In this article, learn how to setup application monitoring for Golang apps using an open-source solution, SigNoz. If you want to check our Github repo before diving in 👇 Scalability, Reliability, Maintainability... The list goes on for the benefits of microservices architecture in today's world. But along with these benefits also comes the challenges of complexity.

Application performance management (APM) and distributed tracing are practices that many teams have been using for years to help detect and mitigate performance issues within applications – while the first one was born in the era of big single-host monoliths, the latter is especially useful for distributed applications that use a microservices architecture, in which tracing is critical for pinpointing the source of performance issues.

These days, many IT executives believe that it is easier to deploy applications in the cloud than on-prem. They are also often under the misconception that once an application is hosted in the cloud, it is the responsibility of the cloud service provider to maintain the availability and performance of the application.

Having user-friendly programs is paramount for any software company’s success. To gain a better understanding of your users’ experience and enjoyment, it’s vital that you learn how customers interact with your app or website. Real user monitoring (RUM) solutions enable your company to visualize how users interact with your software, helping you learn what works best for your customers so you can thrive against the competition.

In the last few years, fintech enterprises have disrupted the financial services and banking industry by taking everything computing technology offers – from machine learning to blockchain – and turning it up a notch. Traditional financial institutions must now compete with challenger banks offering electronic payment alternatives, peer-to-peer lending, and investment apps.

In the previous article, we learned what distributed tracing is, why it is necessary, how to do tracing, encountered challenges with existing tracing tools, and finally discovered that there is a more mature option available for the industry to adopt in terms of telemetry and observability. In this article, we will be trying to understand OpenTelemetry in more depth. To begin, we will examine how OpenTelemetry addresses some of the issues confronting the observability ecosystem.

Many organizations rely on distributed tracing in Datadog APM to gain end-to-end visibility into the performance of their Kubernetes applications. But as teams grow, it can become impractical for them to manually configure each new application with the libraries and environment variables needed for tracing.

Have you heard about traces? Most likely, yes! Do you confuse it with auditing? Hope not. Today, we're going to talk about tracing, specifically “Distributed Tracing,” and do a deep dive into it. Once we’re familiar with distributed tracing, we will show you how to implement it with OpenTelemetry - a new-age observability framework.

Effective log management is a fundamental aspect of maintaining and troubleshooting today's complex systems and applications. The sheer volume of data generated by various software and hardware components can make it challenging to identify and resolve issues in a timely manner. Open-source log management tools offer a cost-efficient and customizable approach for collecting, analyzing, and visualizing log data.

There are many - maybe too many - programming languages to choose from. One of the most effective ways to assess their popularity is by the number of search queries for each language, across the web. The TIOBE Index is the definitive list of programming languages, ranked in order of search volume popularity as an indication of prominence and public interest. This article lists the top 40 languages on that list, with a brief overview and their pros, cons, and hiring prospects.