azure-kusto-data

v6.0.4 safe
2.0
Low Risk

Kusto Data Client

🤖 AI Analysis

Final verdict: SAFE

The package shows low risks across all categories, with no indications of malicious behavior or supply-chain attacks.

  • Low network risk
  • No shell execution detected
  • Code is not obfuscated
  • No credential harvesting observed
  • Single package from author, no additional suspicious activities
Per-check LLM notes
  • Network: The network call pattern is expected as it initializes a session for making HTTP requests, likely for interacting with Azure Kusto service.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected, indicating normal and transparent code practices.
  • Credentials: No credential harvesting patterns detected, suggesting secure handling of sensitive information.
  • Metadata: The author has only one package, which could indicate a new or less active account, but no other suspicious activities were flagged.

📦 Package Quality Overall: Medium (5.6/10)

○ 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 (3056 chars)
◈ Medium Contributing Guide 7.0

Some contribution signals present

  • Governance file: security.py
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 155 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 score 1.5

Found 1 network call pattern(s)

  • oling self._session = requests.Session() adapter = HTTPAdapterWithSocketOptions(
✓ 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-data 
Create a data analysis tool using Python that interacts with Azure Data Explorer (Kusto) clusters to retrieve, analyze, and visualize log data. Your task is to develop a mini-application that connects to a specified Kusto database, queries it for recent log entries, processes these entries to extract meaningful insights, and finally visualizes the results in an interactive dashboard.

### Steps to Complete the Project:
1. **Setup Environment:** Ensure you have Python installed along with the `azure-kusto-data` and `azure-kusto-ingest` packages. Additionally, install necessary visualization libraries like Matplotlib or Plotly.
2. **Connection Setup:** Use the `azure-kusto-data` library to establish a secure connection to your Azure Data Explorer cluster. This involves specifying the cluster URL and providing authentication credentials (AAD).
3. **Query Execution:** Write KQL (Kusto Query Language) queries to fetch relevant log data from the Kusto database. Your query should be dynamic enough to allow filtering based on date ranges or specific event types provided as input parameters.
4. **Data Processing:** After fetching the logs, process the raw data to derive insights such as frequency of events, common patterns, anomalies, etc. Implement functions to calculate metrics like average response times, error rates, etc.
5. **Visualization:** Utilize a visualization library to display the processed data in a user-friendly manner. Create graphs showing trends over time, pie charts for distribution analysis, and other relevant visual representations.
6. **User Interface:** Develop a simple command-line interface where users can specify the query parameters and view the results directly. Alternatively, create a basic web interface using Flask or Django if preferred.
7. **Documentation & Testing:** Document all code thoroughly and write tests to ensure the reliability of your application.

### Suggested Features:
- **Dynamic Filtering:** Allow users to filter logs based on date range, severity level, or specific event IDs.
- **Real-time Monitoring:** Implement a feature to continuously monitor new logs as they come in, updating the dashboard in real-time.
- **Custom Queries:** Provide a way for advanced users to input their own KQL queries directly.
- **Export Options:** Enable exporting the analyzed data into CSV or Excel formats.
- **Alert System:** Set up alerts for critical conditions detected during analysis, such as high error rates or unexpected spikes in traffic.

💬 Discussion Feed

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