apache-airflow-providers-apache-drill

v3.3.2 safe
4.0
Medium Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package shows low risk across multiple categories with only minor concerns about metadata reliability. There are no indications of malicious activities.

  • Low network and shell risks
  • Minimal obfuscation risk
  • No signs of credential harvesting
  • Non-secure external link and limited author info
Per-check LLM notes
  • Network: No network calls detected, which is normal for a package focused on integration with Apache Drill.
  • Shell: No shell execution patterns detected, indicating no immediate risk of executing arbitrary commands.
  • Obfuscation: The observed pattern is likely a standard practice for extending module paths and not indicative of malicious activity.
  • Credentials: No suspicious patterns indicating credential harvesting were found.
  • Metadata: The package has a non-secure external link and an author with limited information, suggesting potential unreliability.

📦 Package Quality Overall: Medium (7.4/10)

✦ High Test Suite 9.0

Test suite present — 16 test file(s) found

  • Test runner config found: conftest.py
  • 16 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-apa
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (3583 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

  • Type checker (mypy / pyright / pytype) referenced in project
✦ 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 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-apache-drill 
Create a mini-application that leverages Apache Airflow and the 'apache-airflow-providers-apache-drill' package to automate data extraction from Apache Drill and perform basic ETL operations. This application will serve as a bridge between Apache Drill and other data processing tools or storage systems, allowing for seamless data flow and analysis. Here’s a detailed breakdown of the steps and features you need to implement:

1. **Setup Environment**: Begin by setting up your development environment. Install Apache Airflow and the 'apache-airflow-providers-apache-drill' package. Ensure Apache Drill is also running and accessible.

2. **Define Data Sources**: Define one or more data sources within Apache Drill. These could be tables, views, or even external data sources that Drill supports.

3. **Create DAGs**: Develop Directed Acyclic Graphs (DAGs) in Apache Airflow that utilize operators provided by the 'apache-airflow-providers-apache-drill' package to extract data from Apache Drill. Each DAG should represent a specific workflow or task.

4. **ETL Operations**: Implement basic Extract, Transform, Load (ETL) operations within these DAGs. For example, extract data from Apache Drill, transform it by filtering, aggregating, or joining datasets, and then load it into another system such as a relational database or a file system.

5. **Scheduling & Monitoring**: Configure scheduling parameters for the DAGs to run at specified intervals (e.g., hourly, daily). Additionally, set up monitoring capabilities to track the execution status of each DAG and its tasks.

6. **User Interface**: Optionally, develop a simple user interface where users can select which DAGs to trigger manually, view logs, and monitor the progress of ongoing tasks.

7. **Documentation**: Provide comprehensive documentation on how to install, configure, and use the application. Include examples of different workflows and use cases.

This mini-application not only showcases the integration capabilities of Apache Airflow with Apache Drill but also demonstrates the power of automated data pipelines. It aims to simplify complex data handling tasks and make them accessible through a user-friendly interface.

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