Mikhail Shilkov

Today, Microsoft announced a new general-purpose serverless container platform: Azure Container Apps. Container Apps is a fully managed platform for microservice applications that runs on top of Kubernetes and open-source technologies like KEDA, Envoy, and Dapr.

Container Apps are designed to abstract infrastructure management with flexible serverless containers. Developers can run containers at scale without the burden of standing up and managing a Kubernetes cluster manually.

We are happy to announce same-day support for Azure Container Apps in the Pulumi Azure Native Provider, which covers 100% of the Azure Resource Manager APIs and gives you highest fidelity integration with Azure’s resources.

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Last September, we announced the beta release of Pulumi Azure NextGen: a new Microsoft Azure provider for Pulumi that combines same-day access to the entire Azure API surface with the excellent Pulumi experience you know and love, including version-less resources, auto-naming, and auto-location.

Today, we’re excited to announce that this new provider is now the default way to manage Azure resources with Pulumi. We’re also excited to announce its final name: the native Azure provider for Pulumi, or “Azure-Native” for short. You can get started with the new provider using our newly-updated getting started guide.

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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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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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We are excited to announce the beta release of a next generation Microsoft Azure provider for Pulumi. Azure has been a rapidly growing cloud platform among Pulumi users over the last year, and with the next generation Azure provider, we are doubling down on providing the best support possible for the Azure platform in Pulumi. We designed the new provider to expose the entire API surface of Azure to developers and operators, now and forever.

The new Azure provider for Pulumi (azure-nextgen) works directly with the Azure Resource Manager (ARM) platform instead of depending on a handwritten layer as with the previous provider. This approach ensures higher quality and higher fidelity with the Azure platform.

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Because Pulumi uses general-purpose programming languages to provision cloud resources, you can take advantage of native tools and perform automated tests of your infrastructure. The full power of each language is available, including access to libraries and frameworks for testing.

This blog post takes a deeper dive into mock-based unit testing of Pulumi programs written in C# and F#.

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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.

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Google Cloud Run is the latest addition to the serverless compute family. While it may look similar to existing services of public cloud, the feature set makes Cloud Run unique:

• Docker as a deployment package enables using any language, runtime, framework, or library that can respond to an HTTP request.
• Automatic scaling, including scale to zero, means you pay for what you consume with no fixed cost and no management overhead.
• HTTP load-balancing out of the box simplifies the usage.

Cloud Run is targeted very specifically at stateless web applications. It uses ephemeral containers, and each execution is limited to 15 minutes.

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AWS Lambda cold starts (the time it takes for AWS to assign a worker to a request) are a major frustration point of many serverless programmers. In this article, we will take a look at the problem of latency-critical serverless applications, and how Provisioned Concurrency impacts the status-quo.

1. Concurrency Model of AWS Lambda
2. Cold Starts
3. Warming
4. Provisioned Concurrency
5. Dynamic Provisioned Concurrency
6. Pricing
7. Conclusion

Concurrency Model of AWS Lambda

Despite being serverless, AWS Lambda uses lightweight containers to process incoming requests. Every container, or worker, can process only a single request at any given time.

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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!

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