apache-airflow-providers-apache-flink

v1.8.4 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package has low risks across all categories, with only minor concerns about metadata completeness and license link security.

  • No network calls or shell executions detected.
  • Minimal obfuscation observed, likely not malicious.
Per-check LLM notes
  • Network: No network calls detected, which is normal for this type of package.
  • Shell: No shell execution patterns detected, indicating no unexpected system command executions.
  • Obfuscation: The observed pattern is likely for standard package extension rather than malicious obfuscation.
  • Credentials: No evidence of credential harvesting patterns detected.
  • Metadata: The author details are incomplete and the license link is non-secure, but no other suspicious activities are observed.

📦 Package Quality Overall: Medium (7.4/10)

✦ High Test Suite 9.0

Test suite present — 7 test file(s) found

  • Test runner config found: conftest.py
  • 7 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 (3769 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
✦ 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-apache-flink 
Develop a data processing pipeline that leverages Apache Flink for real-time data analysis using Apache Airflow as the orchestrator. Your goal is to create a mini-application that can ingest live streaming data from a Kafka topic, perform real-time analytics on this data, and then store the processed results into a PostgreSQL database. The application will demonstrate the power of combining Apache Flink's stream processing capabilities with Apache Airflow's workflow management system.

Key Features:
1. **Data Ingestion**: Use the 'kafka-python' library to connect to a Kafka topic and continuously pull in streaming data.
2. **Real-Time Analytics**: Utilize Apache Flink operators provided by the 'apache-airflow-providers-apache-flink' package to process the ingested data in real-time. Implement basic aggregations such as counting occurrences of specific events or calculating average values over time windows.
3. **Storage**: After processing, the results should be stored in a PostgreSQL database. Use SQLAlchemy ORM for interacting with the database.
4. **Visualization**: Integrate Grafana or a similar tool to visualize the real-time data analytics output from the PostgreSQL database.
5. **Automation**: Set up Apache Airflow DAGs (Directed Acyclic Graphs) to automate the entire pipeline. Ensure that the DAGs are scheduled to run at regular intervals, and include error handling and retry mechanisms for robustness.
6. **Monitoring & Alerts**: Implement monitoring for the pipeline using Prometheus and Alertmanager to detect any anomalies or failures in the data flow and trigger alerts via Slack or email.

Instructions:
- Start by setting up a local development environment with Docker containers for Kafka, PostgreSQL, and Apache Airflow.
- Install necessary Python packages including 'kafka-python', 'apache-airflow-providers-apache-flink', and 'sqlalchemy'.
- Define the schema for your PostgreSQL database and create the necessary tables.
- Write Airflow operators using the 'apache-airflow-providers-apache-flink' package to define tasks such as reading from Kafka, performing real-time analytics, and writing to PostgreSQL.
- Configure Airflow DAGs to orchestrate these tasks and ensure they are executed in the correct order.
- Develop a simple Flask web application to serve as the front-end for visualizing data from Grafana.
- Test your pipeline thoroughly by simulating live data streams and verifying that the data is correctly processed and stored.

💬 Discussion Feed

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