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According to leading analyst firm Gartner, “80% of software engineering organizations will establish platform teams as internal providers of reusable services, components, and tools for application delivery…” by 2026. The ability to effectively manage multi-cluster infrastructure is critical to consistent and scalable service delivery.
A platform encompasses a set of tools, services, and infrastructure that enables developers to build, test, and deploy software applications. Other aspects of the discipline — such as infrastructure as code, automation, and standardization — reduce extraneous manual processes to increase developer productivity.
during 2021–2026. Organizations can be more agile when they have access to real-time data about their IT infrastructure. When IT teams move from manual log management tasks, they can focus on more strategic work, such as product innovation and optimizing infrastructure. Free IT teams to focus on and support product innovation.
For pre-existing contractual arrangements with service providers, the standard will only apply from the next contract renewal date or 1 July 2026, whichever comes first. Unified observability is the ability to know how systems and infrastructure are performing based on the data they generate, such as logs, metrics, and traces.
billion by 2026 according to numerous market research reports. This includes troubleshooting issues with software, services, and applications, and any infrastructure they interact with, such as multicloud platforms, container environments, and data repositories. In fact, the global log management market is expected to grow from 1.9
Gartner® predicts that by 2026, 40% of log telemetry will be processed through a telemetry pipeline product, up from less than 10% in 2022.* Application performance monitoring (APM) , infrastructure monitoring, log management, and artificial intelligence for IT operations (AIOps) can all converge into a single, integrated approach.
Nothing is more discouraging than the idea that it will take tens of millions of dollars to train a model and billions of dollars to build the infrastructure necessary to operate it. What about computing infrastructure? Jevons paradox has a big impact on what kind of data infrastructure is needed to support the growing AI industry.
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