Monitoring and observability have increased with software applications moving from monolithic to distributed microservice architectures. While observability and application monitoring share similar definitions, they also have some differences. The purpose of both monitoring and observability is to find issues in an application. However, monitoring aims to capture already known issues and display them on a dashboard to understand their root cause and the time they occurred.

The scope of the OpenTelemetry project encompasses how telemetry data is collected, processed, and transmitted. The OpenTelemetry project is not involved with how the data is stored, displayed, or used beyond the collection and transmission phases. The OpenTelemetry Collector is an application written in Go.

For this post, we’ll use a small Flask app that allows users to input a city name and they receive the current weather information for that city. We’ll make an API call to openweathermap.org to get the weather information for the city.

At Mezmo, we see a massive opportunity to reduce Mean Time to Detection (MTTD) and Mean Time to Resolution (MTTR) by making log data more valuable and actionable. Today, we’re thrilled to announce the release of the Mezmo Exporter for OpenTelemetry- the first step in our continued work with the project to further simplify the ingestion of log data and make that data more actionable with enrichment of key OpenTelemetry attributes.

Frontend technologies typically talk to several services in your backend, and those services talk to other services. At the root of every issue is a single event that causes a domino effect. A domino effect that impacts every operation from the first experience on the frontend to the backend API call. Sentry can show you how these exceptions and latency issues impact every one of your services. For example, take the ever common and seemingly simple to resolve 500 - Internal Server Error.