azure-storage-file-datalake-py
Lets your agent manage files in Azure Data Lake Storage Gen2 with Python. It's useful for organizing large data volumes and analytics.
What it does
This tool lets you manage files and folders in a cloud storage service called Azure Data Lake Storage Gen2 using Python. It's like a cloud file system for organizing and analyzing large amounts of data.
When to use it
- ✓Organizing large datasets for analysis.
- ✓Creating and managing cloud folders and files.
- ✓Uploading or downloading large files efficiently.
Example of what to ask your AI
“I want to upload the file 'report.csv' to the 'data/sales' folder in my Data Lake.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
You write Python code that connects to Azure Data Lake. This code uses specific commands to create folders, upload files, download files, or list what's already there.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill azure-storage-file-datalake-pyOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/azure-storage-file-datalake-py/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Azure Data Lake Storage Gen2 SDK for Python
Hierarchical file system for big data analytics workloads.
Installation
pip install azure-storage-file-datalake azure-identity
Environment Variables
AZURE_STORAGE_ACCOUNT_URL=https://<account>.dfs.core.windows.net
Authentication
from azure.identity import DefaultAzureCredential
from azure.storage.filedatalake import DataLakeServiceClient
credential = DefaultAzureCredential()
account_url = "https://<account>.dfs.core.windows.net"
service_client = DataLakeServiceClient(account_url=account_url, credential=credential)
Client Hierarchy
| Client | Purpose |
|---|---|
DataLakeServiceClient |
Account-level operations |
FileSystemClient |
Container (file system) operations |
DataLakeDirectoryClient |
Directory operations |
DataLakeFileClient |
File operations |
File System Operations
# Create file system (container)
file_system_client = service_client.create_file_system("myfilesystem")
# Get existing
file_system_client = service_client.get_file_system_client("myfilesystem")
# Delete
service_client.delete_file_system("myfilesystem")
# List file systems
for fs in service_client.list_file_systems():
print(fs.name)
Directory Operations
file_system_client = service_client.get_file_system_client("myfilesystem")
# Create directory
directory_client = file_system_client.create_directory("mydir")
# Create nested directories
directory_client = file_system_client.create_directory("path/to/nested/dir")
# Get directory client
directory_client = file_system_client.get_directory_client("mydir")
# Delete directory
directory_client.delete_directory()
# Rename/move directory
directory_client.rename_directory(new_name="myfilesystem/newname")
File Operations
Upload File
# Get file client
file_client = file_system_client.get_file_client("path/to/file.txt")
# Upload from local file
with open("local-file.txt", "rb") as data:
file_client.upload_data(data, overwrite=True)
# Upload bytes
file_client.upload_data(b"Hello, Data Lake!", overwrite=True)
# Append data (for large files)
file_client.append_data(data=b"chunk1", offset=0, length=6)
file_client.append_data(data=b"chunk2", offset=6, length=6)
file_client.flush_data(12) # Commit the data
Download File
file_client = file_system_client.get_file_client("path/to/file.txt")
# Download all content
download = file_client.download_file()
content = download.readall()
# Download to file
with open("downloaded.txt", "wb") as f:
download = file_client.download_file()
download.readinto(f)
# Download range
download = file_client.download_file(offset=0, length=100)
Delete File
file_client.delete_file()
List Contents
# List paths (files and directories)
for path in file_system_client.get_paths():
print(f"{'DIR' if path.is_directory else 'FILE'}: {path.name}")
# List paths in directory
for path in file_system_client.get_paths(path="mydir"):
print(path.name)
# Recursive listing
for path in file_system_client.get_paths(path="mydir", recursive=True):
print(path.name)
File/Directory Properties
# Get properties
properties = file_client.get_file_properties()
print(f"Size: {properties.size}")
print(f"Last modified: {properties.last_modified}")
# Set metadata
file_client.set_metadata(metadata={"processed": "true"})
Access Control (ACL)
# Get ACL
acl = directory_client.get_access_control()
print(f"Owner: {acl['owner']}")
print(f"Permissions: {acl['permissions']}")
# Set ACL
directory_client.set_access_control(
owner="user-id",
permissions="rwxr-x---"
)
# Update ACL entries
from azure.storage.filedatalake import AccessControlChangeResult
directory_client.update_access_control_recursive(
acl="user:user-id:rwx"
)
Async Client
from azure.storage.filedatalake.aio import DataLakeServiceClient
from azure.identity.aio import DefaultAzureCredential
async def datalake_operations():
credential = DefaultAzureCredential()
async with DataLakeServiceClient(
account_url="https://<account>.dfs.core.windows.net",
credential=credential
) as service_client:
file_system_client = service_client.get_file_system_client("myfilesystem")
file_client = file_system_client.get_file_client("test.txt")
…
FAQ
What is Azure Data Lake Storage Gen2?
It's a cloud storage service from Microsoft designed to hold and process large amounts of data in an organized and efficient way.
Do I need to know Python programming to use this?
Yes, this tool is a Python SDK (Software Development Kit), so you need to write Python code to interact with the service.
Can I use this to store any type of file?
Yes, you can store any file type, such as documents, images, videos, and especially data files for analysis.
Details
Original description by the author
Azure Data Lake Storage Gen2 SDK for Python. Use for hierarchical file systems, big data analytics, and file/directory operations.
Popularity
- GitHub stars
- 48k
- Ranking position
- #2382
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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