apache-airflow-providers-alibaba

v3.3.8 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package shows low risk indicators across all categories with only minor issues in metadata and obfuscation, which do not suggest any malicious activities or supply-chain attacks.

  • Low network and shell risks
  • Minor obfuscation and metadata concerns
Per-check LLM notes
  • Network: No network calls detected, which is normal for a package focused on local operations and integration with Alibaba services.
  • Shell: No shell execution patterns detected, indicating the package does not perform system-level commands that could be exploited.
  • Obfuscation: The observed pattern is likely for path manipulation and not indicative of malicious activity.
  • Credentials: No credential harvesting patterns were detected.
  • Metadata: The package has some minor issues with maintainer history and a non-secure external link, but no clear signs of malicious intent.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 32 test file(s) found

  • Test runner config found: conftest.py
  • 32 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-ali
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (3884 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
  • 60 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)

  • under the License. __path__ = __import__("pkgutil").extend_path(__path__, __name__) # Licensed to the Apache S
✓ 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 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-alibaba 
Create a mini-application that leverages the 'apache-airflow-providers-alibaba' package to automate data processing tasks on Alibaba Cloud services. This application will serve as a bridge between local data sources and Alibaba Cloud services, enabling users to perform data extraction, transformation, and loading (ETL) operations seamlessly. Here are the key steps and features of your application:

1. **Setup**: Install Apache Airflow and the 'apache-airflow-providers-alibaba' package. Ensure you have the necessary credentials to access Alibaba Cloud services.
2. **Data Extraction**: Design a task to extract data from a local CSV file. This could represent incoming data from various sources such as user-generated content or external APIs.
3. **Data Transformation**: Implement a transformation process where you clean and manipulate the extracted data. For instance, you might want to convert certain fields into different formats or aggregate data based on specific criteria.
4. **Loading Data to Alibaba Cloud**: Utilize Alibaba Cloud services like OSS (Object Storage Service) or RDS (Relational Database Service) to store the transformed data. Write a task that uploads the processed data to these services.
5. **Scheduling**: Use Apache Airflow's DAGs (Directed Acyclic Graphs) to schedule the ETL process at regular intervals (e.g., daily).
6. **Monitoring and Alerts**: Integrate monitoring capabilities within your application so that it can notify users about the status of their data processing jobs. Include error handling to manage any issues that arise during the execution of tasks.
7. **Visualization**: Optionally, integrate a simple visualization component to display the progress of data processing tasks and any relevant metrics.

By utilizing the 'apache-airflow-providers-alibaba' package, you'll be able to leverage Alibaba Cloud's robust infrastructure for scalable and efficient data processing tasks. Your application will demonstrate the integration of local data workflows with cloud-based services, showcasing the power of combining Apache Airflow with Alibaba Cloud technologies.

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