Databricks File System


             Databricks File System

Here’s a breakdown of the Databricks File System (DBFS) along with its features, use cases, and how it works:

What is the Databricks File System (DBFS)?

  • Distributed File System: DBFS is a distributed file system that’s integrated natively into Databricks workspaces.
  • Cloud Object Storage Abstraction: It acts as a layer on top of scalable object storage services (like Azure Blob Storage, AWS S3, Google Cloud Storage), making interaction with these cloud storage solutions seamless.
  • Simplified Management: DBFS simplifies working with data stored in the cloud. It lets you use traditional file system conventions (directories, files, paths) rather than dealing with complex cloud storage APIs directly.

Key Features of DBFS

  • Simplified Interaction: DBFS uses file and directory semantics making file manipulation in object storage feel familiar to most developers.
  • Mounting: You can mount cloud storage buckets or containers to DBFS, allowing easier data access across your Databricks environment.
  • Persistence: Data saved into DBFS persists even after your Databricks cluster terminates.
  • Convenient Storage: DBFS is an ideal location to store resources like libraries, configuration files, and initialization scripts for your clusters.

How Does DBFS work?

At its core, DBFS translates file system operations you perform within your Databricks code into the native API calls of the underlying object storage provider. This abstraction layer hides the complexities of working with the cloud storage service directly.

Common DBFS Use Cases

  1. Data Storage and Access: DBFS is commonly used to store and read data of various sizes for processing with Spark or other data analytics tools within your Databricks workspace.
  2. Data Sharing Across Workspaces: DBFS facilitates data sharing between different Databricks workspaces.
  3. Code and Library Storage: Teams regularly store code, libraries, and JARs in DBFS for access by clusters.
  4. Model Storage: DBFS can house machine learning models for easy deployment.

Example: Reading a File from DBFS with Python

# Assuming data is in a CSV file in DBFS
df =“dbfs:/FileStore/tables/my_data.csv”)
Important Notes:
  • While DBFS offers convenience, Databricks strongly recommends using Unity Catalog for a more robust and secure approach to managing datasets in production environments.
  • DBFS comes with a default root volume. Storing production data there is generally discouraged.

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