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Azure Storage File Datalake Py

MicrosoftOfficialCreated by Microsoft

  • Skill
  • Tool integration
  • Cloud and DevOps
  • MIT
  • Low risk

From the author

Azure Data Lake Storage Gen2 SDK for Python. Use for hierarchical file systems, big data analytics, and file/directory operations. Triggers: "data lake", "DataLakeServiceClient", "FileSystemClient", "ADLS Gen2", "hierarchical namespace".
— the description by Microsoft

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The azure-storage-file-datalake-py skill provides the Python SDK for managing Azure Data Lake Storage Gen2. It enables developers to perform file system, directory, and file operations within a hierarchical namespace. The skill emphasizes secure authentication using DefaultAzureCredential and provides patterns for both synchronous and asynchronous operations, including support for large file uploads and access control management.

What it does

  • Provides clients for account, file system, directory, and file-level operations.
  • Supports file system creation, deletion, and listing.
  • Enables directory management including creation, deletion, and renaming.
  • Facilitates file uploads, downloads, and data appending.
  • Allows retrieval and modification of file and directory properties and metadata.
  • Supports management of Access Control Lists (ACLs) for directories.

When to use it

  • When building applications that require interaction with Azure Data Lake Storage Gen2.
  • When implementing hierarchical file system structures for data storage.
  • When needing to manage file and directory permissions via ACLs.
  • When performing large file uploads using chunked data appending.

Advantages

  • Supports both synchronous and asynchronous programming models.
  • Integrates with DefaultAzureCredential for flexible authentication.
  • Provides a structured client hierarchy for intuitive resource management.
  • Includes patterns for recursive directory listing and property management.

Usage examples

“How do I upload a local file to Azure Data Lake Storage Gen2?”
The agent provides a code snippet using the DataLakeFileClient.upload_data method, demonstrating how to open a local file in binary mode and upload it to a specified path.
“Show me how to list all files and directories in a container.”
The agent provides an example using the FileSystemClient.get_paths method, including logic to distinguish between files and directories in the output.
“How can I set permissions on a directory in the data lake?”
The agent provides a code snippet demonstrating the use of set_access_control or update_access_control_recursive on a DataLakeDirectoryClient.
“How do I use the async client for data lake operations?”
The agent provides an example using the azure.storage.filedatalake.aio namespace and async context managers for secure and efficient resource handling.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/microsoft/azure-storage-file-datalake-py/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip azure-storage-file-datalake-py.zip -d .agents/skills

Documentation by Microsoft

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  # Required for all auth methods
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production

Authentication & Lifecycle

🔑 Two rules apply to every code sample below:

  1. Prefer DefaultAzureCredential. It works locally (Azure CLI / VS Code / Developer CLI) and in Azure (managed identity, workload identity) with no code change. Avoid connection strings, account/API keys — they bypass Entra audit and rotation.

    • Local dev: DefaultAzureCredential works as-is.

    • Production: set AZURE_TOKEN_CREDENTIALS=prod (or AZURE_TOKEN_CREDENTIALS=<specific_credential>) to constrain the credential chain to production-safe credentials.

  2. Wrap every client in a context manager so HTTP transports, sockets, and token caches are released deterministically:

    • Sync: with <Client>(...) as client:

    • Async: async with <Client>(...) as client: and async with DefaultAzureCredential() as credential: (from azure.identity.aio)

Snippets may abbreviate this setup, but production code should always follow both rules.

from azure.identity import DefaultAzureCredential, ManagedIdentityCredential
from azure.storage.filedatalake import DataLakeServiceClient

# Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
credential = DefaultAzureCredential(require_envvar=True)
# Or use a specific credential directly in production:
# See https://learn.microsoft.com/python/api/overview/azure/identity-readme?view=azure-python#credential-classes
# credential = ManagedIdentityCredential()
account_url = "https://<account>.dfs.core.windows.net"

with DataLakeServiceClient(account_url=account_url, credential=credential) as service_client:
    # Use service_client here (see following sections for operations)
    ...

Client Hierarchy

ClientPurpose
DataLakeServiceClientAccount-level operations
FileSystemClientContainer (file system) operations
DataLakeDirectoryClientDirectory operations
DataLakeFileClientFile 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():
    async with DefaultAzureCredential() as credential:
        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")
            
            await file_client.upload_data(b"async content", overwrite=True)
            
            download = await file_client.download_file()
            content = await download.readall()

import asyncio
asyncio.run(datalake_operations())

Best Practices

  1. Pick sync OR async and stay consistent. Do not mix azure.storage.filedatalake sync clients with azure.storage.filedatalake.aio async clients in the same call path. Choose one mode per module.

  2. Always use context managers for clients and async credentials. Wrap every client in with DataLakeServiceClient(...) as client: (sync) or async with DataLakeServiceClient(...) as client: (async). For async DefaultAzureCredential from azure.identity.aio, also use async with credential: so tokens and transports are cleaned up.

  3. Use DefaultAzureCredential for portable auth across local dev and Azure (avoid connection strings / API keys when possible).

  4. Use hierarchical namespace for file system semantics

  5. Use append_data + flush_data for large file uploads

  6. Set ACLs at directory level and inherit to children

  7. Use async client for high-throughput scenarios

  8. Use get_paths with recursive=True for full directory listing

  9. Set metadata for custom file attributes

  10. Consider Blob API for simple object storage use cases

Reference Files

FileContents
references/capabilities.mdAdditional non-hero capabilities, operation-group coverage, and production checklists.
references/non-hero-scenarios.mdDedicated non-hero examples for secondary/advanced scenarios.

Files

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an API key, Needs Python

Security

What our scanner noticed· Low risk

  • Instructions that use the network

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
Microsoft
Publisher
Microsoft (Official)
Source
microsoft/skills · .github/plugins/azure-sdk-python/skills/azure-storage-file-datalake-py · commit 0696600
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by Microsoft.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download azure-storage-file-datalake-py.zip