apache-superset

v6.1.0 safe
4.0
Medium Risk

A modern, enterprise-ready business intelligence web application

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

🤖 AI Analysis

Final verdict: SAFE

The package shows low risks across network, shell, and obfuscation categories. The metadata risk is slightly elevated due to non-secure links and lack of maintainer details, but there's no strong evidence of malicious intent.

  • No network or shell execution risks detected.
  • Metadata risk due to non-secure links and missing maintainer information.
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communication.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands without user interaction.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package contains non-secure links and lacks maintainer information, raising concerns but not conclusive evidence of malice.

📦 Package Quality Overall: Low (3.2/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "documentation" -> https://superset.apache.org/docs/intro
  • Detailed PyPI description (24037 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 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: superset.apache.org>

Suspicious Page Links score 4.0

Found 2 suspicious link(s) on the package page

  • Non-HTTPS external link: http://www.apache.org/licenses/LICENSE-2.0
  • Link to suspicious/shortener domain: http://bit.ly/join-superset-slack
Git Repository History

No GitHub repository linked

  • No GitHub repository link found
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-superset
Create a fully-functional mini-application using Apache Superset that serves as a dashboard for monitoring sales performance across different regions. This application should allow users to visualize sales data from multiple sources and provide insights into regional sales trends. Here’s a detailed breakdown of the requirements:

1. **Data Sources**: Integrate at least three different data sources into your application. These could include CSV files, SQL databases, or even APIs that return JSON data. Ensure that each data source contains relevant sales data such as product categories, sales amounts, dates, and regions.

2. **Dashboard Design**: Design an intuitive dashboard that allows users to easily switch between different data sources and view sales performance metrics. Include widgets like line charts, bar graphs, pie charts, and tables to display key information.

3. **Custom Metrics**: Implement custom metrics that calculate important KPIs such as total sales per region, average sales per product category, and year-over-year growth rates. These metrics should be dynamically calculated based on user-selected time frames.

4. **User Authentication**: Add basic user authentication functionality so that only authorized users can access the dashboard. Users should be able to log in with their credentials and have roles assigned to them, allowing different levels of access depending on their permissions.

5. **Real-Time Data Updates**: If possible, incorporate real-time data updates from one of your data sources to demonstrate how the dashboard can reflect current sales figures.

6. **Mobile Responsiveness**: Ensure that the dashboard is mobile-responsive, providing a seamless experience for users accessing it via smartphones or tablets.

7. **Documentation**: Provide comprehensive documentation detailing how to set up and run the application, including instructions on installing dependencies, configuring data sources, and setting up authentication.

**Utilization of Apache Superset**:
- Use Apache Superset’s built-in data connectors to integrate your chosen data sources.
- Leverage Superset’s visualization capabilities to create interactive charts and graphs.
- Employ Superset’s security features to manage user authentication and role-based access control.
- Utilize Superset’s SQL Lab feature to perform ad-hoc queries on your data sources.
- Explore Superset’s alerting system to send notifications based on specific conditions or thresholds.

This project will not only showcase Apache Superset’s powerful BI capabilities but also provide valuable insights into sales performance through a visually appealing and user-friendly interface.

💬 Discussion Feed

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