Articles
Elastic's RAG-based AI Assistant: Analyze application issues with LLMs and private GitHub issues
In this blog, we review how GitHub issues and other GitHub documents from internal and external GitHub repositories can be used in root cause analysis with Elastic’s RAG-based AI Assistant.
Analyzing OpenTelemetry apps with Elastic AI Assistant and APM
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.
Build better Service Level Objectives (SLOs) from logs and metrics
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.
Optimizing Observability with ES|QL: Streamlining SRE operations and issue resolution for Kubernetes and OTel
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.
Best practices for instrumenting OpenTelemetry
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.
Native OpenTelemetry support in Elastic Observability
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.
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Automatic instrumentation with OpenTelemetry for Python applications
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.
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Manual instrumentation with OpenTelemetry for Node.js applications
In this blog post, we will show you how to manually instrument Node.js applications using OpenTelemetry. We will explore how to use the proper OpenTelemetry Node.js libraries and in particular work on instrumenting tracing in a Node.js application.
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Manual instrumentation with OpenTelemetry for Python applications
In this blog post, we will show you how to manually instrument Python applications using OpenTelemetry. We will explore how to use the proper OpenTelemetry Python libraries and in particular work on instrumenting tracing in a Python application.
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Automatic instrumentation with OpenTelemetry for Node.js applications
Learn how to auto-instrument Node.js 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.
Managing your applications on Amazon ECS EC2-based clusters with Elastic Observability
Learn how to manage applications on Amazon ECS clusters based on EC2 instances and how simple it is to use Elastic agents with the AWS and docker integrations to provide a complete picture of your apps, ECS service, and corresponding EC2 instances.
Unleash the power of Elastic and Amazon Kinesis Data Firehose to enhance observability and data analytics
AWS users can now leverage the new Amazon Kinesis Firehose Delivery Stream to directly ingest logs into Elastic Cloud in real time for centralized alerting, troubleshooting, and analytics across your cloud and on-premises infrastructure.
Gain insights into Kubernetes errors with Elastic Observability logs and OpenAI
This blog post provides an example of how one can analyze error messages in Elasticsearch with ChatGPT using the OpenAI API via Elasticsearch.
Trace your Azure Function application with Elastic Observability
Serverless applications deployed on Azure Functions are growing in usage. This blog shows how to deploy a serverless application on Azure functions with Elastic Agent and use Elastic's APM capability to manage and troubleshoot issues.
Root cause analysis with logs: Elastic Observability's AIOps Labs
Elastic Observability provides more than just log aggregation, metrics analysis, APM, and distributed tracing. Our machine learning-based AIOps capabilities help you analyze the root cause of issues allowing you to focus on the most important tasks.
Root cause analysis with logs: Elastic Observability's anomaly detection and log categorization
Elastic Observability provides more than just log aggregation, metrics analysis, APM, and distributed tracing. Elastic’s machine learning capabilities help analyze the root cause of issues, allowing you to focus your time on the most important tasks.
Easily analyze AWS VPC Flow Logs with Elastic Observability
Elastic Observability can ingest and help analyze AWS VPC Flow Logs from your application’s VPC. Learn how to ingest AWS VPC Flow Logs through a step-by-step method into Elastic, then analyze it and apply OOTB machine learning for insights.
Wait… Elastic Observability monitors metrics for AWS services in just minutes?
Get metrics and logs from your AWS deployment and Elastic Observability in just minutes! We’ll show you how to use Elastic integrations to quickly monitor and manage the performance of your applications and AWS services to streamline troubleshooting.
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Independence with OpenTelemetry on Elastic
OpenTelemetry has become a key component for observability given its open standards and developer-friendly tools. See how easily Elastic Observability integrates with OTel to provide a platform that minimizes vendor lock-in and maximizes flexibility.
Managing your Kubernetes cluster with Elastic Observability
Unify all of your Kubernetes metrics, log, and trace data on a single platform and dashboard, Elastic. From the infrastructure to the application layer Elastic Observability makes it easier for you to understand how your cluster is performing.