apache-airflow-providers-apache-hive

v9.5.0 safe
1.0
Low Risk

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

📦 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-apa
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (5985 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
  • 43 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 2.0

Found 1 shell execution pattern(s)

  • sub_process: Any = subprocess.Popen( hive_cmd, stdout=subprocess.PIPE, stderr=su
✓ 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-hive 
Create a data pipeline management tool using Apache Airflow and the 'apache-airflow-providers-apache-hive' package. This tool will serve as a robust solution for orchestrating workflows that involve interacting with Apache Hive databases. Your task is to design and implement a fully-functional mini-application that demonstrates the integration of Apache Airflow with Hive, showcasing key functionalities such as scheduling tasks, executing SQL queries on Hive, and handling data transformations.

The application should include the following features:
1. **Task Scheduling**: Implement a DAG (Directed Acyclic Graph) in Apache Airflow that schedules periodic jobs to run at specific intervals (e.g., daily, hourly).
2. **Hive Interaction**: Use the 'apache-airflow-providers-apache-hive' package to execute SQL queries against a Hive database. These queries could include creating tables, inserting data, and performing complex aggregations.
3. **Data Transformation**: Integrate data transformation logic within your DAGs to manipulate data before or after it's stored in Hive. For example, you might need to join datasets from different sources or perform data cleansing operations.
4. **Error Handling and Logging**: Ensure that the application logs all actions performed during execution and provides alerts when errors occur. This includes logging query results, execution times, and any exceptions thrown during processing.
5. **User Interface**: Although not mandatory, consider adding a simple web interface using Airflow’s UI capabilities to monitor the status of running jobs and view logs.

To achieve these objectives, follow these steps:
1. Set up a local development environment with Apache Airflow installed and configured.
2. Install the 'apache-airflow-providers-apache-hive' package to enable interaction with Hive.
3. Define a DAG that specifies tasks to be executed, including SQL queries and data transformation scripts.
4. Configure the DAG to schedule tasks based on defined intervals.
5. Test the functionality of your application thoroughly, ensuring that all features work as expected and that error handling mechanisms are effective.
6. Document your code and setup process clearly, providing instructions for others to replicate your work.

This project will not only demonstrate your proficiency with Apache Airflow but also showcase your ability to integrate external systems like Hive into a data orchestration framework.

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