ABOUT THE CLIENT
Our client is a reputed company in the telecommunications sector, Their operations rely on a complex ecosystem of 12 Kubernetes clusters and 100+ virtual machines, running diverse workloads such as Spark applications, Kafka, and Apache Druid
CHALLENGE
The client faced significant challenges with their existing log monitoring setup, which relied on Google Cloud Stackdriver. The decentralized nature of their environment made it difficult to manage logs from ephemeral Kubernetes pods and diverse workloads consistently. Stackdriver struggled to handle custom log formats and lacked integration with configuration management tools, leading to inefficiencies in log collection and analysis. These issues hampered troubleshooting efforts and delayed responses to critical incidents, impacting their operational efficiency and ability to maintain high service standards.
SOLUTIONS
We implemented a centralized log monitoring system using the ELK Stack (Elasticsearch, Logstash, and Kibana) to address these challenges. A dedicated Elasticsearch cluster was deployed to ensure scalable, centralized storage and efficient querying. Fluentd was configured as a daemonset in Kubernetes clusters and as agents on virtual machines to dynamically collect system and application logs. These logs were processed through custom Logstash pipelines to enrich data, detect error patterns, and trigger real-time alerts via Icinga. Kibana dashboards provided intuitive visualization, enabling proactive monitoring and trend analysis. Integration with Helm, Ansible, and Terraform ensured consistent and streamlined deployments across environments, minimizing manual intervention and maintaining uniform configurations.
