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At Banzai Cloud we develop a container management platform that provisions and orchestrates hundreds of Kubernetes clusters across six different cloud providers or hybrid clouds every day. Pipeline makes it easy for users to run applications on Kubernetes, but under the hood there is an insanely complex system, so it’s mission critical for us to have realtime information about what’s happening inside the platform and have access to historical data to investigate incidents our customers may have had.
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This is the second part of a very popular post, Helm from basics to advanced. In the previous post (we highly suggest you read it, if you haven’t done so already) we covered Helm’s basics, and finished with an examination of design principles. In this post, we’d like to continue our discussion of Helm by exploring best practices and taking a look at some common mistakes. If you are looking for a place to securely store your Helm charts, remember that Banzai Cloud runs a free Helm Charts repository as a service: charts.
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The Cloudinfo project has been a core part of the Pipeline platform since day one. We built and open sourced Cloudinfo as a unified interface that would allow us to access prices and services from all the cloud providers we support (AWS, Azure, Google, Alibaba and Oracle), resulting in a multi- and hybrid-cloud application platform. When a Pipeline user creates a single, hybrid- or multi-cloud Kubernetes cluster in one of the cloud providers or on-premise, they have the option of choosing between specifying their resource requirements (number of CPUs, memory, advanced network, I/O, etc) or simply selecting the cheapest cloud provider.
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In our last post about using Cadence workflows to spin up Kubernetes we outlined the basic concept of Cadence and walked you through how to use the Cadence workflow engine. Let’s dive into the experiences and best practices associated with implementing complex workflows in Go. We will use the deployment of our PKE Kubernetes distribution, from Pipeline to AWS EC2 as an example. Of course, you can deploy PKE independently, but Pipeline takes care of your cluster’s entire life-cycle , starting from nodepool and instance type recommendations, through infrastructure deployment, certificate management, opt-in deployment and configuration of our powerful monitoring, logging, service mesh, security scan, and backup/restore solutions, to the scaling or termination of your cluster.
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One of the main goals of the Banzai Cloud Pipeline platform and PKE Kubernetes distribution is to radically simplify the whole Kubernetes experience and execute complex operations on behalf of the users. These operations communicate with a number of different remote services (from cloud providers to on-prem virtualization or storage providers) where we have little or no way to influence the result of these calls: how long will it take, will it ever succeed, whether it provides the desired result and so.
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As a tech startup our main focus is on delivering value to our users and customers which means that the Developer Experience often comes second. Since we launched our beta platform Pipeline, we received an enormous amount of attention not just from users and customers, but from developers as well. Based on your feedback we’ve started to formalize our development processes. This post contains instructions for setting up a basic development environment and explains our development workflow through a simple pull request as an example.
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Spotguides are one of the most useful features of the Banzai Cloud Pipeline platform. Spotguides are managed application environments, which provide an easy and scalable way of deploying applications, and programmatically take care of all the necessary “plumbing” that is critical for production (if you missed our introductory series on Spotguides, you can catch up here). A Spotguide defines a template for provisioning cloud resources (Kubernetes clusters, object stores, logging, monitoring, security, etc.
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The following is a guest blog post from Robbie Blaine, Site Reliability Engineer at EOH Big Data Lab. Contributions from the community are a key factor in driving our products forward. BIG thanks to all of you who have engaged with us by raising issues, giving feedback, or creating pull requests. Keep them coming, we love them! Automating Vault Deployment and Configuration on OKD with Bank-Vaults Hashicorp Vault is an Encryption-as-a-Service tool that is used to securely store and access secrets.
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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 - the operating system for your clouds. The platform itself consists of many building blocks - 30+ components - but they share one commonality: they are all developed in Golang. Dependency Injection is a hot topic in the Go community these days, mainly because of Google’s recently released dependency injection container: Wire.
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Satellite is a Golang library and RESTful API that determines a host’s cloud provider with a simple HTTP call. Behind the scenes, it uses file systems and provider metadata to properly identify cloud providers. When we started to work on Pipeline and the Banzai Cloud Pipeline Platform Operators, we soon realized how frequently we would need to find out which cloud provider the service was actually running on. Note that Pipeline supports 6 different cloud providers
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