alation-data-quality-sdk

v1.0.7 suspicious
5.0
Medium Risk

Production-ready SDK for running Alation data quality checks using Soda Core

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in terms of obfuscation and credential handling but has a moderate metadata risk due to lack of transparency in authorship and repository linkage.

  • Low obfuscation risk
  • Low credential risk
  • Moderate metadata risk due to sparse author information and missing GitHub repository
Per-check LLM notes
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, suggesting secure handling of sensitive information.
  • Metadata: The package has no associated GitHub repository and the author information is sparse, indicating potential unreliability.

📦 Package Quality Overall: Low (4.6/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 2 test file(s) detected (e.g. client_test.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://www.alation.com/docs/en/latest/
  • Detailed PyPI description (10006 chars)
○ 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

  • 237 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

  • job_id self.session = requests.Session() def _get_job_id(self) -> Optional[int]: try:
  • imeout self.session = requests.Session() self.logger = get_logger(__name__) # JWT
  • oken/" response = requests.post(endpoint, headers=headers, data=data, timeout=self.timeout)
  • Response: response = requests.post(**kwargs) if request_name: trace_id = r
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • = simulator_app.__file__ subprocess.run(["streamlit", "run", streamlit_app_path]) def __execute_qu
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: alation.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 alation-data-quality-sdk 
Create a mini-application that leverages the 'alation-data-quality-sdk' package to perform comprehensive data quality checks on a dataset. Your application should allow users to upload a CSV file containing their data, specify which Soda Core checks they wish to apply, and generate a report detailing the results of these checks. Here’s a detailed outline of the steps and features your app should include:

1. **User Interface**: Design a simple yet intuitive UI where users can upload their CSV files. Ensure there's a section for specifying Soda Core checks such as completeness, uniqueness, format validation, etc.
2. **Data Processing**: Implement functionality within your app to read the uploaded CSV file into memory or a temporary database table. Utilize the 'alation-data-quality-sdk' package to run the specified Soda Core checks against this data.
3. **Check Results**: After running the checks, compile the results into a human-readable report. This report should highlight any issues found during the checks and provide suggestions for remediation if possible.
4. **Report Generation**: Allow users to download the generated report in PDF or HTML format for easy sharing and review.
5. **Error Handling & Feedback**: Ensure robust error handling to gracefully manage cases where the uploaded file is not valid or the specified checks cannot be applied. Provide clear feedback messages to guide users through potential issues.
6. **Optional Advanced Features**: Consider adding features like scheduling regular data quality checks, comparing results over time, or integrating with external alerting systems for critical issues.

Your task is to write the code from scratch, focusing on modular design and efficient use of the 'alation-data-quality-sdk' package. Remember to document your code thoroughly and include comments explaining key decisions and implementation details.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!