AWS ALB was developed to simplify the management of modern web traffic while enhancing application scalability, security, and performance. And like any other AWS service, understanding AWS ALB pricing is essential for managing cloud costs effectively. This guide offers a comprehensive overview of AWS ALB, detailing its key use cases and pricing structure.

Red Hat OpenShift is an open-source container platform that provides a complete environment for developers to create, launch, and manage applications in both cloud and on-premises settings, leveraging the potential of container technology. With increasing popularity in container orchestration, OpenShift's strong scalability and features attract developers and IT experts. Nonetheless, the intricacy and changing aspects of OpenShift setups demand a tailored strategy for monitoring and observation.

DevOps is essential for engineering teams looking to enhance collaboration, speed up development cycles, and maintain high-quality software delivery. Building on our previous discussion of common DevOps mistakes startups make, let's move on to ten practical strategies small engineering teams can adopt to foster a robust DevOps culture.

Over the last few weeks I've been talking about the key differences between Amazon EKS and Cycle. If you happened to miss it and want to catch up before diving into this post you can check it out here: This post will round out the series by taking a look at how worker nodes are added to a cluster and the major differences between EKS and Cycle there.

Load balancers are some of the most important load-bearing (pun intended) components in cloud environments. They perform multiple critical tasks: network switching, packet inspection, and of course, routing. Most cloud-based load balancers focus on load balancing within a single zone, but what if you have resources spread across multiple zones?

Robotic process automation (RPA) initiatives often falter because enterprise application leaders choose use cases that involve intricate business logic, extended workflows, and require significant orchestration or additional technologies to deliver business value. In essence, RPA is not well-suited for complex workflows, lengthy processes, and those requiring extensive coordination.

Over the past six years at Megaport, I’ve witnessed a growing desire for change in the Financial Services (FS) industry, particularly regarding application and data mobility within organizations and with third-party partners. Initially, many in FS approached cloud adoption tentatively, waiting until it was fully matured and trusted across the industry. Today, however, the FS sector is one of the largest users of cloud services globally.

As Data Scientists and Engineers in a fast-paced corporate environment, our daily activities are filled with numerous challenges. One of the most daunting obstacles we face is the management of infrastructure and related costs. Each task or model we work on must be scheduled meticulously, cost-optimized, and equipped with the right CPU and memory specifications. On top of that, we must also supervise their dependencies.

The introduction of ChatGPT two years ago caused sharply increased interest in (and use of) large language models (LLMs), followed by a crush of commercial and open source competition. Now, companies are rushing to develop and deliver applications that use LLM APIs to provide AI functionality.

Earlier this year at Mobile World Congress (MWC) 2024 in Barcelona, Canonical announced the availability of Real-time Ubuntu on Amazon Elastic Kubernetes Services Anywhere (EKS Anywhere). With this technology enablement, a telecom operator can confidently run its Open Radio Access Network (RAN) software workloads on Amazon EKS Anywhere, thanks to the necessary support for ultra low-latency data processing with high reliability in Real-time Ubuntu.