Operations | Monitoring | ITSM | DevOps | Cloud

One of the real successes of the Agile Software development movement has been the push to have regular, frequent deployments. This has manifested as build and deployment automation and the general adoption of CI/CD. As engineers automate more processes of their software release lifecycle, an important question is how to automate Quality Assurance, which includes resilience testing and, more specifically, Fault Injection.

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.

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.

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.

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.

In today’s digital landscape, the performance and reliability of messaging systems are paramount for business operations. Systems like IBM MQ play a crucial role in ensuring seamless communication between different parts of an application, impacting everything from transaction processing to customer experiences. To optimize these systems, it’s essential to focus on robust monitoring, efficient troubleshooting, and effective tuning techniques.

Rules are excellent when you know precisely what you want to match, typically based on experience. Yet rules only let you observe what you have learned to look for. This is where artificial intelligence (AI) and machine learning (ML) contribute significantly to observability – detecting errors and early warning signs that were previously unobservable. LM Envision supports metric and log anomaly detection. This blog discusses how LM Envision Log Anomalies uncovers previously unknown anomalies.

As of 2023, 89% of companies are using a multi-cloud approach. The race to the cloud might feel truly like that – a race – but the finish line isn’t hybridization or even a full migration. Even after you’ve made it to the cloud, there’s still more work. Namely, determining cloud costs. Microservices, containers, Kubernetes made resource costs, associated costs and more are near-impossible to sort though.