We have been busy shipping improvements in the last 2 months. Let’s walk through the release highlights across Pulumi engineering areas from March and April. If you want to learn more between release blogs, follow the CLI improvements in the pulumi/pulumi repo changelog and Pulumi Cloud features in the new features blogs.
PulumiUP is our virtual user conference for the Pulumi community and anyone interested in Infrastructure as Code. Every year, we strive to build a program packed with technical talks, demos, and best practices with the goal of leaving you with new learnings and inspiration that will help you become a better cloud engineer. You’ll hear from industry leaders and experts about IaC, software engineering, DevOps, Platform engineering, and AI. We hope you’ll join us virtually and take this opportunity to grow your knowledge and become infrastructure as code stars.
This post continues our series of blog posts focused on IaC recommended practices. In earlier posts, we introduced Zephyr Archaeotech Emporium, the fictional company that sits at the center of this series, and discussed Zephyr’s primary use case for Pulumi: managing their online retail store. You read how Zephyr’s initial use of Pulumi changed to incorporate the use of short-lived per-developer stacks. Later, as Zephyr continued to grow, you saw how Zephyr restructured their Pulumi projects and stacks, and incorporated the use of Stack References. This post is a complement to the post on structuring Pulumi projects, concentrating on the use of role-based access control (RBAC) and security in Zephyr’s multi-project configuration.
Pulumi AI harnesses a form of generative AI, known as large language models, to help you discover, learn, and use new cloud infrastructure APIs with ease. Think of Pulumi AI as a sophisticated compass, guiding you through the ever-changing landscape of cloud infrastructure and pointing you in the direction of the most suitable solutions for your unique requirements. In this blog post, we’ll explore our recent enhancements to Pulumi AI, focusing on how we’ve integrated Pulumi Package schema data to generate more accurate and relevant Pulumi programs.
Last month we announced a new Pulumi Cloud feature available for everyone: Resource Search. In the past month, Resource Search has been the fastest adopted feature since launching Pulumi Cloud, with thousands of users leveraging the feature to find resources across cloud environments, projects, stacks, teams, and users. Today we are announcing two new improvements to Resource Search: advanced filtering and Pulumi Teams support.
Danny Zalkind is the Senior Director of Infrastructure Engineering for Skai, an award-winning intelligent marketing platform. He brings his 15 years of experience of managing tech teams to his current role where he’s dedicated to allow Skai R&D to efficiently produce and serve software. You can find him on Linkedin. As Skai continues its journey towards fully migrating to the cloud using Pulumi, we’ve taken another large bite out of the migration pie, moving our most critical data to AWS on top of Amazon Keyspaces, an Apache Cassandra–compatible database service.
We have a global community made up of people from many different countries, but we also have many local communities. We want to enable our users to meet, share knowledge and ideas, answer questions, and learn about Pulumi and best practices. For that reason, we created the Pulumi User Groups, aka PUGs on Meetup. Members of PUGs will have access to a global network of experts who can offer help, advice, and support on using Pulumi effectively.
If you’ve been following along with our IaC Recommended Practices series, then you’re already familiar with Zephyr Archaeotech Emporium, the fictional company at the center of the series. Today, you’ll get an inside look at how Zephyr starts using Pulumi for locally testing the application code for their online store and accelerating the inner dev loop for their development team.
We recently released Pulumi AI, a purpose-built AI Assistant that can create cloud infrastructure using Pulumi. It builds on the power of Large Language Models (LLMs) and GPT to dramatically reduce the time it takes to discover, learn and use new cloud infrastructure APIs.
We’ve seen amazing engagement and stories from Pulumi users about the impact this tool has had for them over the past few weeks. In this post, we’ll dive deeper into this new technology, and share why we and so many other Pulumi users are so excited about Pulumi AI.
In this blog series, you will learn how to create a hub-and-spoke network architecture in AWS with centralized egress and traffic inspection. In this second installment, we’ll show you how to create spoke VPCs to run your workloads, verify centralized egress is working, and then add centralized traffic inspection using Pulumi, the infrastructure as code tool that enables you to manage infrastructure with real programming languages!
