A few weeks ago, I was speaking with a consultant at one of the big firms who asked me how he could introduce Pulumi into a client’s organization when that client had created many infrastructure resources manually through the AWS console and was running production workloads on those resources. Introducing modern cloud infrastructure tooling and automation is relatively simple (or at least more straightforward) when organizations decide to adopt IaC from the start of their cloud journey, but what about organizations who have gone far enough down the route of manually created cloud infrastructure to see the perils of that approach?

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This is the first post in a series about performance optimizations we’ve made to the Pulumi CLI. Over the last six months at Pulumi, the Platform Team has been working on a project we call “Amazing Performance.” Amazing Performance is a new initiative to improve the throughput and latency of the Pulumi CLI not only for power users but for everyone. By the end of June 2022, we assembled a list of issues containing both high-value improvements requiring a sizable investment and low-hanging fruit for quick wins. The full list, including the items we have yet to tackle, is contained in a tracking issue on GitHub. This blog series will cover the highlights.

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We are excited to announce that we’ve updated the Pulumi Provider Boilerplate to make custom provider implementation easier!

This major update brings a wealth of usability improvements to the Pulumi Provider Boilerplate by incorporating our brand new Pulumi Go Provider SDK.

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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?

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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.

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