Centralized Logging and Observability in AWS- Implementing ELK Stack for Enterprise Applications

Abstract Modern enterprise environment requires complex, distributed infrastructures, and centralized logging is essential for managing such systems. Consolidating log data from applications, servers, and network devices to a single central location allows organizations to improve system performance, security, and scalability and realize more efficient logging and archival of system events. As an alternative, it presents a quicker way to try and resolve or at least detect system issues by utilizing a single, high-level view of the system. Centralized logging also supports regulatory compliance for creating an audit trail of access and potential security breaches. In a cloud-native environment, there are distinctive challenges of data fragmentation, varied log formats, and log correlation across distributed goods. Those are amplified in a highly dynamic environment, such as microservices, containers, and multi-cloud environments. These problems can add up to delayed incident detection and increase downtime. They can have an impact on the customer experience as well as the reliability of the system itself without centralized logging. Centralized logging and observability can be implemented easily using the ELK Stack (Elasticsearch, Logstash, Kibana), especially in AWS. The ELK Stack integrates with AWS services such as Lambda, CloudWatch, ELK Stack, and Elasticsearch Service and provides real-time log collection, processing, and visualization at scale. This study explores implementing ELK Stack in enterprise applications to enhance the system observability and performance and adherence to the best practices, security, and trends in logging and observability research.