apache-airflow-providers-odbc

v4.12.2 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package shows low risks across all categories with only minor issues noted in metadata. There are no indications of malicious activities or supply-chain attacks.

  • Low network and shell risk
  • Minor obfuscation and metadata issues
  • No credential risk detected
Per-check LLM notes
  • Network: No network call patterns detected, which is normal for this package as it's primarily a library for Apache Airflow and doesn't require external communications.
  • Shell: No shell execution patterns detected, aligning with the expected behavior of a package designed to integrate with database systems via ODBC without executing arbitrary shell commands.
  • Obfuscation: The observed pattern is likely used for extending package paths and not indicative of malicious obfuscation.
  • Credentials: No patterns indicating credential harvesting were detected.
  • 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 — 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-odb
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (3836 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
  • 3 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 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-odbc 
Create a mini-application using Apache Airflow that automates the process of extracting data from various ODBC-compliant databases and loading it into a central data warehouse. This application will serve as a simple ETL (Extract, Transform, Load) tool, designed to streamline data integration tasks for a small business or department within an organization. Your task is to design and implement this tool, ensuring it includes the following features:

1. **Database Connection Management**: Utilize the `apache-airflow-providers-odbc` package to establish connections with multiple ODBC-compliant databases such as SQL Server, MySQL, PostgreSQL, etc. Ensure your DAGs (Directed Acyclic Graphs) are capable of dynamically connecting to these databases based on configuration settings.
2. **Data Extraction**: Implement operators within your DAGs that can extract data from these databases. This could involve querying tables or views directly, or pulling specific datasets based on predefined criteria.
3. **Transformation Logic**: Include basic transformation logic within your workflow to clean and prepare the extracted data before loading it into the warehouse. This might include handling missing values, converting data types, or applying custom transformations as required.
4. **Loading Data**: Design operators that load the transformed data into a central data warehouse. This warehouse could be another ODBC-compliant database or a different type of storage solution like Amazon S3, depending on your choice.
5. **Error Handling & Logging**: Ensure robust error handling and logging mechanisms are in place to monitor the health of each job in the DAG. Logs should provide insights into any failures, retries, or successes.
6. **Scheduling & Monitoring**: Configure your DAGs to run on a schedule defined by the user, such as daily, hourly, or weekly. Additionally, set up monitoring capabilities so users can track the progress of their jobs and receive alerts if something goes wrong.
7. **User Interface (Optional)**: While not mandatory, consider building a simple web interface using Airflow’s UI capabilities to allow non-technical users to manage and monitor their ETL jobs without needing direct access to the Airflow backend.

Your application should demonstrate proficiency in using the `apache-airflow-providers-odbc` package to connect to various databases and handle complex data workflows efficiently. Focus on making your code modular, reusable, and well-documented to facilitate future enhancements or changes.

💬 Discussion Feed

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