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It’s been some time since we open sourced our Kafka Operator, an operator designed from square one to take advantage of the full potential of Kafka on Kubernetes. That guiding principle was what led us to use simple pods instead of StatefulSet. This blog will not detail our every design decision, so if you are interested in learning more, feel free to look at an earlier blog post about the operator.
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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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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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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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Satellite is a Golang library and RESTful API that determines a host’s cloud provider with a simple HTTP call. Behind the scenes, it uses file systems and provider metadata to properly identify cloud providers. When we started to work on Pipeline and the Banzai Cloud Pipeline Platform Operators, we soon realized how frequently we would need to find out which cloud provider the service was actually running on. Note that Pipeline supports 6 different cloud providers
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At Banzai Cloud we are building a feature-rich enterprise-grade application platform, built for containers on top of Kubernetes, called Pipeline. Applications deployed to Pipeline automatically inherit the platform’s features: enterprise-grade security, observability (centralized log collection, monitoring and tracing), discovery, high availability and resiliency, just to name a few - encapsulated in spotguides. One of the most popular spotguides we deploy is Spark. In the past few months we’ve been working and pushing many pull requests to make Spark a first class player on Kubernetes and to make it resilient.
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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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