CloudHedge Transform provides the user with the option of modifying the data that goes into the container. It uses the data gathered from the X-Ray, a part of the CloudHedge Discover module that has been performed on the process. Using the Transform Platform, the user can currently: The Edit Dockerfile feature can be used to: And the File Selection feature can be used to.
Welcome to part 2 of our blog series, where we go through how to forward container logs from Amazon ECS and Fargate to Splunk. In part 1, "Splunking AWS ECS Part 1: Setting Up AWS And Splunk," we focused on understanding what ECS and Fargate are, along with how to get AWS and Splunk ready for log routing to Splunk’s Data-to-Everything platform.
With build-or-buy decisions, it often comes down to an all-in-one platform or a mixture of best-of-breed technologies. With open-source technology companies can actually get the best of everything. So, why not roll your own platform based on top-notch technologies? The real question is whether enterprises can afford to. Open-source software is free to use, but teams have to invest quite a bit in selecting, introducing, using, and maintaining these technologies.
As a team we have spent many years troubleshooting performance problems in production systems. Applications have gotten so complex you need a standard methodology to understand performance. Fortunately right now there are a couple of common frameworks we can borrow from: Despite using different acronyms and terms, they fortunately are all different ways of describing the same thing.
Let’s face it. Once you have consumed your free credit, AWS costs an arm and a leg. This is the price to pay for high-quality services. But how can you reduce your costs without sacrificing quality? This post will show you how to reduce your bill by up to 60% by combining four built-in features in Qovery. There are three categories of costs on AWS. The “data transfer”, the “compute”, and the “storage” costs.
In this episode of Coffee & Containers, North American DevOps Group‘s Jim Shilts speaks with Shipa‘s Bruno Andrade and Fiserv‘s Ken Owens. The topics covered include.
AI is everywhere. Except in many enterprises. Going from a prototype to production is perilous when it comes to machine learning: most initiatives fail, and for the few models that are ever deployed, it takes many months to do so. While AI has the potential to transform and boost businesses, the reality for many companies is that machine learning only ever drips red ink on the balance sheet.
Following the GA of Kubernetes 1.19 support in AWS, EKS-optimized Ubuntu images for 1.19 node groups have been released. The ami-id of this image for each region can be found on the official site for Ubuntu EKS images.
The latest versions of Konvoy and Kommander are now generally available: the D2iQ Kubernetes Platform (DKP) continues on its relentless roll. DKP is the leading independent Kubernetes platform for enterprise grade production at scale and Konvoy and Kommander are the reason why. You can learn more about Konvoy here, Kommander here, and our general approach here.
Admission controllers are a powerful Kubernetes-native feature that helps you define and customize what is allowed to run on your cluster. As watchdogs, they can control what’s going into your cluster. They can manage deployments requesting too many resources, enforce pod security policies, and even block vulnerable images from being deployed. In this article, you’ll learn what admission controllers are in Kubernetes and how their webhooks can be used to implement image scanning.
