Modern cloud environments generate massive amounts of logs, metrics, and traces—but without a scalable observability strategy, they quickly become noisy, expensive, and operationally overwhelming. In this session, I will walk through a DevOps-driven framework for implementing OpenSearch as a high-performance observability platform capable of handling real-time ingestion at scale while keeping search fast and costs predictable.

I’ll cover the architectural patterns, pipelines, and indexing strategies that allow teams to reliably ingest terabytes of logs, enrich them, route them intelligently, and create structures optimized for fast troubleshooting. Attendees will also see practical techniques for reducing cluster load, improving retention strategies, tuning queries, and building dashboards that cut MTTR dramatically.

Whether you are migrating from Elasticsearch, centralizing observability, or scaling OpenSearch for multi-cloud workloads, this talk provides a field-tested blueprint for building an observability system that is resilient, efficient, and engineered for growth.