README

Project Overview

This repository implements a data pipeline designed to analyze and visualize real-time data streams from a web application I developed. The web application facilitates a multiplayer rock-paper-scissors game enhanced with machine learning capabilities.

Pipeline Components

The pipeline includes the following key components:

• Data Extraction: Utilizes custom APIs from the "SpokeLizard" web application 🦎 to extract game data in real-time.

• Data Transformation and Collection: Logstash is configured to collect and transform raw game data into a structured format suitable for further processing.

• Data Processing: Apache Kafka manages data streams, ensuring reliable messaging and scalability as data moves through the pipeline.

• Real-time Analytics: Apache Spark performs distributed data processing, applying machine learning models to analyze gameplay patterns and outcomes in real-time.

• Data Storage and Indexing: Elasticsearch indexes processed data, enabling fast search and retrieval capabilities for analysis and reporting.

• Data Visualization: Kibana is employed to create interactive dashboards and visualizations, providing insights into gameplay trends, player behavior, and machine learning model performance.

Usage

To deploy and use this pipeline:

1. Clone Repository: Clone this repository to your local environment.

2. Configuration: Adjust configuration files (logstash.conf, spark-config, etc.) as per your environment setup and requirements.

3. Deploy: Deploy and configure Logstash, Kafka, Spark(spark setting is not present because git did not make me load the folder), Elasticsearch, and Kibana in your environment.

4. Run Pipeline: Start the pipeline components in the specified order to begin streaming and analyzing data from the "SpokeLizard" web application.

5. Monitor and Visualize: Access Kibana to monitor real-time analytics and visualize insights derived from the gameplay data.

Notes

• Ensure proper network configurations and security measures are in place, especially when handling real-time data streams and sensitive gameplay information.

• Regularly monitor pipeline performance and optimize configurations for efficient data processing and analysis.

Contact

For any questions, issues, or suggestions regarding this repository, please contact [alemicieli26@gmail.com]

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