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At Banzai Cloud we are building a managed Cloud Native application and devops platform called Pipeline. Pipeline supercharges the development, deployment and scaling of container-based applications with native support for multi and hybrid-cloud environments. The Pipeline platform provides support for advanced scheduling that enables enterprises to run their workflows in an efficient way by scheduling workflows to nodes that meet the needs of the workflow (e.g.: CPU, memory, network, IO, spot price, etc).

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Banzai Cloud’s Pipeline platform allows enterprises to develop, deploy and scale container-based applications on six cloud providers, using multiple Kubernetes distributions. One significant difference between the cloud providers that support Kubernetes (we support ACSK, EKS, AKS, GKE, DO and OKE) and our own Banzai Cloud Pipeline Kubernetes Engine is our ability to access the Kubernetes API server, and to configure it. Whether our enterprise customers are using Banzai Cloud’s PKE distribution in a hybrid environment, or cloud provider-managed Kubernetes, they demand we meet the same high standards - the ability to authenticate and authorize (e.

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One of the Banzai Cloud Pipeline platform’s key open-source projects is Bank-Vaults - the Vault swiss-army knife (and more) for Kubernetes. Feature requirements are part of the Pipeline platform, and the relatively large community around Bank-Vaults also has its own use cases and requirements. We’ve received lots of external contributions (thank you!), and we continue to find time to work on our community-driven features. While there have been many besides, these are the most sought-after features of the last few weeks.

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As a tech startup our main focus is on delivering value to our users and customers which means that the Developer Experience often comes second. Since we launched our beta platform Pipeline, we received an enormous amount of attention not just from users and customers, but from developers as well. Based on your feedback we’ve started to formalize our development processes. This post contains instructions for setting up a basic development environment and explains our development workflow through a simple pull request as an example.

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At Banzai Cloud we’re building a managed Cloud Native application and devops platform, called Pipeline. Pipeline supercharges the development, deployment and scaling of container-based applications with native support for multi- and hybrid-cloud environments. Pipeline’s built-in CI/CD solution is capable of creating Kubernetes clusters, running and testing builds, packaging and deploying applications as Helm charts, and lots more—all while its secrets are stored and managed by Vault. If you’d like to read more about the CI/CD system’s other features, such as native Kubernetes support, unprivileged builds and more, please read this post.

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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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