Today we are pleased to announce the Preview of .NET support for custom resource providers. This means you can build custom providers using your favorite .NET language, including C#, F#, and VB.NET.

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After launching Pulumi Deployments a few months ago and receiving a ton of community feedback (thank you!) today we are launching an integration with OpenID Connect (OIDC) to enable temporary credentials and granular access controls. We would like to thank GitHub user eriklz for creating the original request for this functionality.

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In our last episode, Deploying a Data Warehouse with Pulumi and Amazon Redshift, we covered using Pulumi to load unstructured data from Amazon S3 into an Amazon Redshift cluster. That went well, but you may recall that at the end of that post, we were left with a few unanswered questions:

• How do we avoid importing and processing the same data twice?
• How can we transform the data during the ingestion process?
• What are our options for loading data automatically — for example, on a regular schedule?

These are the kinds of questions you’ll almost always have when setting up a data-processing (or ETL) pipeline — and every platform tends to answer them a little differently.

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Last month, we released our first set of architecture templates — configurable Pulumi projects designed to make it easy to bootstrap new stacks for common cloud architectures like static websites, containers, virtual machines, and Kubernetes clusters. Architecture templates are a great way to get a new project up and running quickly, and they’ve already grown quite popular with our users, several of whom have asked if whether it’s possible to create templates of their own.

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Exactly 3 years ago we added support in the Pulumi Service to transfer stacks from an Individual account to a Pulumi organization and between Pulumi organizations. We heard from customers that they love this feature but found it both hard to discover and tedious when moving a large workload from one organization to another and from Individual accounts to organizations. We are excited to announce bulk stack transfer to address this feedback and a new organization set up wizard to improve discovery of the feature.

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When building with Kubernetes for the first time, we often need to stand up a lot of infrastructure just to get to the point of having a base to build an application. Let’s explore how we can wire together two of our architecture templates to generate a base for a web application running on Kubernetes on Google Cloud with Python and Poetry.

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It’s fun to think about how much data there is swirling around in the global datasphere these days. However you choose to measure it (and there are various ways), it’s a quantity so massive — hundreds of zettabytes, by some estimates — that it’s kind of a hard thing to quite get your head around.

If you could convert all the world’s data into droplets of water, for instance, at one megabyte per drop, you’d have enough 1MB drops to fill two more Lake Washingtons. If you could store all that data on 3.5" floppies, you’d need more than a hundred quadrillion floppies to capture it all — enough to cover the planet entirely (with much room for overlap) or to pave a nice bridge for yourself from your front porch well into interstellar space. If you could pull all that data into an HD movie, and you sat down to start watching that movie 2.5 million years ago (with your favorite saber-toothed friend, say), you’d still be watching the same movie today.

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As AWS Lambda has matured as a serverless platform, there are two key ways the service has evolved:

1. New capabilities that extend the platform to support new use cases like Lambda Container support, Lambda URLs and attribute-based access control support.
2. Performance enhancements that enable Lambda functions to be more responsive and cost-effective such as Tiered compilation, and Graviton2 support are just a few examples of the investments AWS made in this space.

With each advancement, the reasons to deploy a full VM or cluster to support your application get fewer and the time to deliver value in the cloud gets shorter.

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We introduced Crosswalk for AWS three years ago as a library of components on top of the core AWS platform to 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. Since then, we’ve added many new capabilities, expanded the portfolio of libraries, and made these libraries available to all Pulumi languages. We’ve also seen thousands of Pulumi customers, including more than 25% of all Pulumi AWS users, adopting one or more of the Crosswalk for AWS components to aid in delivering their AWS-based applications and services.

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Earlier this year we announced the experimental introduction of Update Plans as we heard from many of you that you need a strong guarantee about exactly which changes an update will make to your infrastructure, especially in critical and production environments. We have been making steady progress on this feature and are excited to further integrate it into your workflows. In the latest release of the Pulumi CLI (v3.48.0), there’s a new prompt to use experimental Update Plans when running an update.

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