apache-airflow-providers-snowflake

v6.13.0 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package is deemed safe based on the low risk scores across all categories. There are no clear signs of malicious activity.

  • Low credential risk
  • No shell execution detected
  • Base64 decoding present but common
Per-check LLM notes
  • Network: Network calls are expected for packages interacting with external services like Snowflake.
  • Shell: No shell execution patterns detected.
  • Obfuscation: Base64 decoding is commonly used for data serialization and may not indicate malicious activity unless used improperly.
  • Credentials: No patterns indicative of credential harvesting were detected.
  • Metadata: The package has a non-secure external link and an author with limited activity, raising some concerns but not strong indicators of malicious intent.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 30 test file(s) found

  • Test runner config found: conftest.py
  • 30 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-sno
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (5484 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
  • 84 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 score 4.5

Found 3 network call pattern(s)

  • """ response = requests.post( url, data=data, headers
  • " ) with requests.Session() as session: for attempt in Retrying(**self.ret
  • on_kwargs} async with aiohttp.ClientSession(**session_kwargs) as session: async for attempt
⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • private_key_pem = base64.b64decode(private_key_content) if private_key_pem:
  • 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-snowflake 
Create a small project that automates the process of managing data pipelines using Apache Airflow and the Snowflake provider package. Your project should include the following steps:

1. **Setup Environment**: Ensure your development environment includes Python, Apache Airflow, and the `apache-airflow-providers-snowflake` package.
2. **Data Pipeline Definition**: Define a data pipeline that involves extracting data from a source (such as a CSV file), transforming it (e.g., cleaning up data or adding new columns), and loading it into a Snowflake database. Use Apache Airflow DAGs (Directed Acyclic Graphs) to structure these tasks.
3. **Task Automation**: Automate the execution of the data pipeline using Apache Airflow's scheduler. Tasks should run at specific intervals, such as daily or hourly, based on your defined DAG schedule.
4. **Error Handling**: Implement error handling within your DAGs to manage failures gracefully. This might include retry logic, logging errors, and sending notifications via email or Slack when errors occur.
5. **Monitoring & Logging**: Set up monitoring and logging for your data pipeline to track its performance over time. This could involve setting up alerts for failed tasks or unusual patterns in task execution times.
6. **Security**: Ensure that sensitive information, like Snowflake credentials, is securely managed. Consider using Airflow's secrets backend feature for secure storage and retrieval of credentials.
7. **Visualization**: Optionally, visualize the workflow using Airflow's UI or integrate with other visualization tools to better understand the flow of data and task dependencies.

In this project, the `apache-airflow-providers-snowflake` package will be crucial for interacting with Snowflake. Specifically, it will be used to define operators that can execute SQL queries against Snowflake databases, handle connections to Snowflake, and manage data transfer between local sources and Snowflake.

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