PyAthena

v3.31.0 safe
3.0
Low Risk

Python DB API 2.0 (PEP 249) client for Amazon Athena

🤖 AI Analysis

Final verdict: SAFE

The package has minimal risk indicators with no network calls, shell executions, or obfuscations detected. The only concern is the incomplete maintainer's author information.

  • No network calls detected
  • Incomplete maintainer's author information
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution detected, indicating no direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer's author information is incomplete, indicating potential low trustworthiness.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ 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: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository pyathena-dev/PyAthena 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 PyAthena 
Create a data analysis tool using Python that leverages the PyAthena library to interact with Amazon Athena for querying large datasets stored in S3. This tool will enable users to connect to their Athena database, execute SQL queries, and visualize the results directly within the application. The main functionalities of the tool should include:

1. User Authentication: Implement a simple user authentication system to ensure only authorized users can access the tool.
2. Database Connection: Develop a feature that allows users to connect to their Athena database using PyAthena. Ensure that connection parameters such as S3 output location and region are configurable.
3. Query Execution: Design an interface where users can input SQL queries to be executed against the connected Athena database. The tool should handle query execution, display progress, and manage errors gracefully.
4. Result Visualization: Once a query is executed successfully, the tool should present the results in a visually appealing format. Consider implementing basic charting capabilities like bar charts, line graphs, and pie charts using libraries such as Matplotlib or Plotly.
5. Data Export: Provide an option for users to export query results to CSV or Excel formats for further analysis outside the tool.
6. Query History: Maintain a history of executed queries along with their results for easy reference.
7. Customizable Interface: Allow users to customize the look and feel of the tool through themes or custom stylesheets.

The application should be designed with a focus on usability and efficiency. Use PyAthena to its full potential by exploring its advanced features such as cursor types, query cancellation, and result fetching options. Additionally, consider implementing error handling and logging mechanisms to enhance robustness.