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During the development of our open source Pipeline PaaS, we introduced some handy features to help deal with deployments. We deploy most of our applications as Helm releases, so we needed a way to interact programatically (using gRPC) and to use a UI (RESTful API) with Helm. In order to do that with Pipeline, we introduced a very useful feature that manages Helm repositories and deploys applications with Helm to Kubernetes, using RESTful API calls.

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Cloud cost management series: Overspending in the cloud Managing spot instance clusters on Kubernetes with Hollowtrees Monitor AWS spot instance terminations Diversifying AWS auto-scaling groups Draining Kubernetes nodes Cluster recommender Cloud instance type and price information as a service Kubernetes was designed in such a way as to be fault tolerant of worker node failures. If a node goes missing because of a hardware problem, a cloud infrastructure problem, or if Kubernetes simply ceases to receive heartbeat messages from a node for any reason, the Kubernetes control plane is clever enough to handle it.

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At Banzai Cloud, we’re building a feature rich platform as a service on Kubernetes, called Pipeline. With Pipeline, we provision large, multi-tenant Kubernetes clusters on all major cloud providers, such as AWS, GCP, Azure and BYOC, and deploy all kinds of predefined or ad-hoc workloads to these clusters. When we needed a way for our users to login and interact with protected endpoints and, at the same time, provide dynamic secrets management support, while simultaneously providing native Kubernetes support for all our applications, we turned to Vault.

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The Pipeline platform contains a complete CI/CD component to support developers building, deploying and operating applications in an automated way, deployed to Kubernetes. Most of our documentation, blog posts and howtos have so far focused on Spark, Zeppelin and Tensorflow examples. However, we can actually build and deploy any application with Pipeline’s CI/CD component. This post showcases how to enable a simple Spring Boot application for the Banzai Cloud CI/CD flow, build and save the necessary artifacts, and deploy it to a Kubernetes cluster.

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For our Pipeline PaaS, monitoring is an essential part of operating distributed applications in production. We put a great deal of effort into monitoring large and federated clusters and automating these with Pipeline, so all our users receive out of the box monitoring for free. You can read about our monitoring series, below: 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

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For some time we’ve been evangelizing the idea that the runtime fabric of big data workloads should be Kubernetes. In this post I’d like to walk through the thought process behind that change and discuss its benefits. Obliviously, this is a pretty large topic, and this post has no intention of covering it completely - also, it reflects the views and opinions that we at Banzai Cloud believe in and push others to adopt.

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The adoption of serverless technologies is quickly progressing. According to this survey, it’s on par with the adoption of containers. And, even though ‘serverless’ is a very vague term (it can be argued that it’s rarely used in production, especially in complex applications), it seems set to be one of the most dominant trends in the near future in the cloud computing space. While, once, serverless referred specifically to early stage AWS Lambda, the category has matured rapidly.

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This is a copy of a guest post we published on the Hashicorp blog about how we use Vault with Kubernetes. At Banzai Cloud, we’re building a feature rich platform as a service on Kubernetes, called Pipeline. With Pipeline, we provision large, multi-tenant Kubernetes clusters on all major cloud providers, such as AWS, GCP, Azure and BYOC, and deploy all kinds of predefined or ad-hoc workloads to these clusters. We needed a way for our users to log in and interact with protected endpoints and, at the same time, provide dynamic secrets management support, while simultaneously providing native Kubernetes support for all our applications.

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Banzai Pipeline, or simply “Pipeline” is a tabletop reef break located in Hawaii, on Oahu’s North Shore. It is the most famous and infamous reef on the planet, and serves as the benchmark by which all other surf breaks are measured. Pipeline is a PaaS with a built in CI/CD engine to deploy cloud native microservices to a public cloud or on-premise. It simplifies and abstracts all the details of provisioning cloud infrastructure, installing or reusing a Kubernetes cluster, and deploying an application.

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As part of the Debug 101 series, we’re back hunting a small but annoying bug. This kind of bug is not really a bug, but a side effect of several tools working together. Here comes trouble I deploy a development version of Pipeline on a Kubernetes cluster running on top of AWS infrastructure. For this deployment I use the following Helm chart command. $: helm install --name pipeline banzaicloud-stable/pipeline-cp \ --set=drone.

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