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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Whether it’s an IoT installation, a website, or a mobile app, modern software systems generate a trove of usage and performance data. While it can be daunting to collect and manage, surfacing data empowers the business to make informed product investments. In this article, we’ll explore the following:

1. An overview of the traditional Redshift analytics stack on AWS, the use cases it excels at, and where it falls apart.
2. An alternative architecture utilizing serverless and streaming.
3. How to implement this architecture as code in a reusable library with Pulumi.
4. How to automate the development loop when writing Pulumi libraries.

If you’d like to follow along, you can clone and run the reference implementation. If you’re new to Pulumi, you can follow this guide to get started.

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Pulumi has many resource providers that allow you to interact with your favorite cloud or resource. There are times when a provider may not deliver on the specific task that you want to accomplish. Dynamic Providers can be a powerful tool to help accomplish your infrastructure tasks.

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The Amazon Web Services (AWS) Cloud ecosystem is large and vibrant, so vast and vibrant that at times, it can be challenging to know where best to start! In the case of containers, Abby Fuller tweeted a descriptive summary about using AWS container services.

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As we celebrate another incredible year in the books here at Pulumi, I wanted to share some reflections about our most exciting milestones over the past twelve months. The best part has been connecting with more customers worldwide, as we saw more than a 15x growth in our customer base, surely a sign of big things to come in 2020. We couldn’t have done it without our amazing community; thank you deeply for your continued support and passion around Pulumi’s bold mission to empower every engineer to program the cloud — you make it all worthwhile.

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We recently announced a new policy as code solution, CrossGuard that validates policies at deployment time. Policies are expressed as code and are used to prevent the creation of out-of-compliance resources. This allows an organization to prevent entire classes of security and reliability defects to ensure infrastructure is following best practices. Because policies are written using full-blown programming languages, it’s possible to do interesting things such as combining IAM Access Analyzer and Pulumi CrossGuard. In this post, we’ll take a closer look at the different types of policies that can be written.

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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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Last month, we announced .NET support for Pulumi, including support for AWS, Azure, GCP, and many other clouds. One of the biggest questions we heard was about Kubernetes — “can I use Pulumi to manage Kubernetes infrastructure in C#, F#, and VB.NET as I can already in TypeScript and Python today?” With last week’s release of Pulumi.Kubernetes on NuGet, you can now also deploy Kubernetes infrastructure using your favorite .NET languages.

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AWS Elastic Kubernetes Service (EKS) provides a range of performance and control for dynamically scaling your Kubernetes clusters, including Managed Node Groups, Fargate, and Manually-Managed Node Groups in EC2. In this post, we’ll see how to use each of these compute options, and when to prefer one over the other in order to maximize productivity, flexibility, and control, based on your needs.

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Yesterday AWS announced an exciting new feature — the AWS Identity and Access Management (IAM) Access Analyzer — a service powered by automated reasoning that detects potentially-insecure access to your AWS resources, including S3 Buckets, SQS Queues, Lambdas, and more. At the same time, Pulumi announced a new policy as code solution, CrossGuard, that validates policies at deployment time. The question is: Can IAM Access Analyzer and Pulumi CrossGuard be combined to get the best of both solutions? The answer is Yes!

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