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As those of you who are following us here at Banzai Cloud may or may not be aware, we are in the middle of releasing/certifying our own Kubernetes distribution — Pipeline Kubernetes Engine (PKE). PKE will be orchestrated the same way as other providers already supported by Pipeline, and will benefit from/inherit those features of the Banzai Cloud Pipeline platform that you already know and love. If you’re interested in learning more about PKE and our vision for buidling multi and hybrid cloud managed (application) environments, please read this post

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Enterprises often use heterogenous clusters to deploy their applications to Kubernetes, because those applications usually have needs that involve special scheduling constraints. Pods may require nodes that have special hardware, that are isolated, or that are colocated with other pods running within a system. To help our customers solve these problems, the Banzai Cloud Pipeline platform uses nodepools. A nodepool is a subset of node instances within a cluster with the same configuration, however, the overall cluster can contain multiple nodepools as well heterogenous nodes/configurations.

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Spotguides are one of the most useful features of the Banzai Cloud Pipeline platform. Spotguides are managed application environments, which provide an easy and scalable way of deploying applications, and programmatically take care of all the necessary “plumbing” that is critical for production (if you missed our introductory series on Spotguides, you can catch up here). A Spotguide defines a template for provisioning cloud resources (Kubernetes clusters, object stores, logging, monitoring, security, etc.

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Suppose you’re working on a project which is running on Kubernetes - like we usually do - and you would like to test out this project on each and every pull request or commit. You can write many unit and integration tests, but at the end of the day, the proof of the pudding is in the eating. A real test would be to start up the application on the same platform where it will end up being deployed in production (in this case Kubernetes) and exercise it with some real workloads (aka end-to-end tests).

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In December 2018 we released the public beta of Pipeline and introduced a Banzai Cloud terminology - spotguides. We have already gone deep into what Spotguides were and how they supercharged Kubernetes deployments of application frameworks (automated deployments, preconfigured GitHub repositories, CI/CD, job specific automated cluster sizing, Vault based secret management, etc.). This post is focused on one specific spotguide: Spark with HistoryServer. Since the very early days, one of the most popular deployments to Kubernetes has been Apache Spark.

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The following is a guest blog post from Robbie Blaine, Site Reliability Engineer at EOH Big Data Lab. Contributions from the community are a key factor in driving our products forward. BIG thanks to all of you who have engaged with us by raising issues, giving feedback, or creating pull requests. Keep them coming, we love them! Automating Vault Deployment and Configuration on OKD with Bank-Vaults Hashicorp Vault is an Encryption-as-a-Service tool that is used to securely store and access secrets.

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Our last two blog posts about the Kubernetes scheduler explained how taints and tolerations and different types of affinities are working. In today’s post we are going one layer deeper and we’ll discuss how to implement and deploy a custom Kubernetes scheduler. Writing a scheduler may sound intimidating at first, but if you’ll follow this article you’ll realise that creating something that works and schedules pods based on some simple rules is quite easy.

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If you’re a frequent reader of this blog, you may have already seen a “short” description of what our platform does. It usually goes something like this: Banzai Cloud Pipeline is a solution-oriented application platform which allows enterprises to develop, deploy and securely scale container-based applications in multi- and hybrid-cloud environments. We frequently elaborate on this by providing a list of key features: Banzai Cloud Pipeline leverages best-of-breed cloud components, such as Kubernetes and adds a unified system architecture that enables a highly productive, yet flexible environment for developers and operations teams alike.

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One of the main features of the Banzai Cloud Pipeline platform is that it allows enterprises to run workloads cost effectively by mixing spot or preemptible instances with regular ones, without sacrificing overall reliability. The platform allows enterprises to develop, deploy and scale container-based applications and it leverages best-of-breed cloud components, such as Kubernetes, to create a highly productive yet flexible environment for developers and operation teams alike. tl;dr The Banzai Cloud Pipeline platform switched to a unified, cloud-aware spot instance termination handler to properly drain the cluster node and provide information to the monitoring system if an instance is going to be preempted from a cluster nodepool.

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About a year ago we published a blog post on Spotguides, a core feature of the Banzai Cloud Pipeline platform. We spent a lot of time using and refining the original ideas, and as a result, many things changed since we first introduced the concept. In this blog post we’ll learn about how Spotguides are used to easily deploy and manage complex cloud-native application stacks. What are Spotguides? At Banzai Cloud most of the project names and terminologies are borrowed from surfing (yes, few of us are eager surfers).

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