1. Persistence and scope of tables
Welcome back! In this video, we'll explore persistence and scope in Databricks tables, two essential concepts for effective data management and querying.
2. What is table persistence?
In Databricks, table persistence defines how data is stored and retained across sessions, affecting data storage, access, and maintenance. Databricks offers two main table types - managed and unmanaged (unmanaged tables can also be known as external tables). Both managed and unmanaged tables are each suited for different data management needs.
3. Managed tables in Databricks
Managed tables are handled entirely by Databricks, covering data location and lifecycle. When you delete a managed table, Databricks automatically removes all associated data, simplifying data management and ensuring consistency without needing manual intervention. This makes managed tables a great choice when you want a straightforward, centralized approach to data management.
4. Unmanaged tables in Databricks
Unmanaged tables, on the other hand, offer a decentralized approach, providing greater flexibility and control. When you create an unmanaged table, you specify the data's storage location and manage its lifecycle independently. Deleting the table doesn't remove the underlying data, which can be beneficial for custom storage needs or compliance requirements. However, this decentralized setup requires more attention to detail in data management.
5. Managed or unmanaged tables?
When choosing between managed and unmanaged tables, consider your data management strategy. If you prefer simplicity and want Databricks to handle the storage and lifecycle, managed tables are ideal. If you need precise control over where data is stored, unmanaged tables offer that flexibility. Aligning your choice with your project's needs for storage and lifecycle control will ensure effective and efficient data management.
6. The LOCATION keyword
For unmanaged tables, the LOCATION keyword is essential because it lets you define the exact storage location for the data. This can impact storage costs, retrieval times, and retention policies, allowing you to control where your data is physically stored based on your project's needs.
In the example syntax, we can see how a new table is created and how the LOCATION keyword defines the storage location.
7. Key takeaways
To summarize, managed tables in Databricks integrate storage and lifecycle management, making them simpler to use, while unmanaged tables provide flexibility and control over data location and lifecycle. Your decision should reflect your specific data storage, control, and management needs.
8. Let's practice!
Now it's time to put these concepts into practice with some exercises!