1. Creating and managing tables
In this video, we will dive into more details about creating and managing tables and databases in Databricks.
2. The data library
We can actually think of this setup like a library: databases act as sections, tables as shelves, and views as bookmarks, making information easy to locate and use.
3. Create and manage
We'll begin by looking at how to create and manage databases and tables. In Databricks, a database is a logical container that groups related tables and views, providing a foundation for a well-organized data environment. This step is like organizing library sections for easy access. Creating a database is the first step in laying the foundation for an organized and scalable data management system.
Tables, the core storage units within each database, store your data in a structured format, making it accessible for querying and analysis.
4. Effective management
Beyond creation, effective management is essential. This includes querying tables, updating data as needed, and safely deleting tables or databases when they're no longer relevant.
Deleting is akin to clearing outdated books from a library, requiring careful steps to avoid accidental data loss. We'll explore best practices for safe deletion, such as backing up data before removal to protect against unintended loss.
5. Using the LOCATION keyword
By default, Databricks manages table storage locations, automatically placing each table in a designated storage space. This default setup simplifies management, similar to a library that allocates shelves to new books. However, Databricks also allows for customization with the LOCATION keyword, providing the flexibility to specify storage paths for particular tables. This flexibility is crucial in cases where certain data needs to be stored in specific locations due to compliance, cost, or performance requirements.
6. Overriding the default storage
The LOCATION keyword enables you to override the default storage, allowing data to be stored in external locations, such as a cloud storage bucket (for example, AWS S3 and Azure Blob Storage). This level of control is helpful when integrating Databricks with an existing data infrastructure or when compliance demands specific storage environments for sensitive data. For instance, healthcare records might require storage in a regulated, secure location, while less sensitive data can remain in default storage.
7. Dynamic data management
This customization with LOCATION also supports dynamic data management. If regulations change or costs become a concern, you might move a table's data to a new location. The LOCATION keyword allows this transition seamlessly, enabling you to relocate data without disrupting the table's structure or your workflow. It's like reorganizing library shelves to make room for new collectibles while keeping everything orderly and accessible.
8. Let's practice!
By mastering these Databricks capabilities, you'll establish a scalable, flexible approach to managing data, ensuring that it remains organized, accessible, and adaptable. Time for some practice!