atoti-server-directquery-jdbc

v0.9.15 suspicious
4.0
Medium Risk

Resources to use DirectQuery through JDBC

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in terms of network, shell, and obfuscation activities. However, the metadata risk score is elevated due to the maintainer having only one package, which could indicate a potential new or less active account.

  • Metadata risk due to single-package maintainer
  • No other significant risks detected
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 invocation.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent related to code obfuscation.
  • Credentials: No credential harvesting patterns detected, suggesting no immediate risk of secret or credential theft.
  • Metadata: The maintainer has only one package, which might indicate a new or less active account, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (3.4/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
○ Low Documentation 1.0

No documentation detected

  • No documentation URL, doc files, or meaningful description found
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in atoti/atoti
  • Two distinct contributors found

🔬 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: activeviam.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository atoti/atoti appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "ActiveViam" 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 atoti-server-directquery-jdbc 
Develop a real-time data analytics dashboard using the 'atoti-server-directquery-jdbc' package in Python. This dashboard will allow users to connect to various data sources through JDBC and perform direct queries to analyze data on-the-fly without loading it entirely into memory. Your task is to create a Flask web application that integrates with 'atoti-server-directquery-jdbc' to provide a user-friendly interface for data exploration and visualization.

### Project Steps:
1. **Setup Environment**: Ensure you have Python installed along with Flask and the 'atoti-server-directquery-jdbc' package. Set up a virtual environment if necessary.
2. **Database Connection**: Use 'atoti-server-directquery-jdbc' to establish a connection to a sample database (e.g., PostgreSQL, MySQL). You'll need to configure the JDBC URL, username, and password.
3. **Data Querying**: Implement functionality within your Flask app to accept SQL queries from the user and execute them against the connected database using 'atoti-server-directquery-jdbc'. Display the results in a tabular format.
4. **Real-Time Analytics**: Extend your application to support real-time data analysis by allowing users to visualize query results dynamically. Consider integrating a JavaScript library like D3.js or Chart.js for interactive visualizations.
5. **User Interface**: Design a clean and intuitive UI where users can input their queries, see results, and interact with visualizations. Make sure to include error handling and feedback messages.
6. **Deployment**: Prepare your application for deployment. This could involve setting up a Docker container or deploying directly to a cloud service like Heroku.

### Suggested Features:
- Support for multiple data sources via JDBC.
- Real-time data streaming capabilities.
- Interactive charts and graphs based on user queries.
- User authentication and role-based access control.
- Export query results to CSV or Excel formats.
- Mobile responsiveness of the UI.

### Utilizing 'atoti-server-directquery-jdbc':
- Use the package to connect to your chosen database through JDBC.
- Leverage its direct query capabilities to minimize latency and improve performance when dealing with large datasets.
- Explore advanced querying options provided by the package to enhance the analytical capabilities of your dashboard.

💬 Discussion Feed

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