efficiency and streamline data handling.

In the final chapter, you’ll explore how to use Data Explorer to preview, analyze, and secure datasets. The content covers table ownership, responsibilities, and governance best practices. It also dives into managing access rights and securely handling Personally Identifiable Information (PII) with compliance-focused strategies and practical exercises.