airflow-mcd

v0.3.12 safe
3.0
Low Risk

Monte Carlo's Apache Airflow Provider

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators, with no network calls, obfuscation, or credential harvesting detected. Shell execution is noted but seems legitimate for integration purposes.

  • Shell risk due to potential command execution
  • Maintainer has only one package, possibly new or less active
Per-check LLM notes
  • Network: No network calls detected, which is normal and does not indicate risk.
  • Shell: Detection of shell execution suggests the package might be executing external commands, potentially for integration purposes, but further investigation is needed to confirm legitimacy.
  • Obfuscation: No obfuscation patterns detected, suggesting low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package has no suspicious HTTPS links and does not appear to be typosquatting. The maintainer has only one package, which could indicate a new or less active account.

📦 Package Quality Overall: Low (4.8/10)

✦ High Test Suite 9.0

Test suite present — 12 test file(s) found

  • 12 test file(s) detected (e.g. test_dag.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (8945 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

  • 67 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

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • dbt command process = subprocess.Popen( cmd, env=env, stdout=su
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: montecarlodata.com

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://www.apache.org/licenses/LICENSE-2.0
✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Monte Carlo Data, Inc" 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 airflow-mcd 
Create a data pipeline monitoring tool using the 'airflow-mcd' package, which is Monte Carlo's Apache Airflow Provider. This tool will help users monitor the health and performance of their data pipelines in real-time. The application should allow users to set up alerts for anomalies detected in their data pipelines, visualize the lineage of data flow within the pipelines, and provide historical insights into the pipeline's execution history. Here’s a step-by-step guide on how to build this application:

1. **Setup Environment**: Begin by setting up your Python environment. Ensure you have installed the necessary packages including 'airflow', 'montecarlo-airflow-provider' (renamed as 'airflow-mcd' for this exercise), and any other dependencies required for data visualization.
2. **Integrate Airflow-MCD**: Integrate the 'airflow-mcd' package into your existing Apache Airflow setup. Configure it to automatically send metadata about your DAGs, tasks, and their executions to Monte Carlo for analysis.
3. **Design User Interface**: Develop a simple web-based user interface where users can log in, view the status of their data pipelines, set up alert thresholds, and receive notifications when these thresholds are breached.
4. **Alert System**: Implement a feature that allows users to define custom alerts based on various metrics such as latency, error rates, or data volume changes. When an anomaly is detected, users should receive real-time notifications via email or SMS.
5. **Data Lineage Visualization**: Utilize the lineage capabilities provided by 'airflow-mcd' to create interactive visualizations showing how data flows through different stages of your pipeline. Users should be able to click on specific nodes to get more details about the task, its input/output data, and execution times.
6. **Historical Insights**: Provide historical data analysis features that allow users to review past pipeline runs, identify trends over time, and compare performance metrics across different runs or environments.
7. **Testing & Deployment**: Thoroughly test your application in a staging environment before deploying it to production. Ensure all functionalities work as expected and there are no security vulnerabilities.
8. **Documentation**: Finally, write comprehensive documentation for both end-users and developers explaining how to use the application, set up new pipelines, configure alerts, and troubleshoot common issues.

💬 Discussion Feed

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