The Databricks CLI is often the fastest method for ad-hoc downloads as it handles the chunking and API calls automatically.
Install the CLI and run databricks configure --token . databricks api download file from dbfs
To download an entire directory, add the --recursive or -r flag. Method 3: Browser Download via /FileStore The Databricks CLI is often the fastest method
Use the fs cp command to copy a file from DBFS to your local machine. Method 3: Browser Download via /FileStore Use the
The GET /api/2.0/dbfs/read endpoint is the standard way to retrieve file contents. However, this endpoint has a per request. To download larger files, you must implement a loop that reads the file in chunks using the offset parameter. Key API Parameters: path : The absolute path of the file (e.g., /tmp/data.csv ). offset : The starting byte position for the read operation.
Downloading files from the Databricks File System (DBFS) to a local environment is a common requirement for data engineering workflows. While Databricks does not offer a direct "Download" button in the standard UI for most DBFS locations, you can use the Databricks REST API or the Databricks CLI to automate these transfers. Method 1: Using the DBFS REST API (Python)
import requests import base64 # Configure credentials domain = "https:// .cloud.databricks.com" token = " " dbfs_path = "/path/to/your/file.csv" local_file_path = "downloaded_file.csv" headers = {"Authorization": f"Bearer {token}"} with open(local_file_path, "wb") as f: offset = 0 while True: response = requests.get( f"{domain}/api/2.0/dbfs/read", headers=headers, params={"path": dbfs_path, "offset": offset, "length": 1048576} ).json() # Decode base64 data and write to file data = base64.b64decode(response['data']) f.write(data) # Check if more bytes remain bytes_read = response['bytes_read'] if bytes_read < 1048576: break offset += bytes_read Use code with caution.