azure-storage-file-datalake-py — Claude Code skill: setup and use
This skill guides Python work with Azure Data Lake Storage Gen2, from file-system setup to file and directory operations. Use it when building or updating ADLS Gen2 code.
npx skills add microsoft/skills --skill azure-storage-file-datalake-py -a claude-codeWhat azure-storage-file-datalake-py does
The skill covers the azure-storage-file-datalake Python SDK. It explains the service, file-system, directory, and file clients, with examples for creating and removing resources, transferring files, listing paths, and managing ACLs.
Both synchronous and asynchronous examples are included. The guidance uses DefaultAzureCredential and context managers for clients and credentials. Install azure-storage-file-datalake and azure-identity; set AZURE_STORAGE_ACCOUNT_URL to the account endpoint. The SDK examples need that endpoint to connect. For production with DefaultAzureCredential, set AZURE_TOKEN_CREDENTIALS=prod or provide a specific credential name.
What it helps with
- create an ADLS Gen2 file system
- upload a local file or append data in chunks
- download a whole file or a byte range
- list files and directories recursively
- read or update directory ACLs
Example prompts for the agent
- Write Python code that creates an Azure Data Lake Storage Gen2 file system and uploads a local file.
- Show how to list a directory recursively in ADLS Gen2, then download one file.
- Create an async Python example that uploads and downloads a file with the Azure Data Lake Storage SDK.
How to install azure-storage-file-datalake-py
1Skills CLI (Vercel)
npx skills add microsoft/skills --skill azure-storage-file-datalake-py -a claude-code2Manually: put the files in place
~/.claude/skills/azure-storage-file-datalake-py/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/azure-storage-file-datalake-py/
Manually: put the files in place
.agents/skills/azure-storage-file-datalake-py/SKILL.mdFor all projects use ~/.agents/skills/
Manually: put the files in place
.cursor/skills/azure-storage-file-datalake-py/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install microsoft/skills azure-storage-file-datalake-py2Manually: put the files in place
.github/skills/azure-storage-file-datalake-py/SKILL.mdPlatform CLI
gemini skills install https://github.com/microsoft/skills --path .github/plugins/azure-sdk-python/skills/azure-storage-file-datalake-pyManually: put the files in place
.devin/skills/azure-storage-file-datalake-py/SKILL.mdThe legacy .windsurf/skills/ path still works
Claude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
Step by step, with commands for each platform: How to install skills in Claude Code: five ways
Questions about azure-storage-file-datalake-py
How do I install azure-storage-file-datalake-py in Claude Code?
Run npx skills add microsoft/skills --skill azure-storage-file-datalake-py -a claude-code. The skill file is at ~/.claude/skills/azure-storage-file-datalake-py/SKILL.md.
What does azure-storage-file-datalake-py do?
It guides Python development with the Azure Data Lake Storage Gen2 SDK. The examples cover file systems, directories, files, ACLs, and asynchronous clients.
Which packages and settings does the SDK require?
The installation uses azure-storage-file-datalake and azure-identity. Set AZURE_STORAGE_ACCOUNT_URL for the account endpoint; in production, DefaultAzureCredential also uses AZURE_TOKEN_CREDENTIALS=prod or a specific credential name.
Source text
SKILL.md · MIT
---
name: azure-storage-file-datalake-py
description: |
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".
license: MIT
metadata:
author: Microsoft
version: "1.0.0"
package: azure-storage-file-datalake
---
# Azure Data Lake Storage Gen2 SDK for Python
Hierarchical file system for big data analytics workloads.
## Installation
```bash
pip install azure-storage-file-datalake azure-identity
```
## Environment Variables
```bash
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.
```python
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
| Client | Purpose |
|--------|---------|
| `DataLakeServiceClient` | Account-level operations |
| `FileSystemClient` | Container (file system) operations |
| `DataLakeDirectoryClient` | Directory operations |
| `DataLakeFileClient` | File operations |
## File System Operations
```python
# 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
```python
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
```python
# 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
```python
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
```python
file_client.delete_file()
```
## List Contents
```python
# 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
```python
# 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)
```python
# 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
```python
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
| File | Contents |
|------|----------|
| [references/capabilities.md](references/capabilities.md) | Additional non-hero capabilities, operation-group coverage, and production checklists. |
| [references/non-hero-scenarios.md](references/non-hero-scenarios.md) | Dedicated non-hero examples for secondary/advanced scenarios. |
© Microsoft · MIT · microsoft/skills