apache-airflow-providers-dbt-cloud

v4.9.0 safe
3.0
Low Risk

Provider package apache-airflow-providers-dbt-cloud for Apache Airflow

🤖 AI Analysis

Final verdict: SAFE

The package shows no significant indicators of malicious activity. All risks assessed are low to moderate and do not suggest any supply-chain attack.

  • Low network risk due to common usage of aiohttp.ClientSession.
  • No shell execution detected.
Per-check LLM notes
  • Network: The use of aiohttp.ClientSession is common for making HTTP requests and does not inherently indicate malicious activity.
  • Shell: No shell execution patterns detected.
  • Obfuscation: The observed pattern is likely a standard method for extending package paths and not indicative of malicious obfuscation.
  • Credentials: No suspicious patterns indicating credential harvesting were found.
  • Metadata: The package has some minor issues but does not appear to be malicious.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 19 test file(s) found

  • Test runner config found: conftest.py
  • 19 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-dbt
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (4428 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
  • 59 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 1.5

Found 1 network call pattern(s)

  • ) async with aiohttp.ClientSession(headers=headers, timeout=timeout) as session: as
⚠ 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-dbt-cloud 
Create a data pipeline orchestration tool using Apache Airflow and the 'apache-airflow-providers-dbt-cloud' package. Your goal is to build a mini-application that automates the deployment of dbt (data build tool) projects on dbt Cloud. This application will allow users to define dbt Cloud jobs and schedules through an intuitive Airflow interface, streamlining the process of running data transformations and validations.

### Project Requirements:
1. **Setup Environment**: Ensure your development environment includes Python, Apache Airflow, and the 'apache-airflow-providers-dbt-cloud' package. Use version control (e.g., Git) to manage your codebase.
2. **Define DAGs**: Create Directed Acyclic Graphs (DAGs) within Airflow to represent the workflow of deploying dbt projects. Each DAG should include tasks for triggering dbt Cloud jobs, such as 'run', 'test', and 'snapshot'.
3. **Integrate dbt Cloud API**: Utilize the 'apache-airflow-providers-dbt-cloud' package to interact with the dbt Cloud API. Tasks within your DAGs should leverage this integration to authenticate and execute dbt Cloud jobs programmatically.
4. **Scheduling and Monitoring**: Implement scheduling capabilities so that dbt jobs can run at specified intervals (daily, hourly, etc.). Additionally, set up monitoring to track the status of these jobs and log any errors or successes.
5. **User Interface**: Develop a simple user interface where users can input their dbt Cloud job IDs and desired schedules. This UI should also display the status of ongoing and past runs, providing visibility into the health of the data pipelines.
6. **Error Handling and Notifications**: Include robust error handling to manage failures gracefully. Implement notification mechanisms (email, Slack, etc.) to alert stakeholders when issues arise.
7. **Documentation**: Provide comprehensive documentation detailing how to install, configure, and use your application. Include examples and best practices for integrating dbt Cloud jobs into existing data workflows.

### Features to Consider:
- Support for multiple dbt Cloud environments (e.g., dev, staging, prod).
- Ability to define complex workflows involving conditional execution based on previous task outcomes.
- Integration with other Airflow providers for enhanced functionality (e.g., connecting to databases, cloud storage services).
- Flexible scheduling options allowing for both cron-style expressions and time-based triggers.
- Detailed logging and audit trails for all operations performed through the system.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!