apache-airflow-providers-apache-pig

v4.8.4 safe
3.0
Low Risk

Provider package apache-airflow-providers-apache-pig for Apache Airflow

🤖 AI Analysis

Final verdict: SAFE

The package is deemed safe with no clear signs of malicious activity. It has legitimate functionalities such as executing shell commands for Pig job execution.

  • No network calls detected
  • Potential shell execution requires further review for sanitization and validation
Per-check LLM notes
  • Network: No network calls detected, which is normal and does not raise suspicion.
  • Shell: Detection of shell execution suggests the package may execute external commands, potentially for Pig job execution. This is likely legitimate functionality but should be reviewed for proper sanitization and input validation to prevent command injection.
  • Obfuscation: The observed pattern is likely for path manipulation and not indicative of malicious obfuscation.
  • Credentials: No suspicious patterns related to credential harvesting were found.
  • Metadata: The package shows some minor red flags but lacks significant indicators of malicious intent.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 12 test file(s) found

  • Test runner config found: conftest.py
  • 12 test file(s) detected (e.g. conftest.py)
✦ High Documentation 9.0

Well-documented package

  • Documentation URL: "Documentation" -> https://airflow.apache.org/docs/apache-airflow-providers-apa
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (3454 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
  • 4 type-annotated function signatures (partial)
✦ 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)

  • under the License. __path__ = __import__("pkgutil").extend_path(__path__, __name__) # Licensed to the Apache S
⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • sub_process: Any = subprocess.Popen( pig_cmd, stdout=subprocess.PIPE, stderr=sub
✓ 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-providers-apache-pig 
Create a data processing pipeline using Apache Airflow and the 'apache-airflow-providers-apache-pig' package. This pipeline will serve as a mini-app designed to automate the extraction of raw data from a database, perform complex data transformations using Apache Pig, and then store the processed data back into another database or file system. Here’s a detailed breakdown of the steps and features you should include in your project:

1. **Setup**: Begin by setting up a local environment where Apache Airflow is installed alongside the 'apache-airflow-providers-apache-pig' package. Ensure all dependencies are correctly configured.
2. **Data Extraction Task**: Implement a task that extracts raw data from a MySQL database. This task should be flexible enough to handle different SQL queries and should be able to read data from multiple tables if needed.
3. **Apache Pig Transformation**: Use Apache Pig scripts to perform complex data transformations on the extracted data. These transformations could include filtering, joining, aggregating, and more. Ensure that the transformations are efficient and optimized for large datasets.
4. **Data Storage Task**: After transformations, implement a task to store the processed data back into a PostgreSQL database or a CSV file. This task should also be configurable to support different storage formats and locations.
5. **Scheduling and Monitoring**: Set up scheduling for the tasks using Apache Airflow's DAG (Directed Acyclic Graph) capabilities. Define intervals for running the pipeline (e.g., daily, hourly) and set up monitoring to track the status of each task.
6. **Error Handling and Logging**: Integrate error handling mechanisms within the tasks to gracefully manage failures and retries. Additionally, implement logging to record the execution details and errors for auditing and debugging purposes.
7. **User Interface**: Develop a simple web-based UI (using Flask or Django) that allows users to trigger the pipeline manually, view logs, and monitor the progress of tasks in real-time.
8. **Documentation**: Provide comprehensive documentation that includes setup instructions, usage guides, and examples. This documentation should be easy to follow and should cater to both beginners and advanced users.

This project aims to demonstrate the power and flexibility of Apache Airflow when combined with Apache Pig for handling big data workflows. It’s not just about building a functional tool but also about showcasing best practices in data engineering and automation.

💬 Discussion Feed

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