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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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On this blog we’ve already discussed our totally redesigned logging operator, which automates logging pipelines on Kubernetes. Thanks to the tremendous amount of feedback and the numerous contributions we received from our community, we’ve been able to rethink and redesign that operator from scratch, but the improvements aren’t going to stop coming any time soon. Our goal is to continue removing the burden from human operators, and to help them manage the complex architectures of Kubernetes.
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Service interruptions caused by outages have severe consequences that range from harming a provider’s reputation to frustrating a customer to the point that they move to a competitor, so it’s extremely important to build and run resilient systems. Resiliency must be implemented at multiple levels from the bottom infrastructure layer to the application. For our customers, it’s a necessity that the EKS clusters they deploy their production applications onto be highly available.
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In production systems mostly when working in microservices architecture, it is soon realized that monitoring each service individually is often not enough to troubleshoot complex issues. It becomes a necessity to have a full picture of the whole call stack through the entire application with detailed information on service topologies, network latencies and individual request durations. This is usually where distributed tracing comes to the rescue. In this post, the concept of distributed tracing will be introduced in microservices architecture.
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One of the key features of Pipeline, our hybrid cloud container management platform, is its ability to provision Kubernetes clusters across five different cloud providers (Alibaba, Azure, Amazon, Google, Oracle), private datacenters (vmWare, baremetal, etc), or any combination thereof. It does this by using either cloud provider-managed Kubernetes, or our own CNCF certified Kubernetes distribution - PKE. Each cloud provider’s internal LB is different, and so is the way each is integrated with Kubernetes.
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About a year ago we published the first release of our popular logging-operator. The initial version of that operator was designed to fit Pipeline, the Banzai Cloud hybrid cloud container management platform. However, since then, all kinds of people have found it to be an extremely useful tool that helps them manage their logs on Kubernetes. Initially, Fluent ecosystem automation was enough to support the disparate needs of our userbase, but, as the popularity of the logging-operator grew, different setups were put in place by our community that revealed some of its limitations.
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Since releasing our open-source Istio operator, we’ve been doing our best to add support for the latest versions of Istio as rapidly as possible. Today, we’re happy to announce that we have added Istio 1.3 support for the Banzai Cloud Istio operator. In this post, we’ll be outlining how to easily upgrade Istio control planes to 1.3 with the Banzai Cloud Istio operator, within a single-mesh multi-cluster topology or across a multi-cloud or hybrid-cloud service mesh.
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Banzai Cloud’s Pipeline platform is an operating system that allows enterprises to develop, deploy and scale container-based applications. It leverages best-of-breed cloud components, such as Kubernetes, to create a highly productive, yet flexible environment for developers and operations teams alike. Strong security - multiple authentication backends, fine grained authorization, Vault-based dynamic secret management, automated secure communications between components using TLS, vulnerability scans, static code analysis, pod and network policies etc.
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Istio has been rightfully praised for ushering in free observability and secure service to service communication. Other, more significant features, however, are what truly make Istio the Swiss army knife of service mesh operators; when it comes to meeting SLOs like uptime, latency and error rates, the ability to manage traffic between services is absolutely critical. When we released the Istio operator earlier this year, our goal (besides managing Istio installation and upgrades) was to provide support for these excellent traffic routing features, while making everything more usable and UX friendly.
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NOTE: This is an updated version of a blog post we wrote nearly a year ago. It’s been extremely popular, however, due to the improvements and new features we’ve added to Bank-Vaults, it’s become outdated and in needs of a fresh coat of paint. A key part of the Banzai Cloud Pipeline platform, has always been our strong focus on security. We incorporated Vault into our architecture early on in the design process, and we have developed a number of support components to be easily used with Kubernetes.
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