Behind the scenes of any IT infrastructure lies the ongoing challenge of monitoring and managing never-ending systems, applications, and network devices. As an IT professional, your task list always includes staying ahead of potential issues, minimizing downtime, and navigating disruptive alerts.

You can elevate your IT infrastructure monitoring with AIOps. AIOps offers full-stack visibility, enhancing IT infrastructure monitoring efforts. This lets you transform the familiar monitoring landscape by turning the chaos of constant alerts into a proactive approach to problem-solving. IT infrastructure monitoring challenges typically relate to the complexity of backend systems, especially when it comes to cloud platforms. For example, consider the following.

Over the last five years we’ve seen our customers run 583,954 incidents more efficiently thanks to a shared workspace, powerful Runbook automations, and auto-captured data. Yet despite a great deal of progress, incident efficiency hasn’t achieved peak potential. We talk to a lot of folks that are still stuck in the muck: new responders struggle to get up to speed quickly, incident commanders wade through post-incident drudgery, and knowledge silos prevent comprehensive improvements.

This post explains how databases optimize queries, which can result in queries running hundreds of times faster. While we focus on one specific query type that is important to InfluxDB 3.0, the optimization process we describe is the same for any database.

DevOps professionals know that ensuring optimal application performance is paramount. More and more customers and prospects interact with companies online, and any hiccup can impact your bottom line. What’s more, companies continue to leverage cloud-native apps for improved flexibility and resource optimization. All of which means that Application Performance Monitoring (APM) tools need to evolve.

As a new company poised to transform the financial services industry with its modern money movement platform, Moov wanted an equally modern observability platform as part of the company’s operational tech stack. With Moov's platform hosted in Google Cloud, it uses a diverse range of technologies to allow clients to accept, store, send, and spend money. The integration of numerous software providers further amplifies the complexity of each transaction.

Kubernetes has emerged as a pivotal force in shaping modern cloud infrastructure. Originating as a brainchild of Google, Kubernetes has evolved into an open-source platform that has revolutionized how applications are deployed, scaled, and managed across a vast network of machines. Its ability to orchestrate containerized applications efficiently makes it an indispensable tool within cloud computing. However, with great power comes great responsibility, particularly in the realm of cost management.

As organizations increasingly depend on distributed system architectures to provide modern applications and microservices, their legacy monitoring tools struggle to keep pace. These outdated systems are often based on predictable failures, but when an unforeseen performance issue occurs, it can lead to outages and unplanned downtime that impacts your customers and your business.

Wherever end-user success is critical to a business, your website’s functionality needs diligent testing. Therefore web application monitoring is required for many organizations. Conducting web application monitoring can also offer a whole host of additional advantages to organizations. For example, tracking user interactions and behaviors within the web application aids your organization in understanding how users engage with your application.

Four years ago, we stepped on some big toes with our developer-first performance monitoring. Since then thousands of software teams have adopted our modern APM solution. But while Performance checks off a lot of boxes, some dev teams juggle separate tools for metrics, leading to a fractured experience. And honestly, what good is a metric without all the context you get from Sentry? Not very – it makes tying problems back to underlying errors or performance issues unnecessarily difficult.