Elastic Observability provides native OpenTelemetry support, but analyzing applications logs, metrics, and traces can be daunting. Elastic Observability not only provides AIOps features but also an AI Assistant (co-pilot) to help get to MTTR faster.

To help manage operations and business metrics, Elastic Observability's SLO (Service Level Objectives) feature was introduced in 8.12. This blog reviews this feature and how you can use it with Elastic's AI Assistant to meet SLOs.

ES|QL enhances operational efficiency, data analysis, and issue resolution for SREs. This blog covers the advantages of ES|QL in Elastic Observability and how it can apply to managing issues instrumented with OpenTelemetry and running on Kubernetes.

Introducing ES|QL: Elastic's piped query language. Transform, enrich, and simplify data investigations with concurrent processing, efficient searches across data, and all-in-one screen aggregations and visualizations delivering iterative workflows.

Elastic offers native support for OpenTelemetry by allowing for direct ingest of OpenTelemetry traces, metrics, and logs without conversion, and applying any Elastic feature against OTel data without degradation in capabilities.

Harness the power of generative AI to turn insights into actions. Powered by the Elasticsearch Relevance Engine™, Elastic’s AI Assistant transforms problem identification with an interactive assistant that delivers context-aware remediation for SREs.

This blog explains the importance of log management, best practices, and tools for managing logs efficiently. From identifying errors to troubleshooting issues, learn how to leverage log management for enhanced performance and streamlined operations.

Instrumenting OpenTelemetry is complex. Even using auto-instrumentation requires understanding details about your application and OpenTelemetry configuration options. We’ll cover the best practices for instrumenting applications for OpenTelemetry.

Learn how to auto-instrument Python applications using OpenTelemetry. With standard commands in a Docker file, applications can be instrumented quickly without writing code in multiple places, enabling rapid change, scale, and easier management.