apache-airflow-providers-standard

v1.13.1 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal signs of potential risks with no network calls, secure shell usage for internal purposes, and standard obfuscation techniques. While there are some metadata concerns, they do not indicate malicious behavior.

  • Low network risk
  • Secure shell execution practices
  • Standard obfuscation techniques
Per-check LLM notes
  • Network: No network calls detected, indicating low risk.
  • Shell: Shell execution is primarily used for package checks and tests, suggesting it's part of the package's functionality rather than malicious activity.
  • Obfuscation: The observed pattern is likely a standard technique for extending module search paths and does not indicate malicious intent.
  • Credentials: No credential harvesting patterns were detected in the provided code snippet.
  • Metadata: The package has a non-secure external link and lacks detailed maintainer information, but no clear signs of malicious intent.

📦 Package Quality Overall: Medium (7.8/10)

✦ 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-sta
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (3869 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
  • 170 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 score 10.0

Found 6 shell execution pattern(s)

  • nnection_url() proc = subprocess.run( ["pip", "search", "not-existing-test-package",
  • try: result = subprocess.check_output(cmd, text=True) except Exception as e: r
  • ool: try: subprocess.check_call([self.python, "-c", "import pendulum"]) return T
  • try: result = subprocess.check_output( [self.python, "-c", self._external_airflow_
  • te(c) for c in cmd)) with subprocess.Popen( cmd, stdout=subprocess.PIPE, stderr
  • e(strict=True).as_posix() subprocess.check_call([python_path, "-m", "pip", "install", "cloudpickle", "dill"]
✓ 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-standard 
Create a mini-application that automates the process of data ingestion, transformation, and loading using Apache Airflow and the 'apache-airflow-providers-standard' package. This application will serve as a basic ETL (Extract, Transform, Load) pipeline, designed to demonstrate the capabilities of Airflow in orchestrating complex data workflows.

### Project Scope:
- **Data Extraction:** The application will periodically extract data from a publicly available API (e.g., OpenWeatherMap for weather data).
- **Data Transformation:** Once extracted, the data will be transformed to fit a specific format required for analysis, such as converting temperature from Kelvin to Celsius.
- **Data Loading:** Finally, the transformed data will be loaded into a local SQLite database for further processing and analysis.

### Key Features:
1. **Dynamic DAG Creation:** Utilize Airflow's dynamic DAG creation capabilities to define tasks that run based on predefined schedules.
2. **Task Dependencies:** Implement task dependencies to ensure that data transformation only occurs after successful extraction and before loading into the database.
3. **Error Handling and Logging:** Integrate robust error handling and logging mechanisms to monitor the execution of each task and maintain a record of any issues encountered during the ETL process.
4. **Database Management:** Use SQLite as the backend storage for the transformed data, ensuring that the application can easily manage and query the dataset.
5. **Scheduling Flexibility:** Allow users to configure the frequency of data extraction through Airflow's user interface, making the application adaptable to different use cases.

### How 'apache-airflow-providers-standard' is Utilized:
- **Operator Usage:** Leverage operators provided by the 'apache-airflow-providers-standard' package to handle HTTP requests for data extraction and database operations for data loading.
- **Connection Management:** Manage connections to external APIs and databases using Airflow's connection management feature, allowing for secure and dynamic configuration of endpoints.
- **Custom Operators:** Develop custom operators if necessary to tailor the data transformation process according to specific requirements, showcasing the extensibility of Airflow's architecture.

Your goal is to create a fully functional mini-application that not only demonstrates the power of Airflow in managing ETL processes but also serves as a practical tool for data enthusiasts looking to automate their data pipelines.

💬 Discussion Feed

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