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Banzai Cloud is happy to announce that it is an Amazon EKS Platform Partner. Last year Amazon joined CNCF and announced plans to create their own Kubernetes service - Amazon EKS. The service has been launched this June, with the objective to make it easy for you to run Kubernetes on AWS without needing to install, operate, and maintain your own Kubernetes control plane. At Banzai Cloud we are committed to provide support for Kubernetes on all major cloud providers for our users thus one of our priority was to enable Amazon EKS in Pipeline and take the DevOps and user experience to the next level by turning EKS into a feature rich enterprise-grade application platform.
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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. For an enterprise-grade application platform security is a must and it has many building blocks. Please read through the Security series on our blog to learn how we deal with a variety of security-related issues. Security series: Authentication and authorization of Pipeline users with OAuth2 and Vault Dynamic credentials with Vault using Kubernetes Service Accounts Dynamic SSH with Vault and Pipeline Secure Kubernetes Deployments with Vault and Pipeline Policy enforcement on K8s with Pipeline The Vault swiss-army knife The Banzai Cloud Vault Operator Vault unseal flow with KMS
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At Banzai Cloud we provision all kinds of applications to Kubernetes and we try to autoscale these clusters with Pipeline and/or properly size application resources as needed. As promised in an earlier blog post, How to correctly size containers for Java 10 applications, we’ll share our findings on the Vertical Pod Autoscaler(VPA) used with Java 10. VPA sets resource requests on pod containers automatically, based on historical usage, thus ensuring that pods are scheduled onto nodes where appropriate resource amounts are available for each pod.
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At Banzai Cloud we place a lot of emphasis on the observability of applications deployed to the Pipeline Platform, which we built on top of Kubernetes. To this end, one of the key components we use is Prometheus. Just to recap, Prometheus is: an open source systems monitoring and alerting tool a powerful query language (PromQL) a pull based metrics gathering system a simple text format for metrics exposition Problem statement Usually, legacy applications are not exactly prepared for these last two, so we need a solution that bridges the gap between systems that do not speak the Prometheus metrics format: enter exporters.
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At Banzai Cloud we run and deploy containerized applications to our PaaS, Pipeline. Java or JVM-based workloads, are among the notable workloads deployed to Pipeline, so getting them right is pretty important for us and our users. Java/JVM based workloads on Kubernetes with Pipeline Why my Java application is OOMKilled Deploying Java Enterprise Edition applications to Kubernetes A complete guide to Kubernetes Operator SDK Spark, Zeppelin, Kafka on Kubernetes
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At Banzai Cloud we’re always looking for new and innovative technologies that support our users in their transition to microservices deployed on Kubernetes, using Pipeline. In recent months we’ve partnered with CoreOS and RedHat to work on Kubernetes operators. That project was opensourced today, and is now available on GitHub. If you read through the rest of this blog, you’ll learn what an operator is, and how to use the operator sdk to develop an operator through a concrete example we developed, here, at Banzai Cloud.
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Apache Spark on Kubernetes series: Introduction to Spark on Kubernetes Scaling Spark made simple on Kubernetes The anatomy of Spark applications on Kubernetes Monitoring Apache Spark with Prometheus Spark History Server on Kubernetes Spark scheduling on Kubernetes demystified Spark Streaming Checkpointing on Kubernetes Deep dive into monitoring Spark and Zeppelin with Prometheus Spark Streaming Checkpointing on Kubernetes Deep dive into monitoring Spark and Zeppelin with Prometheus Apache Spark application resilience on Kubernetes Collecting Spark History Server event logs in the cloud
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At Banzai Cloud we’re always looking for products or frameworks that add value to our business, which we can enable in our open source PaaS, Pipeline. Any list of such products would include serverless frameworks. Thus, today we’re adding Fn as a supported spotguide, making it easy for users to deploy Fn with Pipeline on their chosen cloud provider. Before we dive into how to deploy and use Fn with Pipeline, here are a few reasons why we thought Fn should be supported by Pipeline:
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At Banzai Cloud we provision different frameworks and tools like Spark, Zeppelin, Kafka, Tensorflow, etc to our Pipeline PaaS (built on Kubernetes). Last week we added serverless capabilities to Pipeline, using OpenFaas. This blog post explains how to deploy OpenFaaS to Kubernetes using Pipeline and invoke an example function. We’ll distinguish between the provisioning of the serverless frameworks we support (this post is about OpenFaaS but Pipeline also supports Kubeless), from the invocation of functions through the Pipeline API once it’s dispatched to any of the serverless frameworks we deploy to Kubernetes.
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Apache Spark on Kubernetes series: Introduction to Spark on Kubernetes Scaling Spark made simple on Kubernetes The anatomy of Spark applications on Kubernetes Monitoring Apache Spark with Prometheus Spark History Server on Kubernetes Spark scheduling on Kubernetes demystified Spark Streaming Checkpointing on Kubernetes Deep dive into monitoring Spark and Zeppelin with Prometheus Apache Spark application resilience on Kubernetes Apache Zeppelin on Kubernetes series: Running Zeppelin Spark notebooks on Kubernetes Running Zeppelin Spark notebooks on Kubernetes - deep dive
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