This project aims to collect logs from multiple sources, centralize them in Kafka topics, and utilize consumers to process and store them for analysis.
The purpose of this project is to get a basic idea about the implementation of a distributed log aggregation system using industry-standard technologies. By leveraging Apache Kafka as a messaging system and Spring Boot for building the application, created a scalable and fault-tolerant system for collecting and processing logs.
The project covers the following key aspects:
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Apache Kafka: Learn how to set up Apache Kafka as the messaging backbone of the log aggregation system. Understand the concepts of topics, producers, and consumers in Kafka.
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Spring Boot Integration: Discover how to integrate Spring Boot with Apache Kafka using the Spring Kafka library. This allows seamless communication between the log producers and consumers within the Spring Boot application.
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Log Producer: Implement a log producer component responsible for producing log messages to Kafka. Logs can be sourced from various systems and are sent to Kafka topics for further processing.
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Log Consumer: Develop a log consumer component that subscribes to Kafka topics, consumes log messages, and processes them according to the desired requirements. This component can perform actions such as storing logs in a database, performing analysis, or triggering alerts.
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Distributed Architecture: Understand the benefits of a distributed architecture for log aggregation. Explore concepts such as consumer groups, parallel processing, scalability, and fault tolerance.
Through this project, got insights into building a robust log aggregation system using Apache Kafka and Spring Boot, learned how to collect logs from multiple sources, centralize them in Kafka topics, and process them with distributed consumers. This system enables real-time log analysis, storage, and monitoring, offering a foundation for building more advanced logging and monitoring solutions.
