apache-airflow-providers-sqlite

v4.3.2 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package is deemed safe with low risks across all categories except metadata, where there are some concerns about the link security and author activity. However, these do not indicate any malicious intent.

  • No network calls or shell executions detected.
  • Limited obfuscation risk with standard import mechanisms.
  • No evidence of credential harvesting.
Per-check LLM notes
  • Network: No network calls detected, which is normal for a library focused on SQLite integration with Apache Airflow.
  • Shell: No shell execution patterns detected, aligning with expectations for a standard Python library.
  • Obfuscation: The observed pattern is likely part of standard package import mechanisms and not indicative of malicious obfuscation.
  • Credentials: No patterns indicative of credential harvesting were detected.
  • Metadata: The package has a non-secure link and an author with limited activity, but no clear signs of malicious intent.

📦 Package Quality Overall: Medium (7.4/10)

✦ High Test Suite 9.0

Test suite present — 8 test file(s) found

  • Test runner config found: conftest.py
  • 8 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-sql
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (3359 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 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-sqlite 
Create a data processing pipeline using Apache Airflow that leverages SQLite as its database backend for storing intermediate results. This mini-project will serve as a simple yet powerful tool to demonstrate how to set up an airflow environment with SQLite integration, process data from a CSV file, store the processed data back into SQLite, and then generate a report based on the stored data. Here’s a step-by-step guide to building this application:

1. **Set Up Your Environment**: Install Apache Airflow and the 'apache-airflow-providers-sqlite' package. Ensure you have Python 3.7 or later installed.
2. **Design the DAG**: Create a Directed Acyclic Graph (DAG) that outlines the workflow of your tasks. Tasks include reading data from a CSV file, processing the data (e.g., cleaning, transforming), storing the processed data in SQLite, and generating a summary report.
3. **CSV Reader Task**: Implement a task that reads data from a CSV file. Use the pandas library to handle CSV operations efficiently.
4. **Data Processing Task**: Develop a task that processes the read data. This could involve filtering out unnecessary columns, converting data types, or performing calculations.
5. **SQLite Integration**: Utilize the 'apache-airflow-providers-sqlite' package to integrate SQLite into your workflow. Set up a connection to SQLite within Airflow, and write a task that inserts the processed data into SQLite tables.
6. **Report Generation Task**: After storing the processed data in SQLite, create a task that generates a summary report based on the stored data. This could be a simple count of records, average values, or more complex analytics.
7. **Testing and Deployment**: Test each component of your pipeline separately before integrating them into the full DAG. Once everything works as expected, deploy your pipeline.
8. **Documentation**: Document your setup, including configuration files, DAG code, and any dependencies required for others to replicate your work.

Suggested Features:
- Ability to configure the CSV file path and SQLite database path via environment variables or Airflow's UI.
- Include error handling to manage issues such as missing files or database connection failures.
- Add logging to track the progress and status of each task.
- Optimize the pipeline for performance, especially if dealing with large datasets.

This project not only showcases the power of Apache Airflow but also highlights how SQLite can be effectively integrated into data workflows, providing a robust solution for managing and processing data.

💬 Discussion Feed

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