Mounting S3 to Databricks vs. Using DBFS
Mounting an S3 bucket to Databricks File System (DBFS) is a way to access cloud object storage directly from Databricks. This method allows users to interact with S3 data using familiar file paths relative to DBFS. However, Databricks recommends moving away from mounts and instead using Unity Catalog for data governance and access management.
DBFS, on the other hand, is a distributed file system that is native to Databricks. It provides a hierarchical namespace for storing and managing data within the Databricks workspace. While DBFS offers a convenient way to store and manage data locally within Databricks, it does not directly integrate with external cloud storage like S3 without mounting.
Mounting S3 to DBFS is useful when you need to access data stored in S3 directly from Databricks without copying it into DBFS. This approach is beneficial for large datasets where data transfer might be costly or time-consuming. However, for data that is frequently accessed or modified within Databricks, storing it in DBFS might be more efficient.
Frequently Asked Questions
- Q: What is the purpose of using Unity Catalog in Databricks?
A: Unity Catalog is used for managing data access and governance across different data sources, including cloud storage. It provides a unified way to manage permissions and metadata.
- Q: Can I use both mounts and Unity Catalog simultaneously?
A: Yes, you can use both, but Databricks recommends migrating to Unity Catalog for better data governance and access management.
- Q: How do I secure my S3 bucket when mounting it to DBFS?
A: You can secure your S3 bucket by using AWS instance profiles, AWS keys stored as secrets, or server-side encryption like SSE-S3 or SSE-KMS.
- Q: What happens if I unmount storage while jobs are running?
A: Unmounting storage while jobs are running can lead to errors. Ensure that production jobs do not unmount storage as part of their processing.
- Q: Can I use mounts with other cloud storage services?
A: Yes, Databricks supports mounting other cloud storage services like Azure Data Lake Storage Gen2 using ABFS.
- Q: How do I handle secret rotation for mounted storage?
A: If a secret used for mounting storage is rotated, you must unmount and remount the storage to avoid errors like 401 Unauthorized.
- Q: Can I use DBFS for storing large datasets?
A: While DBFS can store large datasets, it’s more efficient for frequently accessed data. For infrequently accessed large datasets, consider storing them in cloud storage like S3.
Bottom Line
Mounting S3 to DBFS is ideal for accessing external cloud storage directly from Databricks, especially for large datasets. However, for data governance and frequent data access, using Unity Catalog and storing data in DBFS might be more efficient. Always consider the specific needs of your workflow when deciding between these approaches.