apache-airflow-providers-microsoft-azure

v13.3.0 safe
3.0
Low Risk

Provider package apache-airflow-providers-microsoft-azure for Apache Airflow

🤖 AI Analysis

Final verdict: SAFE

The package shows no significant signs of malicious activity. It operates without making network calls or executing shell commands, which is consistent with its intended purpose.

  • Low network and shell risk
  • No evidence of obfuscation or credential harvesting
  • Minor metadata issues but not indicative of malicious behavior
Per-check LLM notes
  • Network: No network calls detected, which is unusual but not necessarily indicative of malicious activity; the package may operate without external dependencies.
  • Shell: No shell execution patterns detected, aligning with expectations for a legitimate package focused on integration and automation.
  • Obfuscation: The observed pattern is likely a standard import mechanism for extending module search paths and not indicative of malicious activity.
  • Credentials: No patterns indicating credential harvesting were detected.
  • Metadata: The package has some minor issues but does not show significant signs of being malicious.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 46 test file(s) found

  • Test runner config found: conftest.py
  • 46 test file(s) detected (e.g. conftest.py)
✦ High Documentation 9.0

Well-documented package

  • Documentation URL: "Documentation" -> https://airflow.apache.org/docs/apache-airflow-providers-mic
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (6338 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
  • 75 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 46 unique contributor(s) across 100 commits in apache/airflow
  • 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)

  • under the License. __path__ = __import__("pkgutil").extend_path(__path__, __name__) # Licensed to the Apache S
  • under the License. __path__ = __import__("pkgutil").extend_path(__path__, __name__) # # Licensed to the Apache
✓ 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: airflow.apache.org>

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://www.apache.org/licenses/LICENSE-2.0
✓ Git Repository History

Repository apache/airflow 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 apache-airflow-providers-microsoft-azure 
Create a mini-application that automates the process of managing Azure resources using Apache Airflow and the 'apache-airflow-providers-microsoft-azure' package. This application will serve as a scheduler and orchestrator for various tasks related to Azure services such as Blob Storage, Virtual Machines, and Web Apps. Here’s a detailed plan on how to proceed:

1. **Project Setup**: Initialize your Python environment and install necessary packages including 'apache-airflow', 'apache-airflow-providers-microsoft-azure', and any other dependencies you might need.

2. **Configuration**: Configure Airflow to connect to your Azure environment. This involves setting up connections within Airflow to authenticate with Azure services. Ensure that you have the necessary credentials and permissions to access Azure resources.

3. **Task Definitions**: Define tasks within Airflow DAGs that correspond to operations you want to perform on Azure resources. For example, create a task to deploy a web app, another to manage virtual machines, and yet another to handle Blob Storage operations.

4. **Integration with Azure Services**:
   - **Blob Storage Operations**: Use the 'apache-airflow-providers-microsoft-azure' package to interact with Azure Blob Storage. Tasks could include uploading files, listing blobs, deleting blobs, etc.
   - **Virtual Machine Management**: Automate the provisioning, scaling, and management of virtual machines in Azure.
   - **Web App Deployment**: Implement tasks for deploying code to Azure Web Apps, including setting up environments, deploying new versions, and managing configurations.

5. **Scheduling and Orchestration**: Set up scheduling for these tasks using Airflow’s powerful scheduling capabilities. For instance, schedule regular backups to Blob Storage, periodic health checks for VMs, and automated deployments for web apps.

6. **Monitoring and Logging**: Integrate monitoring and logging functionalities to track the status of your tasks and ensure everything is running smoothly. Use Airflow’s built-in monitoring tools or integrate with external systems like Azure Monitor for more comprehensive insights.

7. **Documentation and Testing**: Document all steps involved in setting up and running your application. Also, write unit tests and integration tests to ensure reliability and correctness of your implementation.

By the end of this project, you will have a robust, scalable, and maintainable system for managing Azure resources through Apache Airflow, leveraging the capabilities provided by the 'apache-airflow-providers-microsoft-azure' package.

💬 Discussion Feed

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