apache-airflow-providers-yandex

v4.5.0 safe
2.0
Low Risk

Provider package apache-airflow-providers-yandex for Apache Airflow

🤖 AI Analysis

Final verdict: SAFE

The package shows low risks across all categories with no signs of malicious activity. It appears safe for use.

  • Low risk scores across all categories
  • No evidence of supply-chain attack
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communications.
  • Shell: No shell execution patterns detected, indicating no unexpected system command execution.
  • Obfuscation: The observed pattern is likely for extending the package path and is not indicative of malicious obfuscation.
  • Credentials: No suspicious patterns related to credential harvesting were detected.
  • Metadata: The package has some minor issues but no clear signs of malicious intent.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 24 test file(s) found

  • Test runner config found: conftest.py
  • 24 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-yan
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (4011 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
  • 47 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 2.0

Found 1 obfuscation pattern(s)

  • under the License. __path__ = __import__("pkgutil").extend_path(__path__, __name__) # Licensed to the Apache S
✓ 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-yandex 
Create a small but powerful data pipeline management tool using Apache Airflow and the 'apache-airflow-providers-yandex' package. Your goal is to build a mini-application that can automate and manage workflows involving Yandex services, such as cloud storage or databases. This tool will enable users to schedule tasks, monitor execution, and handle errors seamlessly. Here’s a detailed breakdown of what your application should achieve:

1. **Setup Environment**: Begin by setting up a Python environment with all necessary dependencies installed, including Apache Airflow and the 'apache-airflow-providers-yandex' package.
2. **Define Workflow Tasks**: Design several tasks within your workflow, each representing different operations such as fetching data from a Yandex.Cloud Storage, processing it locally, and then uploading results back to another Yandex.Cloud Storage bucket.
3. **Error Handling & Logging**: Implement robust error handling and logging mechanisms to ensure that any issues encountered during task execution are captured and logged appropriately, facilitating easier debugging and maintenance.
4. **Scheduling & Monitoring**: Configure scheduling so that these tasks run at predefined intervals. Additionally, provide a basic monitoring dashboard where users can view the status of their workflows in real-time, including start time, completion time, and any error messages.
5. **Integration with Yandex Services**: Utilize the functionalities provided by the 'apache-airflow-providers-yandex' package to interact with Yandex services efficiently. Ensure that authentication and authorization are handled securely.
6. **User Interface**: Develop a simple web interface where users can create new workflows, modify existing ones, and trigger manual executions if needed. This UI should also display logs and other relevant information about ongoing and completed tasks.
7. **Documentation & Testing**: Provide comprehensive documentation detailing how to install, configure, and use your application. Conduct thorough testing to verify that all components work as expected under various conditions.

By completing this project, you'll gain valuable experience in building complex data pipelines using Apache Airflow and integrating with external cloud services through third-party provider packages.

💬 Discussion Feed

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