Recently we’ve explored the evolving role of Kubernetes as a full ecosystem, rather than just a platform, diving into the power and complexity of add-ons. These tools, as highlighted previously, are key to augmenting Kubernetes core capabilities, and adding-on (as their name implies) essential capabilities not supported directly by Kubernetes itself.

Managing DNS records in Kubernetes at scale is complex, especially as clusters grow and the number of applications increases. Enter ExternalDNS—a tool designed to automate DNS record synchronization with Kubernetes resources, providing the agility and scalability needed for modern application environments.

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.

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.

Edge computing is a decentralized computing approach that processes data and performs computations near the source of the data—local servers, smart devices, or edge nodes—rather than depending on a centralized data center. This reduces latency, which is often a result of data having to travel long distances and improves bandwidth efficiency. Discover how edge computing and Kubernetes revolutionize data processing and deployment.