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APIs & servers (backend)48k · #2382

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

https://github.com/sickn33/agentic-awesome-skills/blob/main/plugins/agentic-awesome-skills-claude/skills/azure-storage-file-datalake-py/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/sickn33/agentic-awesome-skills/blob/main/plugins/agentic-awesome-skills-claude/skills/azure-storage-file-datalake-py/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.
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-py

Or 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.md
Technical 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.

repo: sickn33/agentic-awesome-skills

Popularity

GitHub stars
48k
Ranking position
#2382
License
MIT
Updated
10/15/2026

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