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With KubeCon North America 2019 drawing near, we are excited to share the latest developments from Banzai Cloud. Stop by our booth SE74 to see what exciting new products Banzai Cloud brings into the fold. Don’t miss the opportunity to connect with our team, register for 1:1 sessions, and experience the magic of Hungarian pálinka (it will be under the table, code word: pah-lyn-kah). Join us for a taste! BOOK A MEETING
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Our thinking that there was a hunger for an operator that makes easy the provisioning and operating Kafka clusters on Kubernetes which is not based on Kubernetes stateful sets, proved to be correct as shortly after we released the first version our open-source Banzai Cloud Kafka Operator a community started to build around it. We received lots of valuable feedback that helps to shape the future of the Kafka operator and also feature contributions from the community.
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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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Update - Logging operator v2 Development doesn’t stop here; we’re constantly working to improve the logging-operator on the basis of feature requests made by our ops team and from recent customers. Here are some of those features: No limitations on label selectors Namespaced and Global resource scopes Visualised logging flows Secure output credential management Multi output log flows For more information Logs (one of the three pillars of observability besides metrics and traces) are an indispensable part of any distributed application.
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In one of our previous posts about creating Helm Charts for Kubernetes, we outlined what we consider the best practices for creating Helm charts. We’ve been using Helm in production and investing our time in creating Helm charts (available on the Banzai Cloud Charts GitHub repository) since Banzai Cloud’s inception. Creating Helm Charts is one thing; storing and serving them is another. We’d like to reduce the burden this places on the user, so today marks the launch of our Helm Chart repository service, which you can use to store and serve public Helm Charts for free.
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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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We will be at KubeCon + CloudNativeCon, Seattle this week experiencing the magic and catching up with people from the broader community. This will be a special event for us as we will be launching the first public beta of our Banzai Cloud Pipeline platform. Let us know if you’d like to chat! Get in touch thru Twitter or LinkedIn. See you there!
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Banzai Cloud announced today that it is collaborating with Oracle to bring its feature-rich application platform to Oracle Kubernetes Engine users. Banzai Cloud’s Pipeline deployment automation and execution engine enables developers to go from commit to scale in minutes by automating all the underlying tasks that provide convenient CI/CD flows, robust security, analytics and the ability to scale. The technology not only provides significant productivity gains to developers, but also increases operational efficiencies by aiding instance selection and automated introspection of large-scale workloads.
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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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