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If you’re a Node.js developer, it’s very likely that you are building microservices and have already come across Kubernetes. Kubernetes is a solid foundation for scalable container deployments but is also infamous for it’s steep learning curve. This post will explain how to containerize your Node.js applications and what it will take to make them production-ready on Kubernetes. While this post can be used as a tutorial for DIY jobs, we have also automated this entire process, allowing developers to kickstart their code to production experience with a few clicks.

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At Banzai Cloud we are building an application-centric platform for containers - Pipeline - running on Kubernetes to allow developers to go from commit to scale in minutes. We support multiple development languages and frameworks to build applications, with one common goal: all Pipeline deployments receive integrated CI/CD, centralized logging, monitoring, enterprise-grade security, autoscaling, and spot price support automatically, out of the box. In most cases we accomplish this in a non-intrusive way (i.

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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 on Kubernetes. Most of our documentation, blog posts and how-tos have focused on Spark, Zeppelin and Tensorflow examples. However, it is possible to build and deploy any application with Pipeline’s CI/CD component. Our last post about the Banzai Cloud CI/CD flow described how to build/deploy a Spring Boot application on Kuberbetes.

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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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