README
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.
The pipeline includes the following key components:
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Data Extraction: Utilizes custom APIs from the "SpokeLizard" web application 🦎 to extract game data in real-time.
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Data Transformation and Collection: Logstash is configured to collect and transform raw game data into a structured format suitable for further processing.
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Data Processing: Apache Kafka manages data streams, ensuring reliable messaging and scalability as data moves through the pipeline.
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Real-time Analytics: Apache Spark performs distributed data processing, applying machine learning models to analyze gameplay patterns and outcomes in real-time.
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Data Storage and Indexing: Elasticsearch indexes processed data, enabling fast search and retrieval capabilities for analysis and reporting.
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Data Visualization: Kibana is employed to create interactive dashboards and visualizations, providing insights into gameplay trends, player behavior, and machine learning model performance.
To deploy and use this pipeline:
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Clone Repository: Clone this repository to your local environment.
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Configuration: Adjust configuration files (
logstash.conf,spark-config, etc.) as per your environment setup and requirements. -
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.
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Run Pipeline: Start the pipeline components in the specified order to begin streaming and analyzing data from the "SpokeLizard" web application.
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Monitor and Visualize: Access Kibana to monitor real-time analytics and visualize insights derived from the gameplay data.
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Ensure proper network configurations and security measures are in place, especially when handling real-time data streams and sensitive gameplay information.
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Regularly monitor pipeline performance and optimize configurations for efficient data processing and analysis.
For any questions, issues, or suggestions regarding this repository, please contact [alemicieli26@gmail.com]