apache-airflow

v3.2.2 safe
3.0
Low Risk

Programmatically author, schedule and monitor data pipelines

🤖 AI Analysis

Final verdict: SAFE

The package has minimal risks as it does not engage in any network calls, shell executions, or obfuscations. While there are some concerns with metadata, these do not suggest malicious activity.

  • No network calls
  • No shell execution
  • No obfuscation
  • Some concerns with metadata
Per-check LLM notes
  • Network: No network calls detected, which is normal and expected unless the package requires external services.
  • Shell: No shell execution patterns detected, indicating no direct system command execution from the package.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: Some concerns with author details and non-secure links, but no clear indicators of malicious intent.

📦 Package Quality Overall: Medium (6.4/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://airflow.apache.org/docs/
  • Detailed PyPI description (14061 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

  • Type checker (mypy / pyright / pytype) referenced in project
✦ 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

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: airflow.apache.org>

⚠ Suspicious Page Links score 4.0

Found 2 suspicious link(s) on the package page

  • Non-HTTPS external link: http://www.apache.org/licenses/LICENSE-2.0
  • Non-HTTPS external link: http://airflow.apache.org/docs/apache-airflow-providers/index.html
✓ 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 
Create a mini-application using Apache Airflow that automates the process of data ingestion from multiple sources into a central database. This application will serve as a basic ETL (Extract, Transform, Load) pipeline management tool. Here are the steps and features you should include:

1. **Setup**: Install and configure Apache Airflow on your local machine or a cloud environment. Ensure you have the necessary dependencies installed.
2. **Data Sources**: Define at least three different data sources such as CSV files, a MySQL database, and an API endpoint. Each source should represent a different type of data (e.g., sales data, customer information, and product details).
3. **DAGs Creation**: Create Directed Acyclic Graphs (DAGs) for each data source. These DAGs should outline the tasks required to extract data from their respective sources, transform it into a uniform format, and load it into a PostgreSQL database.
4. **Scheduling**: Set up scheduling for each DAG to run at specific intervals (daily, hourly, etc.). Use Airflow's scheduler to manage these tasks.
5. **Monitoring and Logging**: Implement monitoring and logging functionalities within Airflow to track the status of each task and DAG. Ensure logs are stored and accessible for debugging purposes.
6. **User Interface**: Utilize Airflow’s web interface to visualize the DAGs and monitor the execution of tasks in real-time.
7. **Error Handling**: Incorporate error handling mechanisms to manage exceptions during data extraction and loading processes. Tasks should be retried under certain conditions.
8. **Documentation**: Provide comprehensive documentation explaining how to set up the environment, run the DAGs, and troubleshoot common issues.

This project aims to demonstrate the power of Apache Airflow in managing complex data workflows efficiently and effectively.

💬 Discussion Feed

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