azure-mgmt-datafactory

v9.3.0 safe
3.0
Low Risk

Microsoft Azure Datafactory Management Client Library for Python

🤖 AI Analysis

Final verdict: SAFE

The package azure-mgmt-datafactory v9.3.0 appears to be legitimate with minimal risks identified. The lack of network, shell execution, and credential harvesting patterns suggests it does not pose immediate threats.

  • Low network and shell execution risk
  • Incomplete maintainer metadata
  • No evidence of malicious activity
Per-check LLM notes
  • Network: No network calls detected, which is expected for a library that likely interacts with Azure services through SDKs rather than direct HTTP requests.
  • Shell: No shell execution patterns detected, aligning with the typical behavior of a legitimate software development kit.
  • Obfuscation: The observed pattern is likely a standard method for extending package paths and not indicative of malicious obfuscation.
  • Credentials: No patterns indicative of credential harvesting were detected.
  • Metadata: The maintainer's author information is incomplete, but there are no other red flags.

📦 Package Quality Overall: Medium (7.0/10)

✦ High Test Suite 9.0

Test suite present — 5 test file(s) found

  • Test runner config found: conftest.py
  • 5 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (132138 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 342 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 35 unique contributor(s) across 100 commits in Azure/azure-sdk-for-python
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • __path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore __path__ =
  • ) # type: ignore __path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore # coding=u
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: microsoft.com> license-expression: mit

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Azure/azure-sdk-for-python appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with azure-mgmt-datafactory 
Create a Python-based utility application that leverages the 'azure-mgmt-datafactory' package to automate the management of Azure Data Factory resources. This utility will allow users to create, update, delete, and manage data pipelines within their Azure environment. The application should have a simple command-line interface (CLI) for user interaction.

Step 1: Setup
- Ensure you have the necessary Azure credentials and permissions to manage Data Factory resources.
- Install the 'azure-mgmt-datafactory' package using pip.
- Authenticate your application with Azure using Azure CLI or Azure SDK for Python.

Step 2: Application Design
- Design a CLI interface with commands like 'create', 'update', 'delete', and 'list' for managing Data Factory resources.
- Implement functionality to handle exceptions and errors gracefully.
- Provide clear help messages for each command.

Step 3: Core Features
- Create a new Data Factory resource with specified name and location.
- Update existing Data Factory properties such as tags or linked services.
- Delete a Data Factory resource.
- List all Data Factory resources under a specific subscription.
- Show details of a specific Data Factory resource.

Step 4: Advanced Features
- Integrate with other Azure services like Storage Accounts and SQL Databases to demonstrate end-to-end data pipeline management.
- Implement version control for Data Factory assets (pipelines, datasets, etc.).
- Allow users to upload and download Data Factory templates (.json files).

Step 5: Testing
- Test each feature with different scenarios to ensure robustness.
- Document any issues encountered during testing and provide solutions.
- Ensure the application works as expected across various Azure regions.

The 'azure-mgmt-datafactory' package will be used extensively throughout the project to interact with Azure Data Factory resources programmatically. This includes creating clients, handling operations, and managing resources through the Azure API.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!