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azure-storage-file-datalake-py

Azure Data Lake Storage Gen2 SDK for Python.

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About

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".

Use cases: cloud, data

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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

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  • codeload.github.com · found in install command

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  • ~/.claude/skills/azure-storage-file-datalake-py

What's inside

Skill 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 · package: azure-storage-file-datalake · version: 1.0.0
Instructions
232 lines in SKILL.md · view on GitHub

Files (3)

  • SKILL.md
  • capabilities.md · references/
  • non-hero-scenarios.md · references/

SKILL.md (first lines)

# 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")

From microsoft/skills, MIT.

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Last commit (this folder)
Jul 15, 2026 (3 months ago)

From GitHub (fetched Oct 24, 2026).

Details

Version
1.0.0
License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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azure-storage-file-datalake-py skill. Agent Mart. https://agentmart.dev/skills/microsoft-azure-storage-file-datalake-py (last updated 2026-10-24)