Without collaborations between organizations, the tech industry wouldn’t be where it is today. Customer expectations and needs don’t exist in a silo. They need their tools to work together to solve problems and deliver value regardless of the vendor. With data growth at a 28% CAGR and cybersecurity threats on the rise, customers need their entire suite of tools working for them in a cohesive manner.

KubeCon Europe 2024 in Paris was the biggest event of the Cloud Native Computing Foundation (CNCF) to date. With over 12,000 participants, it was a monumental event, setting the stage for the latest trends and developments in cloud-native computing. As your loyal CNCF Ambassador, I’m here to share some of the important updates you don’t want to miss. I also invited fellow CNCF Ambassador Thomas Schuetz to join me with his own insights.

Monitoring tools are critical for DevOps, enabling them to quickly find and rectify performance issues. With the increasing popularity of Docker, it has become crucial that organizations can effectively monitor these containers. But, as monitoring Docker containers is particularly complex, developing a strategy and an appropriate monitoring system is not simple. However, this process can be streamlined by utilizing a Docker monitoring tool.

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.