Going from a containerized application to a service running in the cloud requires a few steps beyond an application’s normal build-and-test cycle. Namely, it means building and publishing a container image in a registry and then consuming that image from your target environment, whether that’s Kubernetes, Amazon ECS, or another container orchestrator. It’s not enough to just write a Dockerfile — you will need to pick a container registry, decide whether that registry should be public or private, authenticate against it, and ideally automate deploying subsequent updates. Infrastructure as code to the rescue! In this article, we’ll see how to build, publish, and consume a simple container image across any cloud, using just a few lines of code.

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Amazon announced several Elastic Kubernetes Service feature releases and updates during the first week of AWS re:Invent 2020. If we look at all the announcements as a whole, we can see the Kubernetes ecosystem maturing to make deployments and management easier for organizations. Let’s take a look at how they can benefit your use of EKS.

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Azure Synapse is an integrated analytics service that combines enterprise data warehousing of Azure SQL Data Warehouse and Big Data analytics of Apache Spark. Azure Synapse is a managed service well integrated with other Azure services for data ingestion and business analytics.

You could use the Azure portal to get started with Azure Synapse, but it can be hard to define sophisticated infrastructure for your analytics pipeline using the portal alone, and many users need to apply version control to their cloud configurations.

The alternative is to use an infrastructure as code tool to automate building and deploying cloud resources. This article demonstrates how to provision an Azure Synapse workspace using Pulumi and general-purpose programming languages like Python and C#.

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As Kubernetes grows in popularity, the number of options for Kubernetes users continues to increase. Providers of managed Kubernetes offerings will often learn lessons about operating large numbers of clusters at scale; it’s increasingly common that they will contribute this knowledge back to the ecosystem, allowing those organizations who need more control and flexibility to reap the benefits.

With the announcement of the Amazon EKS Distro during AWS re:Invent, the Amazon EKS team has contributed back to the cloud-native community in a big way. In this post, we’ll take a brief look at what the Amazon EKS Distro is, explore why you might choose this over current managed service offerings and finally, explore how you can get started with the Amazon EKS Distro on day 1 using Pulumi.

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At re:Invent, the AWS team unveiled the new Amazon Elastic Container Registry Public (Amazon ECR Public), creating a new option for users in publishing and pulling public container images. Pulumi fully supports Amazon ECR Public in two ways:

1. Official Pulumi container images are available today on Amazon ECR Public.
2. Pulumi is the easiest way to package and publish your container images, and we’ll support publishing your container images to Amazon ECR Public very soon.

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When AWS Lambda launched in 2014, it pioneered the concept of Function-as-a-Service. Developers could write a function in one of the supported programming languages, upload it to AWS, and Lambda executes the function on every invocation.

Ever since then, a zip archive of application code or binaries has been the only supported deployment option. Even AWS Lambda Layers—reusable components automatically merged into the application code—used the zip packaging format.

Today, AWS announced that AWS Lambda now supports packaging serverless functions as container images. This means that you can deploy a custom Docker or OCI image as an AWS Lambda function.

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Most infrastructure projects require working with existing cloud resources, either by building on top of existing resources or adopting existing resources under management with a new and more robust infrastructure provisioning solution.

In June 2019, Pulumi introduced the ability to import existing infrastructure resources to be under Pulumi management no matter how you’ve provisioned these resources — manually in your cloud provider’s console or CLI, using an infrastructure as code tool like Terraform or AWS CloudFormation. Today, we are happy to announce a richer resource import experience.

As of v2.12.0, Pulumi has introduced a pulumi import command. This command will import the cloud resource into the Pulumi state and generate the code for the user’s Pulumi program in the appropriate language.

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There are many moving parts when deploying infrastructure and applications. Playbooks are step-by-step maps that standardize how infrastructure and applications are deployed across your organization. Typically playbooks describe every action to build and deploy, requiring an operator to complete each step before moving on to the next. It’s a process that can be tedious and prone to human error.

What if you could encapsulate a playbook into a single action? This is the promise of declarative infrastructure. You declare the desired state of your infrastructure and the infrastructure as code engine builds the infrastructure. However, you must still deploy the application and perform maintenance, and this is where you hit the limits of templating languages and where programming languages excel. In this hands-on article, we’ll demonstrate how to use Pulumi’s Automation API to create a program that builds infrastructure, installs an application, and can perform application maintenance.

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If you could create infrastructure without using a cloud provider’s console, a CLI, or a templating engine, what would you build? Pulumi’s Automation API lets you create declarative infrastructure defined by your best practices and expose it behind a REST, gRPC, or custom API.

So just what is Automation API? Think of it as Pulumi’s infrastructure as code engine as an SDK. Instead of writing code and using the CLI to declare infrastructure, you can directly tell the engine to build your infrastructure. This means that you’re using the same declarative IaC tooling with the predictability, robustness, safety, and desired state management, except it has a new programmatic surface area. Imagine building an application that creates infrastructure via a REST interface. Get ready, because that’s what we’re going to do.

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This article demonstrates how to deploy and provision a virtual machine in Azure using the Pulumi Azure-Native provider. While there are numerous examples of using the Azure console, the Azure CLI, or ARM templates to deploy and provision virtual machines, we’ll use Python to implement a repeatable deployment.

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