atoti-client-directquery-redshift

v0.9.15 safe
2.0
Low Risk

Code to use DirectQuery on Redshift

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators with no network calls, shell executions, obfuscations, or credential risks. The metadata suggests a single-package maintainer, which could indicate a new or less active developer.

  • No network calls
  • No shell execution patterns
  • No obfuscation patterns
  • No credential harvesting patterns
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires network interaction to function properly.
  • Shell: No shell execution patterns detected, indicating no immediate risk of executing system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, suggesting no immediate risk of secret theft.
  • Metadata: The maintainer has only one package, suggesting a potentially new or less active account.

📦 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-redshift 
Create a data analytics dashboard using Python that integrates directly with Amazon Redshift using the 'atoti-client-directquery-redshift' package. This mini-application will allow users to query large datasets stored in Redshift and visualize the results in real-time without needing to download or pre-process the data locally. Here’s a detailed breakdown of the project requirements:

1. **Setup**: Install the necessary packages including 'atoti-client-directquery-redshift', 'pandas', 'matplotlib', and 'seaborn'.
2. **Connection**: Establish a connection to your Amazon Redshift cluster using the 'atoti-client-directquery-redshift' package. Ensure you have the correct credentials and database parameters ready.
3. **Data Exploration**: Write SQL queries to explore your dataset directly within the application. Use the package's capabilities to execute these queries and fetch results.
4. **Visualization**: Utilize 'matplotlib' and 'seaborn' to create interactive visualizations based on the fetched data. Consider implementing features such as line graphs, bar charts, pie charts, and heatmaps.
5. **User Interface**: Develop a simple user interface where users can input their SQL queries, view the execution status, and see the resulting visualizations. Consider using 'tkinter' for a basic GUI or 'streamlit' for a more advanced web-based interface.
6. **Advanced Features**: Implement additional functionalities like saving visualizations as images, exporting query results to CSV files, and providing query history.
7. **Documentation**: Provide clear documentation explaining how to install dependencies, run the application, and interpret the visual outputs.

The goal is to create a versatile tool that simplifies the process of querying and visualizing data from Amazon Redshift, making it accessible even to those without extensive knowledge of SQL or data visualization tools.

💬 Discussion Feed

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