Without a doubt, you’ve heard about the persistent talent gap that has troubled the technology sector in recent years. It’s a problem that isn’t going away, plaguing everyone from engineering teams to IT security pros, and if you work in the industry today you’ve likely experienced it somewhere within your own teams. Despite major changes in the tech landscape, it is clear that organizations are still having significant difficulty keeping their technical talent in-house.

Splunk gives you amazing tools to analyze system data and make business-critical decisions, react to issues, and even proactively address future problems. And since it is so good at those things, it’s not surprising that there are myriad tools to help you reflexively analyze your own Splunk deployment.

Observability is the ability to understand the internal workings of a system by measuring and tracking its external outputs. In technical terms, it entails collecting and examining data from numerous sources within a system to attain insights into its behavior, performance, and health. All organizations are now familiar with how essential observability is to ensure optimal performance and availability of their IT infrastructure.

Mainframes might seem like relics of a bygone era to many of us in 2024, but the truth, however, is far from that. Despite their reputation as ancient behemoths—and frequent targets of jokes—mainframes continue to be vital powerhouses driving the global economy. Their capability to process billions of transactions daily, including the majority of credit card transactions, underscores their enduring significance.

In today's age of cloud services and SaaS platforms, continuous improvement isn't just a goal — it's a necessity. Here at Elastic, we're always on the lookout for ways to fine-tune our systems, be it our internal tools or the Elastic Cloud service. Our recent investigation in performance optimization within our Elastic Cloud QA environment, guided by Elastic Universal Profiling, is a great example of how we turn data into actionable insights.

Monitoring your data with dashboards and visualizations is perfect for improving the efficiency of your team and facilitating data-driven decisions from insights. They provide a different perspective to your data and by utilizing this data and trends you can clearly view if your system, application, or server is performing optimally, and if it isn’t performing as expected you can analyze where the issue is and promptly rectify this.

In the complex world of microservices and distributed systems, achieving transparency and understanding the intricacies and inefficiencies of service interactions and request flows has become a paramount challenge. Distributed tracing is essential in understanding distributed systems. But distributed tracing, whether manually applied or auto-instrumented, is usually rather coarse-grained.

Imagine a well-designed plumbing system with pipes carrying water from a well, a reservoir, and an underground storage tank to various rooms in your house. It will have valves, pumps, and filters to ensure the water is of good quality and is supplied with adequate pressure. It will also have pressure gauges installed at some key points to monitor whether the system is functioning efficiently. From time to time, you will check pressure, water purity, and if there are any issues across the system.

When discussing observability pricing models, there are three dimensions that must be considered The first, Cost per Unit, is an easy-to-understand metric, but in practice it is often overshadowed by a lack of transparency and predictability for other costs. The question is simple: how does a usage based pricing model impact these variables?