atoti-client-directquery-bigquery

v0.9.15 suspicious
4.0
Medium Risk

Code to use DirectQuery on Google BigQuery

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal signs of malicious activity, but the lack of network calls and the maintainer's single package history raise some concerns about its legitimacy and purpose.

  • No network calls detected, which is unusual for a client package.
  • Maintainer has only one package, suggesting potential new or less active account.
Per-check LLM notes
  • Network: No network calls detected, which is unusual but not necessarily indicative of malicious activity without more context.
  • Shell: No shell execution patterns detected, which is normal and expected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has only one package, suggesting a new or less active account, which could be suspicious but not conclusive evidence of malice.

📦 Package Quality Overall: Low (4.2/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Documentation URL: "Documentation" -> https://docs.activeviam.com/products/atoti/python-sdk/0.9.15
○ 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-client-directquery-bigquery 
Your task is to develop a data analytics mini-application using Python that leverages the 'atoti-client-directquery-bigquery' package to directly query Google BigQuery datasets. This application will serve as a powerful tool for users to explore, analyze, and visualize data stored in BigQuery without needing to download or store the data locally. The app will connect to a publicly available dataset from BigQuery and provide functionalities such as filtering, sorting, and basic statistical analysis of the queried data.

### Step-by-Step Development Plan:
1. **Setup**: Install necessary packages including 'atoti-client-directquery-bigquery', pandas, and matplotlib for visualization.
2. **Connection**: Use 'atoti-client-directquery-bigquery' to establish a connection to a specific BigQuery dataset. For instance, you might choose the 'BigQuery Public Datasets' like the 'US Domestic Flights From the Bureau of Transportation Statistics'.
3. **Data Retrieval**: Implement a function to retrieve data based on user-defined queries or predefined filters. Ensure the application can handle large datasets efficiently by utilizing the direct query capabilities of the 'atoti-client-directquery-bigquery' package.
4. **Data Analysis**: Add functionality to perform basic statistical analyses (mean, median, mode, etc.) on the retrieved data.
5. **Visualization**: Integrate matplotlib or any other plotting library to create visual representations of the data, such as bar charts, line graphs, and pie charts.
6. **User Interface**: Develop a simple command-line interface where users can input their queries or select predefined analyses.
7. **Testing**: Test the application thoroughly to ensure it handles various types of queries correctly and provides accurate results.
8. **Documentation**: Write clear documentation explaining how to install and run the application, along with examples of queries and analyses.

### Suggested Features:
- Allow users to specify columns they want to include in the analysis.
- Implement sorting options based on different criteria.
- Provide options to filter data based on date ranges, categorical values, etc.
- Include a feature to save the results of queries and analyses into a local CSV file.
- Offer advanced statistical functions like standard deviation, variance, etc.
- Enhance the user interface to include a graphical user interface (GUI) using Tkinter or PyQt.

### Utilization of 'atoti-client-directquery-bigquery':
This package enables efficient querying of BigQuery datasets directly, which is crucial for handling large datasets without the need for extensive local storage. By integrating this package, your application can dynamically fetch data based on user inputs, making real-time analysis possible. Additionally, leveraging the direct query capabilities ensures that only the required data is fetched, optimizing performance and reducing costs associated with data transfer.

💬 Discussion Feed

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