apache-airflow-providers-openlineage

v2.17.0 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators, with only minor concerns related to metadata and obfuscation practices that do not suggest malicious activity.

  • Low network and shell execution risks.
  • Minor obfuscation and metadata issues but no signs of malicious intent.
Per-check LLM notes
  • Network: No network calls detected, which is normal for a library focused on Airflow and OpenLineage integration.
  • Shell: No shell execution patterns detected, aligning with the expected behavior of a data processing library.
  • Obfuscation: The observed obfuscation patterns appear to be standard Python practices rather than malicious attempts.
  • Credentials: No evidence of credential harvesting activities has been detected.
  • Metadata: The package has some minor issues with maintainer history and a non-HTTPS external link, but no clear signs of malicious intent.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 30 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: conftest.py
  • 30 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-ope
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (4215 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
  • 159 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 4.0

Found 2 obfuscation pattern(s)

  • under the License. __path__ = __import__("pkgutil").extend_path(__path__, __name__) # Licensed to the Apache S
  • ance.__class__ instance = pickle.loads(pickle.dumps(instance)) for field in attrs.fields(cls):
✓ 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-openlineage 
Your task is to create a mini-application that integrates with Apache Airflow using the 'apache-airflow-providers-openlineage' package to track lineage of data processing tasks. This application will serve as a simple yet powerful tool for monitoring and understanding the flow of data within your organization's workflows.

### Application Overview:
- **Name**: Data Lineage Tracker
- **Purpose**: To provide a visual representation of data lineage within Airflow DAGs (Directed Acyclic Graphs) by leveraging the OpenLineage standard.
- **Features**:
  - Automatically detect and report data ingestion, transformation, and output operations.
  - Visualize lineage relationships between datasets.
  - Support for multiple data sources such as databases, cloud storage, and ETL tools.
  - User-friendly dashboard for viewing lineage information.
  - Alerting mechanism for lineage changes or anomalies.

### Steps to Build the Application:
1. **Setup Environment**:
   - Install necessary packages including 'apache-airflow', 'apache-airflow-providers-openlineage', and any additional dependencies required for data sources you plan to support.
2. **Define Data Sources**:
   - Configure connections to your data sources within Airflow.
3. **Create Airflow DAGs**:
   - Develop DAGs that include operators for ingesting data, transforming it, and storing the results. Ensure these DAGs emit OpenLineage events.
4. **Integrate OpenLineage**:
   - Use the 'apache-airflow-providers-openlineage' package to automatically capture lineage events from your DAGs.
5. **Build Visualization Tool**:
   - Implement a frontend dashboard that visualizes the captured lineage data. This could be a simple web application using technologies like Flask or Django.
6. **Testing and Validation**:
   - Test the application with sample DAGs and data sources to ensure accurate lineage tracking.
7. **Deployment**:
   - Deploy the application to a staging environment before moving it to production.
8. **Monitoring and Maintenance**:
   - Set up monitoring to alert on any issues with lineage tracking or data processing.

### Utilization of 'apache-airflow-providers-openlineage':
This package allows your application to seamlessly integrate with OpenLineage, enabling automatic detection and reporting of data lineage. It provides the necessary hooks and operators to emit lineage events from Airflow tasks, making it easier to understand the flow of data through various processes. By utilizing this package, your application can offer valuable insights into how data moves through different stages of processing, aiding in compliance, debugging, and optimization efforts.

💬 Discussion Feed

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