As an SRE, have you ever had a situation where you were working on an application that was written with non-standard frameworks, or you wanted to get some interesting business data from an application (number of orders processed for example) but you didn’t have access to the source code?

Salt Security had deployed OpenTelemetry but found it insufficient. So the company engineers evaluated Helios, which visualizes distributed tracing for fast troubleshooting. My role as the Director of Platform Engineering at Salt Security lets me pursue my passion for cloud-native tech and for solving difficult system-design challenges. One of the recent challenges we solved had to do with visibility into our services. Or lack thereof.

What do you do when things break in production? Debugging microservices isn’t a walk in the park. Microservices are designed to be loosely coupled, which makes them more scalable and resilient, but also more difficult to debug. When a problem occurs in a microservices app, it can be difficult to track down the source of the problem. When the problem is in production, the clock is ticking and you have to resolve the issues fast.

OpenMetrics and OpenTelemetry are two popular frameworks that provide metrics-driven insight into complex software environments. Understanding the differences between these two options is critical for organizations looking to optimize their monitoring capabilities. In this article, we will explore the key features, benefits, and considerations of OpenMetrics and OpenTelemetry to help you make an informed decision when choosing the right solution for your organization.

OpenTelemetry and Prometheus are both powerful observability tools, each with its own strengths. OpenTelemetry provides a standardized way to collect, instrument, and export telemetry data, while Prometheus excels at time-series monitoring and alerting. In this blog post, we will delve into the similarities and differences between OpenTelemetry and Prometheus, exploring the benefits and advantages of each.

In the previous article, we talked about Distributed Tracing with MuleSoft APIs using OpenTelemetry. In this post, we’ll go through the process of integrating Distributed Tracing with MuleSoft APIs using OpenTelemetry via a proxy server. The purpose of this article is to demonstrate how we can instrument a legacy mule app with open telemetry without making changes to the existing app. Here, we’re showing an example of getting data from a header as well as a query parameter.

What is OpenTelemetry Collector, Architecture, Deployment and Getting started.

Core Web Vitals (CWV) are Google's preferred metrics for measuring the quality of the user experience for browser web apps. Currently, Core Web Vitals measure loading performance, interactivity, and visual stability. These are the main indicators of what a user’s experience will be while using a web page.

Elastic APM supports OpenTelemetry on multiple levels. One easy-to understand scenario, which we previously blogged about, is the direct OpenTelemetry Protocol (OTLP) support in APM Server. This means that you can connect any OpenTelemetry agent to an Elastic APM Server and the APM Server will happily take that data, ingest it into Elasticsearch®, and you can view that OpenTelemetry data in the APM app in Kibana®.