atoti-server-directquery-snowflake

v0.9.15 safe
3.0
Low Risk

Resources to use DirectQuery on Snowflake

⚠ Tarball exceeded 25 MB — source code analysis was limited to package metadata only.

🤖 AI Analysis

Final verdict: SAFE

The package shows low risk indicators across all categories except metadata, where the single-package maintainer status raises a minor flag. Overall, it appears safe with no direct evidence of malicious activity.

  • No network calls or shell executions detected
  • Maintainer has only one package
Per-check LLM notes
  • Network: No network calls detected, which is unusual but not necessarily indicative of malicious activity; the package might be designed to work offline or with minimal external dependencies.
  • Shell: No shell executions detected, suggesting the package does not attempt to execute system commands directly, reducing immediate risk of malicious activity.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets and credentials.
  • Metadata: The maintainer has only one package, suggesting a potentially new or less active account which could indicate risk.

📦 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-snowflake 
Your task is to develop a real-time analytics dashboard using Python that integrates with Snowflake through the 'atoti-server-directquery-snowflake' package. This application will enable users to query large datasets stored in Snowflake directly from a web interface without having to load all data into memory. Here's a detailed plan for your project:

1. **Setup Environment**: Install necessary Python packages including 'atoti-server-directquery-snowflake', 'Flask' for the web server, and 'plotly' for interactive visualizations.
2. **Database Connection**: Use 'atoti-server-directquery-snowflake' to establish a connection to a Snowflake database. Ensure you configure the credentials securely.
3. **Data Querying**: Implement a function that allows users to input SQL queries through the web interface. Utilize 'atoti-server-directquery-snowflake' to execute these queries against the Snowflake database directly, fetching only the results needed for visualization.
4. **Visualization Interface**: Create dynamic charts and graphs using Plotly based on the query results. Users should be able to interact with these visuals to explore data further.
5. **Real-Time Updates**: Enable the dashboard to refresh its data periodically or upon user request, ensuring the displayed information is always up-to-date.
6. **User Authentication**: Integrate basic authentication to secure access to the dashboard.
7. **Error Handling & Logging**: Implement robust error handling and logging mechanisms to capture any issues during query execution or data processing.
8. **Documentation**: Write comprehensive documentation detailing how to set up the environment, use the dashboard, and troubleshoot common issues.

This project leverages 'atoti-server-directquery-snowflake' to handle complex queries efficiently while keeping the application lightweight and responsive. It's ideal for businesses looking to perform ad-hoc analysis on large datasets without significant performance overhead.

💬 Discussion Feed

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