Kubernetes Ingress is an API object that allows you manage external (or) internal HTTP[s] access to Kubernetes services running in a cluster. Amazon Elastic Load Balancing Application Load Balancer (ALB) is a popular AWS service that load balances incoming traffic at the application layer across multiple targets, such as Amazon EC2 instances, in a region. ALB supports multiple features including host or path based routing, TLS (Transport layer security) termination, WebSockets, HTTP/2, AWS WAF (web application firewall) integration, integrated access logs, and health checks.

The AWS ALB Ingress controller is a Kubernetes SIG-AWS subproject - it was the second sub-project added to SIG-AWS after the aws-authenticator subproject. The ALB Ingress controller triggers the creation of an ALB and the necessary supporting AWS resources whenever a Kubernetes user declares an Ingress resource on the cluster. TargetGroups are created for each backend specified in the Ingress resource. Listeners are created for every port specified as Ingress resource annotation. When no port is specified, sensible defaults (80 or 443) are used. Rules are created for each path specified in your ingress resource. This ensures that traffic to a specific path is routed to the correct TargetGroup.

In this post, we will work through a simple example of running ALB based Kubernetes Ingresses with Pulumi EKS, AWS, and AWSX packages.

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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.

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Static websites are back in the mainstream these days. Website generators like Jekyll, Hugo, or Gatsby, make it fairly easy to combine templates and markdown pages to produce static HTML files. Static assets are the simplest thing to serve and cache, so the whole setup ends up being fast and cost-efficient.

Many platforms offer services to host such static websites. This post explains the steps to create the infrastructure to do so on Microsoft Azure.

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Amazon Elastic Container Registry (ECR) is a fully-managed Docker container registry that makes it easy for developers to store, manage, and deploy Docker container images. ECR is integrated with Amazon Elastic Container Service (ECS), including for Kubernetes (EKS), simplifying your development to production workflow, securing access through IAM, and eliminating the need to operate your own container repositories or worry about scaling the underlying infrastructure. ECR hosts your images in a highly available and scalable architecture, allowing you to reliably deploy containers for your applications. In this article, we’ll see how Pulumi Crosswalk for AWS lets you use infrastructure as code to easily build, publish, and pull from private ECR repositories.

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One year ago today – on June 18, 2018 – we open sourced Pulumi, a new approach to multi-cloud infrastructure as code using your favorite languages. And what a year it has been!

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Pulumi Crosswalk for AWS modules can be used to get first class insights and visualizations directly inside your Pulumi application.

As cloud applications tend to be long-lived, we think it’s vital that it be possible to get regular insights on the performance of the application at all times. Using Crosswalk for AWS Pulumi applications allow you to easily define and visualize the appropriate metrics that show the health of your services, create alarms to let you know when something is wrong, and easily create dashboards to get live visualization of what is happening in the cloud. Because this is vital to the health of the application, we think this should be something built in from the start, and not something added after the fact as an out of band artifact.

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Guest Author: Chris Toomey, Solution Architect Lead @ Mapbox

With 8 billion+ connected IoT devices and 2 billion GPS-equipped smartphones already online, logistics businesses are tracking assets at every step in the supply chain. At this scale and complexity, it is imperative to have a flexible way to ingest, process, and act upon this data, without sacrificing security or best practices.

To meet this need, Mapbox has created an Asset Tracking Solution that uses Pulumi’s open source JavaScript libraries (AWS, AWSX) available with multi-language support with Pulumi Crosswalk for AWS. Pulumi Crosswalk for AWS is an open source framework that streamlines creation, deployment and management of AWS services with built-in AWS Best Practices and minimal lines of code in common programming languages.

In this blog, we will show snippets of the Javascript code that embraces the power of Pulumi to program AWS service APIs to create the Mapbox solution. To see the full architecture in action with a live bike race across America, please refer to this webinar recorded on June 13th 2019 and the Mapbox asset tracking solution. Also refer to this blog of the Race across America showcased live during the webinar tomorrow.

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Amazon Web Services provides an incredible platform for developers to build cloud-native applications, and is used by millions of customers of all sizes. The building block services that AWS offers enable teams to offload undifferentiated heavy-lifting to AWS. To maximally benefit from these services though, cloud engineering teams must learn how to compose all of these building blocks together to build and deliver their own applications. Today, this is still too hard. Getting from your laptop to a production-ready AWS deployment frequently takes days or weeks instead of minutes or hours. And AWS building block services frequently leave you to re-implement (and re-discover) best-practices instead of providing these as smart defaults.

Pulumi Crosswalk for AWS is a new open source library of infrastructure-as-code components that make it easier to get from zero to production on AWS, easier to adopt AWS best practices by default, and easier to evolve your AWS infrastructure as your application needs mature.

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While some people coming to Pulumi are entirely new to Infrastructure as Code, increasingly teams are moving from other tools - whether cloud-specific in the form of CloudFormation or ARM Templates, or cloud-agnostic tools such as Terraform. In these organizations, new infrastructure provisioned with Pulumi must co-exist with existing resources provisioned with other tools, and often by different teams. For example, it’s common to see an application team deploying into a VPC owned and managed by a network operations team.

Pulumi supports this kind of workflow natively using the StackReference type from the Pulumi SDK. Integration with the most popular cloud-specific tools have been supported by Pulumi since the earliest days:

• The aws.cloudformation.getStack() function can be used to obtain the outputs from a CloudFormation Stack.

• The get method of the azure.core.SubscriptionTemplateDeployment class can be used to obtain the outputs of an ARM Template Deployment.

We recently added similar support for reading the outputs of a Terraform state file - both from local .tfstate files, and from all of the remote state backends supported by Terraform. This is exposed via the terraform.state.RemoteStateReference type in the @pulumi/terraform NPM package.

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