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.
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.
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?
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.
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?
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.
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.
One thing I love about Pulumi is how easy it is to configure a stack. As a builder mainly of web applications, I’m always thinking about how I’ll configure my apps from one environment to the next, and being able to use Pulumi’s built-in support for configuration and secrets to manage the API keys and database credentials for my dev, staging, and production stacks individually is incredibly convenient. For larger teams and organizations, though, where multiple applications rely on a set of common configuration settings — dozens of apps, say, depending on the same API service or database — having to keep all of those config settings in sync across all of those individually can become a bit of a pain.
When I was a kid growing up in Southern California, there was a phone number you could call to find out what time it was. It was a local number, 853-1212 (easy to remember as the arrangement of the numbers on the keypad made a capital T), and I used it all the time, to set my watch, adjust the alarm clock, fix the display on the VCR. I don’t recall the last time I used it, probably sometime in the mid ’90s, but I do remember clearly the sound of the voice at the other end of the line.
As a developer, I get lots of ideas for web apps—little things, mostly: nifty ways to keep track of my kids’ allowances, habit trackers, shopping lists. Most of them, however, never see the light of day, and not just because I’m lazy; I also tend to get hung up trying to decide what to use for the technology stack.
