apache-airflow-providers-common-sql

v2.0.0 suspicious
4.0
Medium Risk

Provider package apache-airflow-providers-common-sql for Apache Airflow

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has some minor concerns, particularly around metadata, but no direct evidence of malicious activity was found.

  • Missing author name
  • Single associated package
Per-check LLM notes
  • Network: No network calls detected, which is normal for a library focused on common SQL operations.
  • Shell: No shell executions detected, consistent with a non-malicious library.
  • Obfuscation: The observed pattern is likely a standard method for extending package paths and not indicative of malicious obfuscation.
  • Credentials: No patterns indicative of credential harvesting were found.
  • Metadata: The package shows some potential red flags with a missing author name and a single associated package, but there are no clear signs of typosquatting or malicious intent.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 31 test file(s) found

  • Test runner config found: conftest.py
  • 31 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-com
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (5239 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
  • 105 type-annotated function signatures detected in source
✦ 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-common-sql 
Develop a data pipeline automation tool using Apache Airflow and the 'apache-airflow-providers-common-sql' package. Your task is to create a fully functional mini-application that automates the process of extracting data from multiple SQL databases, transforming it into a standardized format, and loading it into a central analytics database. This tool will help streamline data integration processes for businesses looking to consolidate their data sources into a single location for analysis.

The application should have the following features:
1. **Data Extraction**: Define operators within Apache Airflow to connect to various SQL databases (such as MySQL, PostgreSQL, and SQLite). Use the 'apache-airflow-providers-common-sql' package to facilitate these connections and extract specific datasets from each source.
2. **Data Transformation**: Implement tasks that clean and standardize the extracted data. This includes handling missing values, converting data types, and formatting dates consistently across all datasets.
3. **Data Loading**: Design a process to load the transformed data into a central SQL database (e.g., Redshift, BigQuery, or another preferred target database) for further analysis.
4. **Scheduling & Monitoring**: Set up a scheduler to run the data pipeline at regular intervals (daily, weekly, etc.). Additionally, integrate monitoring capabilities to track the status of each job and notify stakeholders of any failures or issues.
5. **Configuration Management**: Allow users to configure different data sources, target databases, and transformation rules via a simple configuration file or UI.
6. **Logging & Documentation**: Ensure comprehensive logging for each step of the pipeline execution. Provide clear documentation on how to set up, run, and maintain the application.

Use the 'apache-airflow-providers-common-sql' package to simplify the connection and data retrieval process from various SQL databases. This package provides a common interface for interacting with SQL databases, making it easier to write generic code that works across different SQL engines. Your goal is to demonstrate proficiency in leveraging this package to build a robust and scalable data pipeline.

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