apache-superset-core

v0.1.0 suspicious
4.0
Medium Risk

Core Python package for building Apache Superset backend extensions and integrations

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some red flags regarding metadata completeness and secure practices, but there are no clear indications of malicious activity or direct risks such as network calls or shell execution.

  • Lacking maintainer's author information
  • Non-HTTPS license link
Per-check LLM notes
  • Network: No network calls detected, which is normal for most packages unless external services are required.
  • Shell: No shell execution patterns detected, indicating no direct system command execution from the package.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows some red flags, but they do not strongly indicate malicious intent. The maintainer's author information is lacking and the license link is non-HTTPS.

📦 Package Quality Overall: Medium (5.4/10)

○ 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/
  • Detailed PyPI description (3220 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 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 29 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 15 unique contributor(s) across 100 commits in apache/superset
  • Active community — 5 or more distinct contributors

🔬 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 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/superset 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-superset-core 
Your task is to create a simple yet powerful dashboard application using Apache Superset, focusing on leveraging the 'apache-superset-core' package for backend functionality. This application will allow users to visualize data from multiple data sources in real-time, making it an invaluable tool for quick decision-making. Here’s a step-by-step guide on how to build this application:

1. **Project Setup**: Begin by setting up your development environment. Ensure you have Python installed along with the necessary packages like Flask, SQLAlchemy, and of course, 'apache-superset-core'. Initialize a new Python project and set up a virtual environment.
2. **Database Integration**: Integrate the application with at least two different types of databases (e.g., PostgreSQL and MySQL) to demonstrate the flexibility of 'apache-superset-core'. Use SQLAlchemy for ORM operations.
3. **Data Source Configuration**: Utilize 'apache-superset-core' to configure these data sources within your application. Set up models to represent tables in your databases and ensure that your application can dynamically discover and connect to these tables.
4. **Dashboard Creation**: Implement a feature that allows users to create custom dashboards. Users should be able to select specific metrics and dimensions from their chosen data source(s), apply filters, and choose from a variety of visualization types such as bar charts, line graphs, and pie charts.
5. **Real-Time Data Updates**: Incorporate real-time data updates into your dashboards. Users should see changes in their visualizations as new data is added to the underlying databases.
6. **User Authentication and Authorization**: Add user authentication and authorization mechanisms to control access to the dashboards and data sources. Different roles should have varying levels of access based on their permissions.
7. **Custom Plugins and Extensions**: Explore the capabilities of 'apache-superset-core' to develop custom plugins or extensions that add unique functionalities to your dashboards, such as advanced filtering options or interactive widgets.
8. **Testing and Deployment**: Write unit tests for critical components of your application to ensure reliability. Finally, deploy your application using a platform like Heroku or Docker, ensuring that all configurations and dependencies are correctly set up for production use.

By following these steps, you'll create a robust and flexible dashboard application that leverages the power of 'apache-superset-core' to provide valuable insights through dynamic data visualization.

💬 Discussion Feed

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