apache-airflow-providers-airbyte

v5.5.0 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package shows low risks across all categories except for metadata where a non-HTTPS link and limited author information were noted. However, these do not strongly indicate malicious intent.

  • Low network and shell execution risks.
  • Metadata risk due to non-HTTPS link and limited author details.
Per-check LLM notes
  • Network: No network calls detected, which is normal for packages not requiring external API interactions.
  • Shell: No shell execution patterns detected, indicating no immediate risk of unauthorized system command execution.
  • Obfuscation: The observed pattern is likely part of the standard mechanism for extending package paths and not indicative of malicious activity.
  • Credentials: No suspicious patterns related to credential harvesting were detected.
  • Metadata: The presence of a non-HTTPS link and an author with limited information suggests potential issues, but there's no strong evidence of malicious intent.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 14 test file(s) found

  • Test runner config found: conftest.py
  • 14 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-air
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (3541 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
  • 17 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-airbyte 
Develop a data integration and automation tool using Apache Airflow and the 'apache-airflow-providers-airbyte' package. This tool will streamline the process of extracting data from various sources, transforming it as needed, and loading it into a target database system. The application will be designed to cater to businesses looking to consolidate data from multiple platforms into a single, accessible repository for analysis and reporting purposes.

**Project Scope:**
- **Data Sources:** The application should support at least three different data sources (e.g., Google Sheets, MySQL, and PostgreSQL).
- **Transformation Logic:** Implement basic data transformation capabilities such as filtering, aggregating, and joining datasets.
- **Target Database:** The transformed data will be loaded into a PostgreSQL database.
- **Scheduling:** Utilize Apache Airflow's scheduling capabilities to run the data integration workflows on a daily basis.
- **Monitoring:** Provide a simple monitoring dashboard to track the status of each workflow execution.

**Core Features Utilizing 'apache-airflow-providers-airbyte':**
1. **Data Extraction:** Use Airbyte connectors to extract data from supported sources.
2. **Transformation Pipelines:** Develop custom operators in Airflow to apply transformation logic to the extracted data.
3. **Loading Data:** Implement an operator to load the transformed data into the PostgreSQL database.
4. **Error Handling:** Include robust error handling mechanisms to manage failures during data extraction or loading processes.
5. **User Interface:** Design a basic web interface using Airflow's UI capabilities to allow users to monitor the progress and outcomes of their data integration tasks.

Your task is to design and implement these functionalities, ensuring that the application is scalable, maintainable, and user-friendly. Additionally, document your setup process, including how to install dependencies, configure Airflow, and set up Airbyte connectors.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!