apache-airflow-core

v3.2.2 safe
3.0
Low Risk

Core packages for Apache Airflow, schedule and API server

🤖 AI Analysis

Final verdict: SAFE

The package shows low risk indicators across all categories with no signs of malicious intent or supply-chain attack.

  • No network calls detected
  • Common shell execution for operational purposes
  • Likely benign obfuscation techniques
  • No evidence of credential harvesting
Per-check LLM notes
  • Network: No network calls detected, which is normal and not indicative of malicious activity.
  • Shell: Shell execution is present but not inherently malicious; it may be used for executing commands within the context of Apache Airflow's operations.
  • Obfuscation: The observed pattern is likely used for extending the package path and is common practice in Python packaging, not indicative of malicious activity.
  • Credentials: No suspicious patterns indicating credential harvesting were detected.
  • Metadata: The package has some minor issues but no strong indicators of malice.

📦 Package Quality Overall: Medium (5.8/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://airflow.apache.org/docs/
  • Detailed PyPI description (929 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 353 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 46 unique contributor(s) across 100 commits in apache/airflow
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • the import paths. __path__ = __import__("pkgutil").extend_path(__path__, __name__) __version__ = "3.2.2" im
⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • urns stdout.""" process = subprocess.Popen( shlex.split(command), stdout=subprocess.PIPE, stder
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: airflow.apache.org>

⚠ 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

Repository apache/airflow appears legitimate

⚠ 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 apache-airflow-core 
Your task is to develop a fully-functional mini-application using the 'apache-airflow-core' package. This application will serve as a simple scheduler and monitoring tool for a series of tasks that could simulate data processing workflows common in Data Engineering projects. Here’s a step-by-step guide on what your application should achieve:

1. **Setup**: Begin by setting up a Python virtual environment and installing 'apache-airflow-core'. Ensure you have a working understanding of Airflow's architecture, including DAGs, Tasks, and Schedulers.
2. **DAG Creation**: Create two Directed Acyclic Graphs (DAGs): one for daily ETL processes and another for weekly reporting tasks. Each DAG should contain multiple tasks designed to mimic real-world operations like extracting data from a database, transforming it, and loading it into a target system.
3. **Task Types**: Define different types of tasks within your DAGs. For example, use BashOperators to simulate data extraction from a hypothetical source, PythonOperators to transform the data, and DummyOperators to represent placeholders for actual operations.
4. **Scheduling**: Configure the scheduling of these DAGs. The daily ETL process should run at a specific time every day, while the weekly reporting task should execute once a week on a chosen day.
5. **Monitoring**: Implement basic monitoring features within your application. This includes logging the status of each task (success, failure, skipped), and providing a simple interface to view these logs. Optionally, include alerts for failed tasks.
6. **API Integration**: Utilize the API server provided by 'apache-airflow-core' to expose endpoints for querying the status of tasks and DAGs. Consider implementing CRUD operations for managing DAGs via the API.
7. **Documentation**: Write comprehensive documentation for your application, explaining how to set it up, configure it, and use its features effectively.

This project not only leverages the 'apache-airflow-core' package but also provides practical experience in building scalable and maintainable data pipelines. Your goal is to create a versatile tool that could be adapted to various data processing needs.

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