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 Infrastructure as Code best practices around these topics, using Zephyr and their online store as the use case.

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Event streaming is used across diverse industries that demand real-time data processing. Apache Kafka is the most popular open-source streaming platform. Confluent Cloud lets you run Kafka on the cloud provider of your choice.

In this blog post, you’ll use the Confluent Cloud Pulumi provider and Pulumi to create a Kafka cluster, topic, and customer account.

1. About Apache Kafka and Confluent Cloud
2. Initializing the Project
3. Adding Resources
4. Testing Kafka Cluster
5. See it in action
6. Conclusion
7. Additional Resources

About Apache Kafka and Confluent Cloud

What is Apache Kafka?

Apache Kafka is an event store and stream-processing platform, used by more than 30% of the Fortune 500 today. Using Kafka streams, developers can write modern, event-driven applications for real-time data streaming and processing. Kafka is used across many industries, including gaming, financial services, healthcare, retail, automotive, and manufacturing.

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What is FinOps?

The FinOps Foundation eloquently defines FinOps as “an evolving cloud financial management discipline and cultural practice that enables organizations to get maximum business value by helping engineering, finance, technology and business teams to collaborate on data-driven spending decisions.” Simply put, FinOps is the continuous effort to control cloud spend.

Just as organizations have adopted operations-focused best practices into software development cycles and have considered how to best insert security best practices along the way, financial best practices may also be codified by developers writing cloud programs.

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In an enterprise organization, an IT self-service “vending machine” allows employees to quickly and easily request and receive access to pre-approved cloud resources. Behind the scenes, Pulumi programs may orchestrate any of the requisite resources. We will look at an example of using Pulumi to create an AWS child account, within an AWS Organization.

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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? Many teams come to this realization only when they’ve deployed too many production workloads to start over from scratch. If your organization is looking at Pulumi as an IaC solution, it’s worth bringing these resources under management because of the low effort and high value of having a single pane of glass to manage all of your resources.

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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?
• 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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