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If you’re reading this post, you’re likely already familiar with our container management platform, Pipeline, and our CNCF certified Kubernetes distribution, PKE: you probably already know how we make it possible to spin up clusters across five cloud providers and on-premise, in multi-cloud but also hybrid-cloud environments. But whether these are single or multi-cluster topologies, resilience is key. We at Banzai Cloud believe this is the case not just for infrastructural components but for entire managed application environments, like Apache Kafka.

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A few weeks ago we announced a new version of Pipeline, the hybrid any-cloud platform. This post is part of a series of posts highlighting the multi- and hybrid-cloud features on that platform. Today, we will be focusing specifically on multi-cloud features. Before we take a deep dive into our technical content, let’s go over some of the key expectations an enterprise has when it embraces a multi-cloud strategy:

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There has been a lot of talk about multi- and hybrid-cloud deployments over the past years. Some cloud vendors see these trends as a threat, others look at them as an opportunity. We think that beneath the buzzwords lie some very important use-cases driven by the needs of enterprises and SaaS providers. However, delivering and operating multi- and hybrid-clouds had been too complex for most organizations so far. The use-cases that we’ve seen at customers broadly relate to three main areas: flexibility, cost optimization, and compliance.

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Every major cloud provider offers a managed Kubernetes service that aims to simplify the provisioning of Kubernetes clusters in its respective environment. The Banzai Cloud Pipeline platform has always supported these major providers - AWS, Azure, Google, Oracle, Alibaba Cloud - turning their managed k8s services into a single solution-oriented application platform that allows enterprises to develop, deploy and securely scale container-based applications in multi-cloud environments. While this was very appealing from the outset, we quickly realized there was demand among our enterprise users to implement more sophisticated use cases that were limited by our initial approach.

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In our last post about using Cadence workflows to spin up Kubernetes we outlined the basic concept of Cadence and walked you through how to use the Cadence workflow engine. Let’s dive into the experiences and best practices associated with implementing complex workflows in Go. We will use the deployment of our PKE Kubernetes distribution, from Pipeline to AWS EC2 as an example. Of course, you can deploy PKE independently, but Pipeline takes care of your cluster’s entire life-cycle , starting from nodepool and instance type recommendations, through infrastructure deployment, certificate management, opt-in deployment and configuration of our powerful monitoring, logging, service mesh, security scan, and backup/restore solutions, to the scaling or termination of your cluster.

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One of the main goals of the Banzai Cloud Pipeline platform and PKE Kubernetes distribution is to radically simplify the whole Kubernetes experience and execute complex operations on behalf of the users. These operations communicate with a number of different remote services (from cloud providers to on-prem virtualization or storage providers) where we have little or no way to influence the result of these calls: how long will it take, will it ever succeed, whether it provides the desired result and so.

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Banzai Cloud is proud to announce that our open source Pipeline Kubernetes Engine is now a CNCF Certified Kubernetes Distribution! PKE is an extremely simple Kubernetes installer and distribution, designed to work anywhere, and is the preferred run-time of Banzai Cloud’s cloud native application and devops container management platform, Pipeline. Banzai Cloud Pipeline supercharges the development, deployment and scaling of container-based applications with native support for multi-, hybrid-, and edge-cloud environments.

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One of the key features of the Pipeline platform is its ability to automatically provision, manage, and operate different application frameworks through what we call spotguides. Among the many spotguides we support on Kubernetes (Spark, Zeppelin, NodeJS, Golang, even custom frameworks - to name a few) Apache Kafka is among the most popular. We are heavily invested in making it as easy and straightforward as possible to operate Apache Kafka automatically on Kubernetes, and we believe that our current Apache Kafka Spotguide does just that.

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At Banzai Cloud we try to provide our users with a unified, cloud and on-premise-agnostic authentication and authorization mechanism. Note that our Pipeline platform supports cloud provider-managed Kubernetes and, as of recently, our own Kubernetes distribution - the Pipeline Kubernetes Engine, PKE. We also recently introduced an open source project, JWT-to-RBAC (you can read more about that project, here), designed to solve authentication and authorization challenges within the Pipeline platform in a cloud provider-agnostic way.

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If you’re a Node.js developer, it’s very likely that you are building microservices and have already come across Kubernetes. Kubernetes is a solid foundation for scalable container deployments but is also infamous for it’s steep learning curve. This post will explain how to containerize your Node.js applications and what it will take to make them production-ready on Kubernetes. While this post can be used as a tutorial for DIY jobs, we have also automated this entire process, allowing developers to kickstart their code to production experience with a few clicks.

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