apache-airflow-providers-microsoft-mssql

v4.6.0 safe
3.0
Low Risk

Provider package apache-airflow-providers-microsoft-mssql for Apache Airflow

🤖 AI Analysis

Final verdict: SAFE

The package is deemed safe based on the low risk scores across all categories, with no indications of malicious activities or supply-chain attacks.

  • No network or shell risks detected.
  • Minor obfuscation and metadata risks but not concerning.
Per-check LLM notes
  • Network: No network calls detected, which is normal for a database connector package like this one.
  • Shell: No shell execution patterns detected, which aligns with the expected behavior of a database connector.
  • Obfuscation: The observed pattern is likely a standard technique for extending module search paths and not indicative of malicious activity.
  • Credentials: No suspicious patterns related to credential harvesting were identified.
  • Metadata: The package has some minor issues but does not appear to be malicious or directly indicative of a supply-chain attack.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 21 test file(s) found

  • Test runner config found: conftest.py
  • 21 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-mic
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (4430 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
  • 15 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-microsoft-mssql 
Create a small data migration tool using Apache Airflow and the 'apache-airflow-providers-microsoft-mssql' package. This tool will help automate the process of transferring data from a Microsoft SQL Server database to another database (such as MySQL or PostgreSQL). Your task is to design a workflow that includes the following steps:

1. **Connection Setup**: Define connections to both the source (Microsoft SQL Server) and destination databases within Airflow. Ensure you use environment variables to securely store sensitive information such as usernames, passwords, and hostnames.
2. **Data Extraction**: Write a custom operator or utilize existing ones to extract data from the Microsoft SQL Server. Consider implementing error handling and retries if the extraction fails.
3. **Data Transformation**: Implement a transformation step where you can clean or modify the data before it is loaded into the destination database. This could include tasks like removing duplicates, converting data types, or filtering records.
4. **Data Loading**: Develop an operator to load the transformed data into the destination database. Again, ensure robust error handling and possibly implement batch loading strategies for efficiency.
5. **Logging and Monitoring**: Set up comprehensive logging for each task within the DAG (Directed Acyclic Graph) to monitor the progress and troubleshoot any issues. Use Airflow's built-in monitoring capabilities to visualize the workflow execution.
6. **Scheduling**: Schedule the entire workflow to run periodically based on your business requirements. This might involve setting up a cron expression or other scheduling mechanisms provided by Airflow.
7. **Testing**: Create a set of unit tests to verify the functionality of your operators and the overall workflow. Pay special attention to edge cases and potential failure points.
8. **Documentation**: Provide clear documentation explaining how to install, configure, and run the data migration tool. Include setup instructions for the required dependencies and any necessary configurations.

The 'apache-airflow-providers-microsoft-mssql' package plays a crucial role in this project by providing hooks and operators specifically designed for interacting with Microsoft SQL Server databases. Utilize these tools to streamline the data extraction process and ensure compatibility with the SQL Server environment.

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