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Add this skill
npx mdskills install sickn33/azure-storage-file-datalake-pyComprehensive reference with clear examples for all major operations and async patterns
Hierarchical file system for big data analytics workloads.
pip install azure-storage-file-datalake azure-identity
AZURE_STORAGE_ACCOUNT_URL=https://.dfs.core.windows.net
from azure.identity import DefaultAzureCredential
from azure.storage.filedatalake import DataLakeServiceClient
credential = DefaultAzureCredential()
account_url = "https://.dfs.core.windows.net"
service_client = DataLakeServiceClient(account_url=account_url, credential=credential)
| Client | Purpose |
|---|---|
DataLakeServiceClient | Account-level operations |
FileSystemClient | Container (file system) operations |
DataLakeDirectoryClient | Directory operations |
DataLakeFileClient | File 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)
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")
# 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
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)
file_client.delete_file()
# 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)
# 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"})
# 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"
)
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://.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())
append_data + flush_data for large file uploadsget_paths with recursive=True for full directory listingInstall via CLI
npx mdskills install sickn33/azure-storage-file-datalake-pyAzure Storage File Datalake Py is a free, open-source AI agent skill. |
Install Azure Storage File Datalake Py with a single command:
npx mdskills install sickn33/azure-storage-file-datalake-pyThis downloads the skill files into your project and your AI agent picks them up automatically.
Azure Storage File Datalake Py works with Claude Code, Claude Desktop, Cursor, Vscode Copilot, Windsurf, Continue Dev, Codex, Gemini Cli, Amp, Roo Code, Goose, Opencode, Trae, Qodo, Command Code. Skills use the open SKILL.md format which is compatible with any AI coding agent that reads markdown instructions.