apache-airflow-providers-jdbc

v5.4.4 safe
1.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package has been thoroughly checked and shows no signs of malicious activity or potential supply-chain attack vectors.

  • No network calls detected
  • No shell execution patterns found
Per-check LLM notes
  • Network: No network calls detected, which is normal for this type of package.
  • Shell: No shell execution patterns detected, indicating no unexpected system command executions.
  • Obfuscation: The observed pattern is a common technique used for extending module search paths and does not indicate malicious obfuscation.
  • Credentials: No suspicious patterns indicating credential harvesting were found.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 8 test file(s) found

  • Test runner config found: conftest.py
  • 8 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-jdb
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (5242 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
  • 6 type-annotated function signatures (partial)
✦ 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-jdbc 
Create a data pipeline automation tool using Apache Airflow and the 'apache-airflow-providers-jdbc' package. This tool will facilitate the extraction of data from various relational databases (such as MySQL, PostgreSQL, etc.) and load it into a centralized data warehouse (such as Amazon Redshift). The project should include the following steps and features:

1. **Setup**: Install and configure Apache Airflow on your local machine or a cloud-based environment. Ensure you have the 'apache-airflow-providers-jdbc' package installed.
2. **Connection Management**: Use the 'apache-airflow-providers-jdbc' package to define connections to your source databases and target data warehouse. These connections should be securely managed within Airflow's connection management system.
3. **Data Extraction**: Write custom operators or use existing ones provided by the package to extract data from the source databases. Ensure these operators handle pagination and large datasets efficiently.
4. **Transformation**: Implement data transformation logic either within the Airflow DAGs or through intermediate steps. This could include cleaning, filtering, and aggregating data.
5. **Loading Data**: Develop tasks that utilize JDBC connections to load transformed data into the target data warehouse. Consider implementing error handling and retry mechanisms for failed loads.
6. **Scheduling & Monitoring**: Set up scheduling for your data pipeline jobs using Airflow's scheduler. Additionally, implement monitoring and alerting functionalities to notify stakeholders about any issues encountered during execution.
7. **Security & Compliance**: Ensure all data transfers and storage comply with relevant security standards. Use secure methods to store and manage database credentials.
8. **Documentation & Testing**: Provide comprehensive documentation detailing setup, usage, and maintenance of the data pipeline. Include unit tests for critical components of your pipeline to ensure reliability.

This project aims to demonstrate the power of Apache Airflow combined with the 'apache-airflow-providers-jdbc' package for building robust, scalable, and maintainable ETL pipelines.

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