airflow-provider-datris

v0.1.3 safe
1.0
Low Risk

Apache Airflow provider for Datris — trigger and monitor Datris taps from DAGs.

🤖 AI Analysis

Final verdict: SAFE

The package exhibits no signs of network, shell, or obfuscation risks and does not appear to harvest credentials, suggesting it is safe from a security perspective.

  • No network calls detected
  • No shell execution detected
  • No obfuscation patterns found
  • No credential harvesting detected
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution detected, indicating no direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, indicating secure handling of sensitive information.

📦 Package Quality Overall: Low (4.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 2 test file(s) detected (e.g. test_hook.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://docs.datris.ai/integrations/airflow
  • Detailed PyPI description (2788 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

  • 13 type-annotated function signatures detected in source
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 5 commits in datris/airflow-provider-datris
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ 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: datris.ai>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 7.5

Git history flags: Repository created very recently: 5 day(s) ago (2026-06-01T19:07:10Z)

  • Repository created very recently: 5 day(s) ago (2026-06-01T19:07:10Z)
  • Repository has zero stars and zero forks
  • All 5 commits happened within 24 hours
⚠ 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 airflow-provider-datris 
Create a data orchestration tool using Apache Airflow and the 'airflow-provider-datris' package. Your task is to design a mini-application that automates the process of extracting data from various sources and loading it into a central data warehouse. This tool will be particularly useful for businesses looking to streamline their ETL (Extract, Transform, Load) processes.

Step 1: Define the Data Sources and Target Warehouse
- Identify at least three different data sources (e.g., databases, APIs, flat files).
- Choose a target data warehouse where the extracted data will be loaded (e.g., Snowflake, Redshift).

Step 2: Set Up Apache Airflow Environment
- Install and configure Apache Airflow on your local machine or a cloud-based server.
- Ensure the 'airflow-provider-datris' package is installed and properly configured within your Airflow environment.

Step 3: Design the DAG (Directed Acyclic Graph)
- Create a DAG that includes tasks for each data source extraction.
- Use the 'airflow-provider-datris' package to define tasks that utilize Datris taps for data extraction.
- Implement transformation logic within Airflow tasks to clean and prepare the data before loading.
- Schedule the DAG to run daily or based on a specific time interval.

Step 4: Implement Error Handling and Monitoring
- Add error handling mechanisms to ensure robustness in case of failures during data extraction or loading.
- Utilize Airflow's monitoring capabilities to track the status of each task and the overall DAG execution.
- Configure alerts to notify stakeholders when errors occur or when data loads are successful.

Suggested Features:
- Support for multiple concurrent DAG executions to handle high-volume data processing.
- Integration with Airflow's web UI for easy visualization and management of DAGs and tasks.
- Automated retry mechanisms for failed tasks.
- Logging and auditing functionalities to maintain a record of all data movements and transformations.

Utilization of 'airflow-provider-datris':
- Leverage the 'airflow-provider-datris' package to seamlessly integrate Datris taps into your DAG tasks.
- Use Datris taps to efficiently extract data from various sources, ensuring compatibility and reliability.
- Monitor the performance and health of Datris taps through Airflow's monitoring tools, enhancing the overall reliability of your data pipeline.