airsql

v0.15.0 safe
3.0
Low Risk

A decorator-based SQL execution framework for Airflow

🤖 AI Analysis

Final verdict: SAFE

The package shows low risk across all assessed categories with no evidence of malicious activity. However, the maintainer's inactivity and lack of community engagement slightly increase the metadata risk.

  • No network calls
  • No shell execution
  • No obfuscation
  • No credential harvesting
  • Low community engagement
Per-check LLM notes
  • Network: No network calls suggest the package does not engage in external communications, which is normal unless specific network interactions are expected for its functionality.
  • Shell: No shell execution detected implies that the package does not execute system commands, reducing the risk of it being used to perform unauthorized actions on the host system.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer seems new or inactive, and the repository lacks community engagement.

📦 Package Quality Overall: Medium (5.6/10)

◈ 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://github.com/1cadumagalhaes/airsql#readme
  • Detailed PyPI description (14269 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

  • 138 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in 1cadumagalhaes/airsql
  • Two distinct contributors found

🔬 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: cadumagalhaes.dev>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "1cadumagalhaes" 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 airsql 
Create a data pipeline mini-application using Apache Airflow and the 'airsql' package. This application will automate the process of fetching data from multiple SQL databases, transforming it, and loading it into a centralized analytics database. The goal is to streamline the ETL (Extract, Transform, Load) process and make it more efficient and manageable through automated workflows.

Step 1: Set up your environment.
- Install necessary packages including Airflow and 'airsql'.
- Configure Airflow to connect to at least two different SQL databases (e.g., MySQL and PostgreSQL).

Step 2: Define the data sources.
- Use 'airsql' decorators to create tasks that extract data from each source database. Each task should be able to handle basic SQL queries and fetch specific datasets.

Step 3: Implement data transformation logic.
- Develop Airflow tasks that perform transformations on the extracted data. These could include cleaning, aggregating, or joining datasets from different sources.
- Utilize 'airsql' to execute these transformations directly within the Airflow DAGs.

Step 4: Load transformed data.
- Design a task that loads the transformed data into a central analytics database.
- Ensure that this task uses 'airsql' to manage the insertion or update of records efficiently.

Suggested Features:
- Error handling and logging for all SQL operations.
- Support for incremental data loads based on timestamps or other criteria.
- Ability to schedule the ETL process at regular intervals (e.g., daily).
- Visualization of the data flow and status updates through Airflow's UI.

How 'airsql' is utilized:
- For each SQL operation within the ETL process, use 'airsql' decorators to define and execute the SQL code. This includes both extraction and transformation steps.
- Leverage 'airsql' capabilities to manage connections and configurations for multiple databases seamlessly.
- Ensure that the application demonstrates the flexibility and ease of use provided by 'airsql' for integrating complex SQL operations within Airflow DAGs.