The cloud revolution has transformed technology landscapes, but managing its complexities poses its own challenges. Here comes Kubernetes, the container orchestration platform empowering rapid application development and deployment. Yet, with diverse options like VMware Tanzu and Red Hat OpenShift, choosing the right tool can be overwhelming.

The promise of cloud computing has always been about flexibility and cost-effectiveness. Yet many organizations find themselves trapped in a cycle of unpredictable costs and underutilized/overutilized resources. The culprit? A lack of understanding about the power of scaling within platforms like Amazon’s Elastic Kubernetes Service (EKS).

Mock services are a powerful tool for accelerating software development. Learn the top use cases for mock APIs in our blog.

Containerization has become a cornerstone of modern software development and deployment. Docker, a leading containerization platform, has revolutionized the way applications are built, shipped, and deployed. As a DevOps engineer, mastering Docker and understanding best practices for Dockerfile creation is essential for efficient and scalable containerized workflows. Let’s delve into some crucial best practices to optimize your Dockerfiles.

Kubernetes, with its robust, flexible, and extensible architecture, has rapidly become the standard for managing containerized applications at scale. However, Kubernetes presents its own unique set of access control and security challenges. Given its distributed and dynamic nature, Kubernetes necessitates a different model than traditional monolithic apps.

As technology takes the driver’s seat in our lives, Kubernetes is taking center stage in IT operations. Google first introduced Kubernetes in 2014 to handle high-demand workloads. Today, it has become the go-to choice for cloud-native environments. Kubernetes’ primary purpose is to simplify the management of distributed systems and offer a smooth interface for handling containerized applications no matter where they’re deployed.

In the dynamic world of containerized applications, effective monitoring and optimization are crucial to ensure the efficient operation of Kubernetes clusters. Metrics give you valuable insights into the performance and resource utilization of pods, which are the fundamental units of deployment in Kubernetes. By harnessing the power of pod metrics, organizations can unlock numerous benefits, ranging from cost optimization to capacity planning and ensuring application availability.

Whenever it comes to a small-scale private cloud infrastructure project roll-out, organisations usually face a serious dilemma. The implementation process often seems complex due to a lack of knowledge, tricky migrations and an immediate need from management to run various extensions, such as Kubernetes, on top.

Cost optimization in cloud environments is no longer a luxury — it’s a necessity. As businesses increasingly migrate to AWS, they are met with the difficult task of ensuring robust performance and scalability, all while keeping an eagle eye on costs. How does one achieve this delicate balance? How can organizations ensure that their AWS architectures are both high-performing and cost-efficient?

Recently, we wrote a great article about Platform Engineering and how to get started; well, now that you know much more about it, you might want to explore the best Platform Engineering tools to use in 2024. If that’s the case, this article is for you.