atoti-client-gcp

v0.9.15 safe
4.0
Medium Risk

Deprecated: use atoti-client-storage-gcp instead

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators with no network calls, shell executions, or obfuscations detected. The only concern is the maintainer's single package history, but this alone does not suggest malicious intent.

  • Low network and shell risk
  • No signs of obfuscation or credential harvesting
  • Maintainer has only one package
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution patterns detected, indicating no direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, suggesting no risk of secret theft.
  • Metadata: The maintainer has only one package, which could indicate a new or less active account.

📦 Package Quality Overall: Low (3.8/10)

○ 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 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

  • 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-gcp 
Create a data analytics dashboard application using Python that leverages the 'atoti-client-gcp' package (though note that it's deprecated; you might consider using 'atoti-client-storage-gcp' as an alternative). This application will allow users to upload datasets from Google Cloud Storage (GCS), perform basic statistical analysis, and visualize the results. Here’s a step-by-step guide on how to build this application:

1. **Setup Environment**: Ensure your Python environment is set up with all necessary packages installed, including 'pandas', 'matplotlib', 'seaborn', and 'atoti-client-gcp'. If 'atoti-client-gcp' is deprecated, replace it with 'atoti-client-storage-gcp'.
2. **User Interface**: Develop a simple web-based UI using Flask where users can select their GCS bucket and file path.
3. **Data Retrieval**: Use 'atoti-client-gcp' (or its replacement) to download the dataset from GCS into your application.
4. **Data Processing**: Implement functions within the app to clean and preprocess the data using pandas.
5. **Statistical Analysis**: Provide options for users to choose different statistical analyses such as mean, median, mode, standard deviation, etc., and display these results.
6. **Visualization**: Integrate visualization libraries like matplotlib or seaborn to create charts based on user selection of data columns and types of analysis.
7. **Results Display**: Design a section of the dashboard to show the visualized data and statistical results.
8. **Error Handling & Logging**: Include robust error handling to manage issues during data retrieval or processing, and implement logging for tracking purposes.
9. **Deployment**: Once the application is developed, deploy it using a cloud service provider like AWS or Google Cloud Platform.

Suggested Features:
- Support multiple file formats (CSV, Excel, Parquet).
- Allow users to specify which columns they want to analyze.
- Provide a feature to save the results of the analysis locally or back to GCS.
- Implement authentication for accessing GCS buckets securely.

💬 Discussion Feed

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