apache-airflow-providers-datadog

v3.10.4 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package appears safe based on the analysis notes. It has low risks across all categories with no indications of malicious activities.

  • Low network and shell execution risk
  • No evidence of obfuscation or credential harvesting
  • Minor metadata concerns do not indicate malicious intent
Per-check LLM notes
  • Network: No network calls detected, which is unusual but not necessarily indicative of malicious activity without additional context.
  • Shell: No shell execution patterns detected, suggesting the package does not attempt to execute commands on the host system.
  • Obfuscation: The observed pattern is likely for extending module search path and not indicative of malicious activity.
  • Credentials: No patterns indicative of credential harvesting were detected.
  • Metadata: The package shows some minor red flags such as a non-secure link and incomplete maintainer information, but there are no clear signs of malicious intent or typosquatting.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 7 test file(s) found

  • Test runner config found: conftest.py
  • 7 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-dat
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (3493 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
  • 7 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-datadog 
Your task is to develop a small monitoring application using Apache Airflow along with the 'apache-airflow-providers-datadog' package. This application will monitor the health of your Airflow DAGs (Directed Acyclic Graphs) and send alerts via Datadog when certain conditions are met. Here’s a detailed breakdown of the project requirements:

1. **Setup Environment**: Start by setting up a Python virtual environment and installing necessary packages including Apache Airflow and 'apache-airflow-providers-datadog'. Ensure you have the correct version of Airflow compatible with this provider package.
2. **Define Monitoring Tasks**: Create two sample DAGs in Airflow. One should simulate a successful process (e.g., data ingestion from a CSV file), while the other simulates a failure scenario (e.g., attempting to read from a non-existent file).
3. **Integrate Datadog**: Use the 'apache-airflow-providers-datadog' package to integrate Datadog into your monitoring setup. Configure Datadog to receive logs and metrics from these DAGs.
4. **Alerting Mechanism**: Implement an alerting mechanism within your application. When a DAG fails, the application should automatically send an alert to Datadog. Alerts should include details like the DAG ID, execution date, and error message.
5. **Visualization**: Utilize Datadog's dashboard feature to visualize the status of your DAGs over time. Create at least one dashboard that shows the success rate of each DAG.
6. **Testing**: Write tests to ensure that your alerting system works as expected. Test both the successful and failure scenarios to verify that alerts are triggered correctly.
7. **Documentation**: Provide clear documentation on how to set up and run your application, including how to configure Datadog and Airflow.

This project will not only demonstrate your ability to use advanced monitoring tools but also show your proficiency in integrating different systems and handling errors gracefully.