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While we build our open source, cloud agnostic Heroku/Cloud Foundry-like Paas - Pipeline - on top of Kubernetes, we continue to launch lots of clusters on different cloud providers. Most of these clusters are launched on spot or preemptible instances, and managed by Hollowtrees. However, there are many smaller development clusters, control planes, instances and PoCs we launch that are marginally related to, or launched with, Pipeline. Naturally, these have an associated cost that we want to keep tight control over.
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In the past few weeks we’ve been blogging about the advanced, enterprise-grade security features we are building into our open source PaaS, Pipeline. If you’d like to review these features, please read this series: 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 Kubernetes secret management with Pipeline Container vulnerability scans with Pipeline Kubernetes API proxy with Pipeline
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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 Apache Spark CI/CD workflow howto 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
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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 Apache Spark CI/CD workflow howto 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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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 As we eluded to in the last post in this series, we’ll be continuing our discussion of centralized and secure Kubernetes logging/log collection.
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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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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 For our Pipeline PaaS, monitoring is an essential part of operating distributed applications in production.
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