Dynatrace and New Relic are popular monitoring and observability tools for monitoring your applications and infrastructure. In this post I have compared both Dynatrace and New Relic based on important features like application performance monitoring, log management, and infrastructure monitoring. Even though both tools offer a lot of similar features, they have some key differences. We’ll explore these differences to help you choose the tool that best fits your needs. Let's get started.

Are you struggling to choose between Datadog and Sentry for your application monitoring needs? Datadog and Sentry are widely recognized for their roles in monitoring applications. Although they share some common features, they serve distinct purposes. Datadog excels in overseeing application performance and providing comprehensive system observability, while Sentry specializes in pinpointing and reporting application errors.

Developers have created many applications over time, and they all agree that the success of their creations depends on how well the applications perform after they are deployed. Therefore, it's crucial to monitor these applications carefully. Addressing performance issues and bugs promptly is essential because if an application doesn't perform well, it can indicate potential failures in a business context as well. We have outlined ten of the top-performing APM Solutions in this blog.

This architecture allows Node.js to handle thousands of concurrent connections with low overhead, making it highly efficient for building high-performance web servers and microservices. With many such systems adopting microservices and going for a distributed architecture, the necessity to monitor them has also increased. It is difficult to monitor every transaction and how it interacts with other services, so a distributed tracing system deserves a lot of attention here.

Imagine you're managing an e-commerce website that experiences occasional performance issues, resulting in slow page loading times and irregular outages. Without a monitoring tool, you're left in the dark about the root cause of these disruptions, leading to frustrated customers, lost sales, and damage to your brand reputation.

When deploying a Node.js function in the cloud, you might initially think of traditional methods involving web servers and other infrastructure. However, if your application suddenly faces a surge in traffic—thousands or even millions of requests—it could crash if it's unable to handle the load. This is where AWS Lambda shines. AWS Lambda allows developers to run code without provisioning or managing servers.

If your application is as busy as a highway with multiple lanes, intersections, and exits, imagine trying to track the journey of a single car from start to finish. Sounds tricky, right? Well, that's what happens when you're dealing with modern, complex software systems. Enter distributed tracing, your trusty GPS for navigating the intricate web of microservices and dependencies within your applications.

Datadog is widely recognized for its comprehensive range of products and tools, making it quite a challenge to find a suitable alternative. When seeking an alternative to Datadog, it's essential to conduct a thorough comparison of features, performance, limitations, and other vital aspects. This task requires a deep dive into the details, and it might not be as straightforward as it seems at first glance.

In today's tutorial, we're diving into the world of container orchestration by setting up a Docker Swarm on AWS EC2 instances. Docker Swarm allows you to manage a cluster of Docker hosts as a single virtual system, making it easier to deploy and scale your containerized applications. By leveraging the power of AWS EC2, we'll walk through each step to get your Swarm up and running smoothly.

Logging is essential in Node.js for tracking errors, monitoring performance, and debugging issues. Traditional node.js logging methods, like using console.log(), are often insufficient due to unstructured, cluttered logs that are hard to read. They lack features like log levels, proper formatting, and efficient storage management. Best practices for logging ensure logs are useful, structured, and manageable. Implementing these best practices is crucial for several reasons.