Christian Nunciato

Welcome to the sixth post in our series of blog posts focused on infrastructure as code (IaC) recommended practices. So far in this series, you’ve seen how Zephyr Archaeotech Emporium—the fictional company at the center of this series—uses Pulumi to manage their online retail store. You read how Zephyr’s initial use of Pulumi changed to use short-lived per-developer stacks. Later, as Zephyr continued to grow, you saw how Zephyr restructured their Pulumi projects and stacks, incorporated Stack References, and used Pulumi Cloud’s role-based access control (RBAC) functionality to control access to their stacks. This post focuses on how Zephyr takes advantage of the Pulumi Automation API to bring an even greater level of orchestration to the stacks that represent their online store.

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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.

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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.

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This is the fourth post in a series of blog posts focused on Zephyr Archaeotech Emporium—our fictional company—and their use of Pulumi to manage their online retail store. In the first three posts, you saw how Zephyr’s initial use of Pulumi changed as the company grew, and how the use of short-lived per-developer stacks helped Zephyr’s application development team meet the demands of a fast-growing company. This post is a complement to the earlier post on structuring Pulumi projects, discussing how Zephyr uses Stack References to link their projects together and sharing some recommended practices around the use of Stack References.

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This is the third post in a series of blog posts focused on Zephyr Archaeotech Emporium—our fictional company—and their use of Pulumi to manage their online retail store. In the first post, you saw how Zephyr initially decided to go with a single Pulumi project for managing deployments of their online retail store application. In this post, you’ll see how Zephyr’s use of Pulumi changes as their company grows and evolves.

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In the first post of this series, we introduced Zephyr, a fictional company that uses Pulumi to manage its online retail store. Following on from that post, which discusses code organization and stacks, this post explores two more questions users frequently ask when working with Pulumi in teams — namely, How can I best enable multiple developers to collaborate on a Pulumi project? And how can I use Git and Git branching to support this kind of collaboration?

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This is the first in a series of blog posts that explores how a fictional company—Zephyr Archaeotech Emporium—uses Pulumi to manage their online retail store. This post explores a couple common questions that users ask when working with Pulumi; specifically, where should I store my Pulumi code? And how do I support multiple environments with Pulumi? This post will provide some guidance and recommended practices around these topics, using Zephyr and their online store as the use case.

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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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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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