apache-airflow-providers-samba

v4.12.5 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package has a low risk score due to minimal concerns identified, primarily related to metadata quality. It shows no signs of malicious intent.

  • No network calls or shell executions detected.
  • Minor metadata issues noted but do not indicate malicious activity.
Per-check LLM notes
  • Network: No network calls detected, which is normal and expected for this type of package.
  • Shell: No shell execution patterns detected, indicating the package does not execute external commands unexpectedly.
  • Obfuscation: The observed pattern is a common method for extending module search paths and does not indicate malicious obfuscation.
  • Credentials: No patterns indicative of credential harvesting were detected.
  • Metadata: The package shows some minor concerns like a missing author name and a non-secure link, but no strong indicators of malicious activity.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 10 test file(s) found

  • Test runner config found: conftest.py
  • 10 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-sam
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (3863 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
  • 8 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-samba 
Create a data processing pipeline using Apache Airflow that leverages the 'apache-airflow-providers-samba' package to manage data stored on Samba shares. This mini-application should automate the process of extracting data from a remote Samba share, transforming it into a more usable format, and then loading it into a local database for further analysis. Here’s a step-by-step guide on how to build this application:

1. **Set Up Your Environment**: Ensure you have Apache Airflow installed along with the 'apache-airflow-providers-samba' package. Also, configure your environment to connect to the Samba share.
2. **Define DAG Structure**: Design a Directed Acyclic Graph (DAG) in Airflow to outline the workflow. The DAG should include tasks for data extraction, transformation, and loading.
3. **Data Extraction Task**: Use the 'SambaToGoogleSheetsOperator' (or equivalent operator provided by the package) to extract data from a specified Samba share directory. This task should read files from the Samba share and save them locally.
4. **Transformation Task**: Implement a Python function or script to transform the extracted data. This could involve cleaning the data, filtering out unnecessary columns, or converting data types as needed.
5. **Loading Data Task**: Write a task that loads the transformed data into a local SQLite database. This task should use SQLAlchemy or similar ORM tools to facilitate the data loading process.
6. **Error Handling and Logging**: Integrate error handling mechanisms within each task to ensure robustness. Additionally, set up logging to track the progress and any issues encountered during execution.
7. **Testing and Deployment**: Before deploying the DAG, thoroughly test each component to ensure everything works as expected. Once tested, deploy the DAG to your Airflow instance for automated execution.
8. **Monitoring and Maintenance**: Set up monitoring to keep an eye on the DAG’s performance and health. Regularly update the DAG as necessary to adapt to changes in the data or requirements.

**Suggested Features**:
- Ability to schedule the DAG to run at regular intervals (e.g., daily).
- Support for multiple file formats (CSV, Excel, etc.) when extracting data from the Samba share.
- Option to specify different transformations based on the source file type.
- Integration with alerts via email or Slack for critical errors.
- Detailed documentation and examples to help new users get started quickly.

💬 Discussion Feed

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