Operations | Monitoring | ITSM | DevOps | Cloud

If you’re new to OpenTelemetry, like I was, you might be wondering how to quickly get started. OpenTelemetry is becoming the gold standard to collect all of your machine data and is changing observability as we know it. Instead of learning multiple technologies to collect all data, you can leverage a single cloud-native framework to complete your observability.

Distributed tracing is a household term nowadays – if your house is an IT department! Modern enterprises use cloud-native applications for agility and responsiveness to customer needs. When monitoring cloud-native applications, distributed tracing follows how transactions perform while traversing services or containers in the backend architecture. By definition, we’re describing production applications with requests, methods, database calls and logs that accompany a transaction.

Nowadays, microservice architecture is a pattern that helps to innovate quicker by enabling easier scalability, giving language flexibility, improving fault isolation, etc. Systems built this way also bring some downsides. Moving parts, concurrent invocations, and different retries policies can make operating and troubleshooting such systems challenging. Without proper tools, correlating logs with metrics may be difficult. To overcome these challenges, you need observability.

There are standalone distributed tracing tools like Jaeger, and there are APM tools that do not provide distributed tracing capabilities. In this article, we will see how distributed tracing complements an APM tool for a holistic performance monitoring experience. Both APM and distributed tracing are critical tools to understand the performance of your applications. And if your application is facing performance issues impacting customer experience, you need to understand what’s causing it fast.

This week we have turned on full trace retention search (beta) in Grafana Cloud Traces. This feature was also introduced in the recent release of Grafana Tempo v1.3. Previously, if you brought up your Grafana Cloud Traces data source, you were greeted with this message: This message simply warned the user that the Grafana Tempo search was calibrated for recent traces only, regardless of the selected time window.

The boom of digital commerce is making all businesses take a closer look at how they deliver great customer experiences. To stay competitive, businesses today are using cloud-native architectures, because the cloud-native applications they produce deploy quickly and better support the continuous improvement cycles of agile methodology. Behind the practicality of keeping online customers happy are distributed cloud environments that business applications use for each customer interaction.

GraphQL is a query language for APIs developed by Facebook in 2012. It was then open-sourced in 2015. GraphQL enables frontend developers or consumers of APIs to request the exact data that they need, with no over-fetching or under-fetching. In this article, we will learn how to monitor GraphQL APIs with the open-source APM tool, SigNoz. GraphQL has become a popular alternative to REST because of its ease of use. It enables developers to pull data from multiple data sources in a single API call.

Today, the Honeycomb OpenTelemetry Distribution for Java reaches a major milestone with a 1.0 release. This is the first Honeycomb OTel Distro to come out of beta and go into the general release category, and you can expect more to come!