Operations | Monitoring | ITSM | DevOps | Cloud

Infrastructure management is critical for companies building and scaling applications. Traditional managed services (MSPs) handle these tasks externally but often come with high costs, slow execution, and limited flexibility. For teams needing control, speed, and efficiency, DevOps automation platforms might offer a much better alternative.

Managing TLS certificates in Kubernetes is no small feat, and the complexity only grows when you’re dealing with multiple clusters. Ensuring secure communication, automating certificate renewals, and integrating with external Certificate Authorities (CAs) are just a few of the challenges Kubernetes administrators, DevOps engineers, and security professionals face.

As businesses evolve in today’s digital landscape, the need for efficient and scalable computing resources has become paramount. In the early days of the Internet, large corporations would build or rent out large data centers to run their applications and serve customers. This was great as they could use dedicated hardware and expand as they pleased.

Technology is advancing rapidly, and with it comes a growing demand for powerful computers, especially in fields such as machine learning (ML), artificial intelligence (AI), and high-performance computing. As these areas develop, the size and complexity of the data they handle also increases. This surge in computing power requirements necessitates new methods for processing large amounts of data efficiently, without sacrificing accuracy or speed.

Artificial intelligence (AI) and machine learning operations (MLOps) have become crucial across a wide swath of industries, with the two technologies working in tandem to provide value. AI enables data-driven insights and automation, while MLOps ensures efficient management of AI models throughout their lifecycle. With AI’s growing complexity and scale, organizations need robust infrastructure to manage intensive computational tasks, giving rise to platforms like Kubernetes.

As someone deeply embedded in the cloud-native space, I’ve witnessed how the rapid evolution of cloud computing has unlocked remarkable opportunities—but also introduced significant challenges. Recently, I revisited some research we conducted back in 2022, which revealed a critical trend: despite the hundreds of services offered by the major cloud providers (the Big Three hyperscalers), most businesses use fewer than 10 services from their cloud provider.

It’s time to stop proclaiming that “cloud native is the future”. Kubernetes has just celebrated its 10 year anniversary, and 76% of respondents to the latest CNCF Annual Survey reported that they have adopted cloud native technologies, like containers, for much or all of their production development and deployment. Cloud native isn’t the future – it’s here and now. Data-intensive workloads are no exception.

Docker has transformed how we deploy, manage, and scale applications. As applications grow in complexity, the need for effective orchestration increases. This is where Docker Swarm comes into play. Docker’s native clustering and orchestration tool simplifies the management of multi-container applications. Together, Docker and Docker Swarm form a powerful combination for building and scaling modern, distributed systems.

Kubernetes is known to be the best container orchestration tool, but it can also add complexity to resource management, particularly as your clusters expand. Without proper monitoring, problems can rapidly worsen, resulting in subpar application performance, service interruptions, and higher expenses. In this blog, you will learn the key metrics for monitoring Kubernetes performance and how monitoring these can assist you in maintaining optimal performance in your environment.