apache-airflow-providers-common-io

v1.7.2 safe
3.0
Low Risk

Provider package apache-airflow-providers-common-io for Apache Airflow

🤖 AI Analysis

Final verdict: SAFE

The package has minimal risks associated with it as it does not make network calls, execute shell commands, or show signs of credential harvesting. While there are some minor issues noted in metadata and obfuscation, these do not indicate any malicious activity.

  • No network calls detected
  • No shell execution patterns
  • Minor obfuscation and metadata issues
Per-check LLM notes
  • Network: No network calls detected, which is normal for most packages unless they require external services.
  • Shell: No shell execution patterns detected, indicating the package does not attempt to execute system commands.
  • Obfuscation: The observed pattern is likely a standard method for extending package paths and not indicative of malicious obfuscation.
  • Credentials: No suspicious patterns for credential harvesting have been detected.
  • Metadata: The package shows some minor issues like missing author information and an insecure link, but no clear signs of malicious intent or typosquatting.

📦 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-com
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (3866 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
  • 12 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-common-io 
Create a data processing pipeline using Apache Airflow that leverages the 'apache-airflow-providers-common-io' package to manage various file operations across different data sources. Your task is to design a mini-application that can ingest data from multiple sources (e.g., S3, local filesystem), process it (e.g., filtering, transformation), and then store the processed data into a database or another file system.

**Steps to Create the Mini-Application:**
1. **Setup Environment**: Ensure you have Python 3.x installed along with Apache Airflow. Install the necessary packages including 'apache-airflow', 'apache-airflow-providers-common-io', and any other required dependencies for your data sources and storage solutions.
2. **Define Data Sources**: Specify at least two different data sources from where you will fetch the raw data. For instance, one could be a local directory containing CSV files, while another might be an AWS S3 bucket storing JSON files.
3. **Data Ingestion Task**: Use operators provided by 'apache-airflow-providers-common-io' to create tasks that read data from these sources. Pay attention to how you handle file formats and ensure data integrity during ingestion.
4. **Data Processing Task**: After ingestion, implement a series of data processing tasks. These could include filtering out unwanted rows, performing aggregations, or converting data types as needed.
5. **Data Storage Task**: Finally, use appropriate operators to write the processed data back into a chosen destination. This could be a relational database like PostgreSQL, or another file system such as Google Cloud Storage.
6. **Error Handling and Logging**: Integrate robust error handling mechanisms and logging throughout your pipeline to monitor its performance and troubleshoot issues efficiently.
7. **Testing and Validation**: Test each component of your pipeline independently before integrating them together. Validate the output against expected results to ensure accuracy.
8. **Documentation**: Provide comprehensive documentation on setting up the environment, running the pipeline, and understanding its components.

**Suggested Features**:
- Support for multiple data formats (CSV, JSON, Parquet).
- Ability to scale the number of parallel data ingestion tasks based on available resources.
- Integration with a monitoring tool to visualize the pipeline's execution status.
- Customizable data processing logic through configuration files.

By completing this project, you'll gain hands-on experience with Apache Airflow and its providers, specifically focusing on managing diverse data flows and transformations.

💬 Discussion Feed

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