apache-airflow-providers-exasol

v4.10.2 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package shows low risk indicators across all checks with only minor obfuscation and metadata concerns, suggesting it is safe to use.

  • Low network and shell risk
  • Minor obfuscation and metadata concerns
Per-check LLM notes
  • Network: No network calls detected, which is normal for most packages unless explicitly stated to have external dependencies.
  • Shell: No shell execution patterns detected, indicating the package does not perform any system-level command executions.
  • Obfuscation: The observed pattern is likely used for extending module search path and does not indicate malicious intent.
  • Credentials: No suspicious patterns related to credential harvesting were found.
  • Metadata: The package has some minor issues but no clear signs of malice.

📦 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-exa
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (4316 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 4.0

Found 2 obfuscation pattern(s)

  • under the License. __path__ = __import__("pkgutil").extend_path(__path__, __name__) # Licensed to the Apache S
  • under the License. __path__ = __import__("pkgutil").extend_path(__path__, __name__) # # Licensed to the Apache
✓ 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-exasol 
Develop a data pipeline management tool using Apache Airflow that integrates with Exasol databases. This tool will enable users to schedule and manage workflows that involve data extraction from various sources, transformation using Python scripts, and loading into an Exasol database. Here are the key steps and features of your project:

1. **Setup Environment**: Install necessary packages including `apache-airflow`, `apache-airflow-providers-exasol`, and other dependencies.
2. **Define Data Sources**: Identify at least two different data sources (e.g., CSV files, MySQL database) from which data will be extracted.
3. **Design Workflow**: Create an Airflow DAG (Directed Acyclic Graph) that defines the workflow steps: data extraction, data cleaning, and data loading into an Exasol database.
4. **Data Transformation**: Implement Python operators within Airflow to perform data cleaning and transformation tasks.
5. **Integration with Exasol**: Use the `apache-airflow-providers-exasol` package to establish connections with an Exasol database and load transformed data into it.
6. **Scheduling and Monitoring**: Configure Airflow to schedule the execution of the DAGs and monitor their progress through the Airflow web interface.
7. **Error Handling and Logging**: Implement robust error handling and logging mechanisms to ensure that any issues during execution are logged and can be reviewed later.
8. **User Interface**: Develop a simple UI using Flask or similar framework to allow users to trigger the data pipeline manually or view logs.

Your task is to create a fully functional mini-app that showcases the capabilities of integrating Apache Airflow with Exasol using the `apache-airflow-providers-exasol` package. Ensure that the application is well-documented and includes instructions on how to set up the environment, run the pipelines, and interpret the results.

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