azure-kusto-ingest

v6.0.4 safe
2.0
Low Risk

Kusto Ingest Client

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risks with no detected malicious activities such as network calls, shell executions, or credential harvesting. The presence of a non-HTTPS link is noted but does not indicate malicious intent.

  • No network calls detected
  • No shell execution patterns
  • No credential harvesting patterns
  • Non-HTTPS link present but not indicative of malicious intent
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external API interactions.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
  • Metadata: The package shows no signs of typosquatting or suspicious email domains. The non-HTTPS link is concerning but not indicative of malicious intent alone.

📦 Package Quality Overall: Low (4.6/10)

○ Low Test Suite 1.0

No test suite detected

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

Some documentation present

  • Detailed PyPI description (1935 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

  • 68 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 14 unique contributor(s) across 100 commits in Azure/azure-kusto-python
  • 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: microsoft.com>

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://jupyter.org/
✓ Git Repository History

Repository Azure/azure-kusto-python appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Microsoft Corporation" 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 azure-kusto-ingest 
Create a Python-based mini-application that allows users to ingest data into Azure Data Explorer (Kusto) from various sources such as CSV files, JSON files, and real-time data streams. Your application will leverage the 'azure-kusto-ingest' package to handle the ingestion process efficiently. Here’s a detailed breakdown of the steps and features your application should include:

1. **Data Source Selection**: Allow users to select from different types of data sources (CSV, JSON, Real-time stream). Provide a simple UI or command-line interface for easy selection.
2. **Connection Setup**: Guide users through setting up a connection to their Azure Data Explorer cluster and database using the 'azure-kusto-ingest' package. Ensure security best practices are followed, including handling credentials securely.
3. **Data Transformation**: Implement functionality to transform incoming data based on user-defined rules. For example, users might want to rename columns, add calculated fields, or filter out certain rows before ingesting.
4. **Ingestion Process**: Utilize the 'azure-kusto-ingest' package to perform the actual data ingestion. Demonstrate how to handle different data formats and ensure the data integrity during the transfer.
5. **Error Handling and Logging**: Integrate robust error handling and logging mechanisms to capture any issues during the ingestion process. This could include retry logic for failed batches, logging errors to a file, and alerting users via email or SMS.
6. **Performance Monitoring**: Implement basic performance monitoring to track the speed and efficiency of the data ingestion process. Display metrics like time taken to ingest data, number of rows processed per second, etc.
7. **User Interface (Optional)**: Develop a simple web-based UI using Flask or Django to make the application more accessible and user-friendly. Alternatively, a well-documented CLI can also serve this purpose.
8. **Documentation**: Provide comprehensive documentation explaining how to install and use the application, along with examples and best practices.

This project aims to showcase the power and flexibility of the 'azure-kusto-ingest' package while offering a practical tool for developers and data engineers working with Azure Data Explorer.

💬 Discussion Feed

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