astronomer-cosmos

v1.14.2 safe
4.0
Medium Risk

Orchestrate your dbt projects in Airflow

🤖 AI Analysis

Final verdict: SAFE

The package shows low risks across most categories with no direct evidence of malicious activities. The incomplete metadata and potential telemetry data transmission are minor concerns.

  • Incomplete maintainer information
  • Potential telemetry data transmission
Per-check LLM notes
  • Network: The detected network call pattern suggests the package may be sending telemetry data, which is common but should be reviewed for sensitivity.
  • Shell: No shell execution patterns detected.
  • Obfuscation: The use of base64 and zlib for encoding/decoding suggests obfuscation but may be legitimate for data compression and storage purposes.
  • Credentials: No clear patterns indicative of credential harvesting were detected.
  • Metadata: The maintainer's author information is incomplete and the account seems new or inactive, which raises some concerns but does not strongly indicate malicious intent.

📦 Package Quality Overall: Medium (6.4/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://astronomer.github.io/astronomer-cosmos
  • Detailed PyPI description (5989 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 518 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 12 unique contributor(s) across 100 commits in astronomer/astronomer-cosmos
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • ) try: response = httpx.get(telemetry_url, timeout=constants.TELEMETRY_TIMEOUT, follow_r
⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

  • encoded_data = base64.b64decode(dbt_ls_compressed.encode()) cache_dict["dbt_
  • encoded_raw = base64.b64decode(raw_selectors_compressed.encode()) raw_selec
  • encoded_parsed = base64.b64decode(parsed_selectors_compressed.encode()) parsed
  • compressed_b64_sql = base64.b64decode(compressed_b64_sql) sql_query = zlib.decompress(comp
  • = json.loads(zlib.decompress(base64.b64decode(compressed.encode("utf-8"))).decode("utf-8")) for key, v
  • de("ascii")) json_bytes = zlib.decompress(compressed_bytes) result: dict[str, Any] = json.loads(js
✓ 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: astronomer.io>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository astronomer/astronomer-cosmos 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 astronomer-cosmos 
Create a mini-application that integrates dbt (data build tool) projects into Apache Airflow using the 'astronomer-cosmos' package. This application will serve as a bridge between dbt workflows and Airflow, enabling data engineers to manage their dbt projects within the Airflow environment more efficiently. Here’s a detailed plan on how to approach this project:

1. **Project Setup**: Start by setting up a virtual environment and installing necessary packages including 'astronomer-cosmos', 'dbt-core', and 'apache-airflow'. Ensure you have the required dependencies installed.
2. **Airflow DAG Creation**: Use 'astronomer-cosmos' to create a DAG that triggers a dbt run. This DAG should be able to detect changes in the dbt project files and trigger a new dbt run automatically when changes are detected.
3. **Customization Options**: Allow users to customize the dbt profile and target environments directly within the Airflow UI. This feature will enable users to switch between different database targets easily without modifying the dbt project configuration files manually.
4. **Logging and Monitoring**: Implement logging and monitoring capabilities to track the execution of dbt tasks within Airflow. Users should be able to view logs and monitor the progress of dbt runs directly from the Airflow web interface.
5. **Error Handling and Notifications**: Add error handling mechanisms that notify users via email or Slack when a dbt task fails. This ensures that any issues are promptly addressed.
6. **Security Enhancements**: Integrate security measures such as encrypted secrets management for sensitive information like database credentials, ensuring that the dbt project remains secure while being orchestrated through Airflow.
7. **Documentation and User Guide**: Provide comprehensive documentation and a user guide that explains how to set up and use the application, including examples and best practices.

This project aims to streamline the process of managing dbt projects within Airflow, making it easier for data teams to integrate their data transformations and modeling workflows with Airflow's robust orchestration capabilities.

💬 Discussion Feed

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