The Databricks File System (DBFS) in Azure

Category : Microsoft Azure Data Engineering | Sub Category : Databricks | By Prasad Bonam Last updated: 2023-09-23 09:59:41 Viewed : 283

Azure Databricks provides a file system called the Databricks File System (DBFS) that allows users to interact with data stored in various storage services, such as Azure Data Lake Storage, Azure Blob Storage, and others, in a convenient and unified way. DBFS is designed to simplify data access and management in Databricks workspaces. Here is an overview of the Azure Databricks File System (DBFS):

1. Unified Data Access:

   • DBFS provides a unified namespace for data access across different storage services. This means that you can interact with data in Azure Data Lake Storage, Azure Blob Storage, and other data sources using a consistent file path.

2. Mounting External Storage:

   • You can mount external storage systems, such as Azure Blob Storage or Azure Data Lake Storage, to DBFS. This allows you to access and manage data in these external storage locations as if they were part of the DBFS.

3. Supported File Formats:

   • DBFS supports various file formats, including Parquet, Delta Lake, CSV, JSON, Avro, and more. This flexibility allows users to work with data in their preferred format.

4. Integration with Databricks Notebooks:

   • DBFS is tightly integrated with Databricks notebooks and can be accessed directly from within notebooks. You can read, write, and manipulate data using DBFS commands within notebook cells.

5. Workspace-Level and Cluster-Level Mounts:

   • You can configure mount points at both the workspace level and the cluster level. Workspace-level mounts are available to all clusters within a workspace, while cluster-level mounts are specific to a particular cluster.

6. Security and Authentication:

   • DBFS integrates with Azure Active Directory (Azure AD) for authentication and access control. You can manage access to DBFS through Azure Databricks role-based access control (RBAC) and Azure AD integration.

7. Parallel Read/Write Operations:

   • DBFS supports parallel read and write operations, making it suitable for large-scale data processing and analytics.

8. dbutils Commands:

   • In Databricks notebooks, you can use the dbutils utility to perform various DBFS operations, such as mounting external storage, copying files, and managing directories.

Here are some common DBFS commands and examples:

• Mounting Azure Blob Storage:

  pythonCopy code
  dbutils.fs.mount(source="wasbs://<container>@<storage_account>.blob.core.windows.net/",
                   mount_point="/mnt/<mount-name>",
                   extra_configs={"<conf-key>":dbutils.secrets.get(scope="<scope-name>", key="<key-name>")})
  

• Reading a File:

  pythonCopy code
  df = spark.read.csv("/mnt/<mount-name>/data.csv")
  

• Writing a File:

  pythonCopy code
  df.write.parquet("/mnt/<mount-name>/output.parquet")
  

• Unmounting a Storage Mount:

  pythonCopy code
  dbutils.fs.unmount("/mnt/<mount-name>")
  

Overall, Azure Databricks File System (DBFS) simplifies data management and access in Databricks workspaces, making it easier for data engineers and data scientists to work with data stored in various Azure storage services.