apache-airflow-providers-apache-pinot

v4.10.2 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package shows low risks across most categories, with only minor concerns about shell execution and metadata integrity. There is no evidence of malicious activity.

  • Low network risk
  • Shell execution detected but appears legitimate
  • Minor issues with metadata
Per-check LLM notes
  • Network: No network calls detected, indicating low risk for direct exfiltration or command and control.
  • Shell: Detection of shell execution may indicate legitimate package functionality, but requires further review to ensure it is not being misused.
  • Obfuscation: The observed pattern is likely part of the package's standard import mechanism rather than obfuscation for malicious purposes.
  • Credentials: No patterns indicative of credential harvesting were detected.
  • Metadata: The package has some minor issues with maintainer history and a non-secure link, but no clear signs of malicious intent.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 16 test file(s) found

  • Test runner config found: conftest.py
  • 16 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 (3719 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
  • 10 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)

  • ".join(command)) with subprocess.Popen( command, stdout=subprocess.PIPE, stderr=subproc
✓ 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-pinot 
Your task is to develop a mini-application using Apache Airflow that leverages the 'apache-airflow-providers-apache-pinot' package to automate data ingestion into a Pinot cluster from various sources such as CSV files, databases, or APIs. This application will serve as a data pipeline management tool, allowing users to schedule and monitor the ingestion of data into their Pinot clusters efficiently.

The application should include the following components:
1. **DAG Creation**: Create a Directed Acyclic Graph (DAG) within Apache Airflow that defines the workflow for ingesting data into Pinot. The DAG should have tasks for extracting data from different sources, transforming it if necessary, and loading it into Pinot.
2. **Data Extraction**: Implement operators that can extract data from various sources. For example, you could create an operator that reads CSV files from an S3 bucket, another that pulls data from a MySQL database, and yet another that fetches data from a REST API.
3. **Transformation (Optional)**: Depending on the data source, implement transformations such as cleaning, filtering, or aggregating data before loading it into Pinot. This step is optional but highly recommended for ensuring data quality.
4. **Data Loading**: Use the 'apache-airflow-providers-apoint' package to define tasks that load the extracted (and optionally transformed) data into a Pinot cluster. Ensure that the data schema in Pinot matches the structure of the incoming data.
5. **Monitoring and Alerts**: Set up monitoring for each task in the DAG to ensure that data ingestion processes run smoothly. If any task fails, the system should send alerts via email or Slack to notify administrators.
6. **Scheduling**: Schedule the DAG to run at regular intervals, such as daily or hourly, depending on the frequency of new data being available.
7. **User Interface**: Optionally, provide a simple web-based UI where users can view the status of the DAGs, see logs, and manage the scheduling of data ingestion tasks.

This mini-application will demonstrate the power of Apache Airflow in managing complex data pipelines, particularly those involving real-time data ingestion into Pinot. It will showcase how the 'apache-airflow-providers-apache-pinot' package simplifies interactions between Airflow and Pinot, making it easier for developers and data engineers to integrate Pinot into their data processing workflows.

💬 Discussion Feed

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