Both DataDog and Jaeger are tools used to monitor application performance. The difference lies in what they monitor and terms of usage. Jaeger is an open-source tool focused on distributed tracing of requests in a microservice architecture. While DataDog is a SaaS APM vendor covering most monitoring needs of an application. Application performance monitoring is the process of keeping your app's health in check. APM tools enable you to be proactive about meeting the demands of your customers.

Modern digital organizations have rapidly adopted microservices-based architecture for their applications. Distributed tracing tools help monitor microservices-based applications. Choosing the right distributed tracing tool is critical. How do you know which is the right one for you? In this post, we will cover the top 13 distributed tracing tools in 2024 that can solve your monitoring and observability needs.

Nextjs is a production-ready React framework for building single-page web applications. It enables you to build fast and user-friendly static websites, as well as web applications using Reactjs. Using OpenTelemetry Nextjs libraries, you can set up end-to-end tracing for your Nextjs applications. Nextjs has its own monitoring feature, but it is only limited to measuring the metrics like core web vitals and real-time analytics of the application.

OpenTelemetry is a Cloud Native Computing Foundation(CNCF) incubating project aimed at standardizing the way we instrument applications for generating telemetry data(logs, metrics, and traces). However, OpenTelemetry does not provide storage and visualization for the collected telemetry data. And that’s where an OpenTelemetry backend is needed. Cloud computing and containerization made deploying and scaling applications easier.

Log analysis is a very powerful feature for an application when it comes to debugging and finding out which flow is working properly in the application and which is not. In a world of containerization and cloud computing, it is essential to understand logs generated by a Docker environment to maintain healthy performing applications. In this article, we will discuss log analysis in Docker and how logging in Docker containers is different than in other applications.

Kubernetes has since emerged as “THE” container orchestration platform for deploying and managing containerized workloads as a result of its robust capabilities. However, the complexity of its architecture and its dynamic nature present significant challenges in monitoring deployed workloads and the platform itself. Kubernetes monitoring is crucial for maintaining the health, performance, and reliability of containerized applications.

Modern-day software systems emit millions of log lines per minute. Cloud computing and containerization have made it easy to have distributed systems. Distributed systems emit logs from multiple sources. While developers have always used logs to debug stand-alone applications, centralized logging solves the challenges of modern-day distributed software systems.

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.