apache-airflow-providers-presto

v5.12.0 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package shows low risks across multiple categories, indicating it is likely safe for use. However, the metadata risk score slightly elevates the overall risk due to a non-HTTPS external link and limited author information.

  • Low network and shell execution risks
  • Minimal obfuscation risk
  • No evidence of credential harvesting
Per-check LLM notes
  • Network: No network calls detected, which is normal for a library focused on local operations like Apache Airflow providers.
  • Shell: No shell executions detected, consistent with a package that does not require administrative privileges or system commands.
  • Obfuscation: The observed pattern is likely a standard method for extending package paths and not indicative of malicious obfuscation.
  • Credentials: No patterns suggesting credential harvesting or secret theft were detected.
  • Metadata: The presence of a non-HTTPS external link and an author with limited information suggests some caution is warranted, but there is no clear indication of malicious intent.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 14 test file(s) found

  • Test runner config found: conftest.py
  • 14 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-pre
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (4694 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
  • 18 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 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-presto 
Your task is to develop a small but comprehensive data processing pipeline using Apache Airflow and the 'apache-airflow-providers-presto' package. This pipeline will be designed to manage and execute SQL queries on a Presto cluster, enabling efficient data extraction and transformation tasks. Your goal is to create a fully functional mini-application that demonstrates the capabilities of this package in real-world scenarios.

**Project Overview:**
- **Name:** Presto Data Pipeline
- **Purpose:** To showcase the integration of Apache Airflow with Presto for executing complex data processing workflows.
- **Technologies Used:** Python, Apache Airflow, PrestoDB, 'apache-airflow-providers-presto'

**Key Features:**
1. **Task Scheduling:** Define DAGs (Directed Acyclic Graphs) in Airflow to schedule and orchestrate tasks.
2. **Presto Integration:** Use the 'apache-airflow-providers-presto' package to connect to a Presto cluster and execute SQL queries.
3. **Data Extraction:** Implement tasks that extract data from various tables in the Presto cluster.
4. **Data Transformation:** Apply transformations to the extracted data within Airflow using custom Python scripts or SQL queries.
5. **Error Handling & Logging:** Ensure robust error handling and logging mechanisms are in place to monitor the execution of each task.
6. **Visualization:** Integrate with Airflow’s UI to visualize the status of each task in the DAG.

**Steps to Completion:**
1. **Setup Environment:** Install necessary packages including Apache Airflow and 'apache-airflow-providers-presto'. Configure Airflow to connect to your Presto cluster.
2. **Define DAG Structure:** Create a DAG that includes tasks for connecting to Presto, extracting data, performing transformations, and logging results.
3. **Implement SQL Queries:** Write SQL queries to interact with the Presto cluster, ensuring you cover a variety of operations like SELECT, JOIN, and GROUP BY.
4. **Transformation Logic:** Develop Python scripts that use Airflow operators to transform the extracted data according to predefined business rules.
5. **Testing:** Test your pipeline thoroughly to ensure all tasks execute correctly and handle errors gracefully.
6. **Documentation:** Provide clear documentation on how to set up and run the pipeline, including configuration settings and example queries.

This project aims to provide a practical understanding of how Apache Airflow can be leveraged alongside the 'apache-airflow-providers-presto' package to streamline data processing workflows involving Presto.

💬 Discussion Feed

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