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One of the key features of our container management platform, Pipeline, as well as our CNCF certified Kubernetes distribution, PKE, is their ability to form and run seamlessly across multi- and hybrid-cloud environments. While the needs of Pipeline users vary depending on whether they employ a single or multi-cloud approach, they usually build upon one or more of these key features: Multi-cloud application management An Istio based automated service mesh for multi and hybrid cloud deployments Federated resource and application deployments built on Kubernetes federation v2 As Istio operator-based multi-cluster and multi/hybrid-cloud adoption increased, so did the demand for the ability to run distributed or decentralized applications wired into a service mesh.

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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 opensourced our Kafka operator, the engine behind our Kafka Spotguide - the easiest way to run Kafka on Kubernetes when it’s deployed to multiple clouds or on-prem, with out-of-the-box monitoring, security, centralized log collection, external access and more. One of our customers’ preferred features is the ability of our Kafka operator to react to custom alerts, in combination with the default options we provide: options like cluster upscaling, adding new Brokers, cluster downscaling, removing Brokers or adding additional disks to a Broker.

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Yes, we opensourced yet another Apache Kafka operator for Kubernetes. This might seem bizarre, considering the alternatives that are already available (they exist but there are not too many), so you may be wondering, ‘Why?’ Well, keep reading and we’ll tell you: from design gaps and features we believe are necessary to operate Kafka on K8s, through my personal fix for Envoy, to some of our specific usage scenarios.

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Two weeks ago we introduced our Kafka Spotguide for Kubernetes - the easiest way to deploy and operate Apache Kafka on Kubernetes. Since then, it’s been integrated into our application and DevOps container management platform, Pipeline, among other spotguides such as Spark on Kubernetes, Zeppelin, NodeJS and Golang, just to name a few. Because we’ve already met our goal of making it easy set up a Kafka cluster on Kubernetes with just few clicks, and in less than ten minutes - provisioning and operating its entire infrastructure, both in Kubernetes and Kafka - we’ve shifted our focus to Kafka security.

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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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If you are looking to try out an automated way to provision and manage Kafka on Kubernetes, please follow this Kafka on Kubernetes the easy way link. At Banzai Cloud we use Kafka internally a lot. We have some internal systems and customer reporting deployments where we rely heavily on Kafka deployed to Kubernetes. We practice what we preach and all these deployments (not just the external ones) are done using our application platform, Pipeline.

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Kubeless was designed to be a Kubernetes-native serverless framework, and, for PubSub functions, uses Apache Kafka behind the scenes. At Banzai Cloud we like cloud-native technologies, however, we weren’t happy about having to operate a Zookeeper cluster on Kubernetes, so we modified and open-sourced a version for Kafka in which we replaced Zookeeper with etcd, which was (and still is) a better fit. This post is part of our serverless series, which discusses deploying Kubeless, using Kafka on etcd with Pipeline, and deploying a so called PubSub function.

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Monitoring series: Monitoring Apache Spark with Prometheus Monitoring multiple federated clusters with Prometheus - the secure way Application monitoring with Prometheus and Pipeline Building a cloud cost management system on top of Prometheus Monitoring Spark with Prometheus, reloaded Kafka on Kubernetes the easy way At Banzai Cloud we provision and monitor large Kubernetes clusters deployed to multiple cloud/hybrid environments, using Prometheus. The clusters, applications or frameworks are all managed by our next generation PaaS, Pipeline.

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At Banzai Cloud we are building a cloud agnostic, open source next generation CloudFoundry/Heroku-like PaaS, Pipeline, while running several big data workloads natively on Kubernetes. Apache Kafka is one of the cloud native workloads we support out-of-the-box, alongside Apache Spark and Apache Zeppelin. If you’re interested in running big data workloads on Kubernetes, please read the following blog series as well. Apache Kafka on Kubernetes series: Kafka on Kubernetes - using etcd Monitoring Apache Kafka with Prometheus Kafka on Kubernetes with Local Persistent Volumes Kafka on Kubernetes the easy way

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