The majority of users continually depend on a variety of web applications to meet their everyday needs, so a business’s success is now often proportionate to the success of its application performance. As a result, the importance of using an appropriate APM solution has become even greater to businesses globally. Application Performance Monitoring (APM) still continues to grow in popularity and is now considered a must for observing the health and performance of your organization's applications.

Whatever the size of your network, as an engineer you will often notice a significant amount of log data being generated. This data will require centralizing for further analysis and management, which can be particularly challenging if you have varying log formats, such as plain text or HTML.

Monitoring the key metrics of your application’s performance are essential to keep your software applications running smoothly as one of the key elements underpinning application performance monitoring. In this article, we will cover many of the key metrics that you should strongly consider monitoring to ensure that your next software engineering project remains fully performant.

When focusing on application performance monitoring (APM), there are currently many options to select from. With many of the tools available offering similar features, it can often be challenging to make an informed decision. One tool that appears to be on almost every list of ‘the best APM tools’ is Datadog.

In the context of application performance monitoring (APM) and observability, traces and spans are fundamental concepts that help users to track and understand the flow of requests and operations within a system. They are essential in assisting users to identify bottlenecks, troubleshoot issues, and optimize application performance.

MLOps stands for Machine Learning Operations. MLOps refers to the set of practices and tools that facilitate the end-to-end lifecycle management of machine learning models, from development and training to deployment and monitoring. The primary objective of MLOps tools is to address the unique challenges associated with deploying and managing machine learning models in real-world scenarios.