apache-airflow-providers-apache-kafka

v1.14.0 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package shows low risk indicators across all categories with only minor concerns about metadata quality.

  • 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 and expected for a library focused on Apache Airflow and Kafka integration without immediate external dependencies.
  • Shell: No shell execution patterns detected, consistent with a library that does not require system-level operations.
  • Obfuscation: The detected pattern is likely a standard method for extending module search paths and not indicative of malicious obfuscation.
  • Credentials: No patterns indicative of credential harvesting were detected.
  • Metadata: The presence of a non-HTTPS link and an author with limited information suggests potential risks, but there's no strong evidence of malice.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 36 test file(s) found

  • Test runner config found: conftest.py
  • 36 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 (4619 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
  • 16 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

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-kafka 
Create a data pipeline management system using Apache Airflow and the 'apache-airflow-providers-apache-kafka' package. This system will automate the process of ingesting data from a Kafka topic, processing it through various stages, and then storing the processed data into a database. The goal is to showcase the integration capabilities between Apache Airflow and Kafka, as well as the power of orchestrating complex data workflows.

### Features:
- **Data Ingestion**: Define a task that subscribes to a Kafka topic and reads incoming messages.
- **Data Transformation**: Implement a series of tasks that transform the raw data into a structured format suitable for storage. These transformations could include parsing JSON, filtering out irrelevant data, or aggregating records.
- **Error Handling**: Ensure that the pipeline includes robust error handling mechanisms, such as retrying failed tasks or logging errors for manual inspection.
- **Database Storage**: Design a task that inserts the transformed data into a relational database (e.g., PostgreSQL).
- **Visualization**: Integrate a simple dashboard or report that visualizes key metrics or insights derived from the stored data.

### Utilization of 'apache-airflow-providers-apache-kafka':
- Use the 'KafkaConsumerOperator' from the 'apache-airflow-providers-apache-kafka' package to consume messages from a Kafka topic. This operator simplifies the process of setting up a Kafka consumer within an Airflow DAG.
- Leverage other operators provided by the package, such as 'KafkaProducerOperator', if necessary, for producing messages to another Kafka topic during the transformation phase.
- Explore additional functionalities offered by the package, such as custom hooks and sensors, to enhance the interaction between Airflow and Kafka.

### Steps to Build the Project:
1. Set up your development environment with Apache Airflow installed along with the 'apache-airflow-providers-apache-kafka' package.
2. Create a new Airflow DAG that includes tasks for each step of the data pipeline.
3. Implement the Kafka consumer task using 'KafkaConsumerOperator'. Configure it to connect to your Kafka cluster and subscribe to the relevant topic.
4. Develop transformation tasks that manipulate the data as required. For example, use Python scripts or libraries to parse and clean the data.
5. Implement a database insertion task using appropriate operators or hooks to store the transformed data.
6. Test the entire pipeline to ensure all tasks run successfully and handle errors gracefully.
7. Optionally, create a visualization component that queries the database and presents meaningful insights about the processed data.

This project will not only demonstrate the practical use of Apache Airflow and Kafka but also provide a valuable tool for managing and analyzing real-time data streams.

💬 Discussion Feed

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