We are happy to announce the release of a new major version of the Pulumi Azure provider. Pulumi Azure 2.0 is based on the 2.0 release of the upstream provider and brings several improvements and breaking changes.
Here at Pulumi, everyone on our engineering team is a Gopher. Go has quickly become the “language of the cloud,” and so when we chose to build our open-source pulumi/pulumi engine and SaaS backend, we chose Go. As such, we are very excited to welcome Go to the family of supported infrastructure as code languages as part of Pulumi 2.0.
Go has become the lingua franca of cloud-native infrastructure development. From projects like Docker, to Kubernetes, the community has embraced Go. The result is an ecosystem rich in tooling and packages designed for systems and cloud engineering.
In an earlier article, we introduced examples of Policy as Code to prevent two of the most common causes of data breaches. Policies are the guardrails of infrastructure. They control access, set limits, and manage how infrastructure operates. In many systems, policies are created by clicking on a GUI, making it difficult to replicate or version. Pulumi implements policy by writing it in Typescript, which ensures that you can write policies using software development practices such as automated testing, deployment, and version control.
This post describes an early version of Crosswalk for Kubernetes. Some of the links, examples, and implementation details may have changed. For the most up-to-date information, see the Pulumi Kubernetes documentation.
Running Kubernetes in production can be challenging. This past year, Pulumi has collected common patterns of usage informed by best practices for provisioning Kubernetes infrastructure and running containerized applications. We call this Pulumi Crosswalk for Kubernetes: a collection of playbooks and libraries to help you to successfully configure, deploy, and manage Kubernetes in a way that works for teams in production.
Azure Functions is a managed service for serverless applications in the Azure cloud. More broadly, Azure Functions is a runtime with multiple hosting possibilities. KEDA (Kubernetes-based Event-Driven Autoscaling) is an emerging option to host this runtime in Kubernetes.
In the first part of this post, I compare KEDA with cloud-based scaling and outline the required components. In the second part, I define infrastructure as code to deploy a sample KEDA application to an Azure Kubernetes Service (AKS) cluster.
The result is a fully working example and a high-level idea of how it works. Kubernetes expertise is not required!
In a previous blog post, I shared how easy it is to create a globally distributed, highly-available, low-latency application with Azure Functions, Azure Cosmos DB, and Pulumi.
Today, I want to show how the same approach can be generalized for any cloud compute service, including containers, virtual machines, and serverless functions.
In this post, we’ll take a look at 10 “pearls”—bite-sized code snippets—that demonstrate using Pulumi to build serverless applications with Azure Functions and infrastructure as code. These pearls are organized into four categories, each demonstrating a unique scenario:
Kubernetes clusters from the managed platforms of AWS Elastic Kubernetes Service (EKS), Azure Kubernetes Service (AKS), and GCP Google Kubernetes Engine (GKE) all vary in configuration, management, and resource properties. This variance creates unnecessary complexity in cluster provisioning and application deployments, as well as for CI/CD and testing.
Additionally, if you wanted to deploy the same app across multiple clusters for specific use cases or test scenarios across providers, subtleties such as LoadBalancer outputs and cluster connection settings can be a nuisance to manage.
In this post, we’ll see how to use Pulumi to deploy the kuard app across EKS,
AKS, GKE and a local Kubernetes cluster, such as Docker Desktop or a self-managed cluster.
We’ll spin up the clusters in each provider, launch the app,
and manage both cluster and app using the TypeScript programming language.
Every non-trivial application relies on configuration values that may depend on the current execution environment. Some of these values contain sensitive information that shouldn’t be shared publicly. In general, the fewer parties that have access to those secret values, the safer the application will be—in fact, in an ideal world, no one would be granted direct access to those secrets.
Pulumi is excellent at connecting multiple cloud components into a cohesive application. In my previous post, I introduced the way to mix JavaScript or TypeScript serverless functions directly into the cloud infrastructure programs.
Today, I will build a serverless application with both the data store and the HTTP endpoint located close to end users to ensure prompt response time. The entire application runs on top of managed Azure services and is defined as a single Pulumi program in TypeScript.