We’re excited to introduce Google Cloud support to Insights Account Discovery. This new capability expands Pulumi’s resource discovery to help you uncover infrastructure in Google Cloud, regardless of how those resources are managed. With full visibility into all of your cloud resources, you can monitor, search, and manage your environments more effectively.
Kubernetes has transformed cloud infrastructure by enabling scalable, containerized applications. While it initially gained traction for managing web applications and microservices, its capabilities now extend to AI/ML workloads, making it the go-to platform for data scientists and machine learning engineers.
Running AI/ML workloads on Kubernetes presents unique challenges, including:
Google Cloud Kubernetes (GKE) provides a robust foundation for AI/ML workloads, but managing infrastructure manually can be cumbersome. This is where Pulumi comes in—enabling Infrastructure as Code (IaC) to automate and simplify AI/ML infrastructure on Kubernetes.
The latest major release of the Pulumi Google Cloud Classic Provider is available now! This 7.0 release contains the latest upstream changes to keep you up-to-date along with a highly requested bug fix, keeping your journey in managing Google Cloud resources fresh and smooth.
The Pulumi Google Cloud Classic provider can be used to provision any of the cloud resources available in the upstream provider. It is part of the suite of Pulumi official providers, which means that it is officially maintained and supported by Pulumi. The provider is also open source and available on GitHub for you to contribute and grow.
Point and click in the console is great when you’re first starting out learning a new cloud or managed service, but it quickly becomes a hindrance when cloud infrastructure is widely adopted by an organization. The point at which the term “widely adopted” becomes applicable to your situation differs, but at some point in their careers, many infrastructure and platform engineers are faced with situations where a large number of critical infrastructure resources were created through “click ops” with no ability to track changes, reproduce environments consistently, and so on. When this happens (and it will probably happen to many of you), it’s time to import those resources into infrastructure as code.
Fortunately, Pulumi has one of the smoothest and most powerful import processes of any IaC tool. In this post, we’re going to show you how to automate the bulk importation of Google Cloud resources into Pulumi! This approach will also work on resources that were created by another IaC tool.
Denis Willett is a software engineer at the North Carolina Institute of Climate Studies who works on the NOAA Open Data Dissemination Program. His work focuses on leveraging cloud technologies for the development of data processing and machine learning pipelines. Denis did his PhD in Entomology and Nematology at University of Florida and his undergraduate and masters work in Earth Systems at Stanford University. You can read his full bio here.
NOAA Open Data Dissemination (NODD) makes environmental data freely and publicly accessible across Amazon Web Services (AWS), Microsoft Azure (Azure), and Google Cloud Platform (GCP). These data include near real-time satellite imagery, weather models, radar feeds, drought information, ocean databases, and a suite of climate data records among many others. This program supports more than 220 datasets and over 24PB of open data. Since its inception, the program has been growing rapidly, almost doubling in size over the past year.
Kubernetes is everywhere now, but it’s primarily been the domain of people working on the ops side of infrastructure. What about devs, though? You benefit from knowing what Kubernetes is and how to use it, too—otherwise, we’re still putting teams in silos. In this blog, we’re going to build off part one by learning about managed Kubernetes services: what they are, when they’re useful, and how you can try deploying to one yourself, starting with Google’s Kubernetes Engine (GKE).
Here at Pulumi, everyone on our engineering team is a Gopher. Go has quickly become the “language of the cloud,” and so when we chose to build our open-source pulumi/pulumi engine and SaaS backend, we chose Go. As such, we are very excited to welcome Go to the family of supported infrastructure as code languages as part of Pulumi 2.0.
Go has become the lingua franca of cloud-native infrastructure development. From projects like Docker, to Kubernetes, the community has embraced Go. The result is an ecosystem rich in tooling and packages designed for systems and cloud engineering.
In an earlier article, we introduced examples of Policy as Code to prevent two of the most common causes of data breaches. Policies are the guardrails of infrastructure. They control access, set limits, and manage how infrastructure operates. In many systems, policies are created by clicking on a GUI, making it difficult to replicate or version. Pulumi implements policy by writing it in Typescript, which ensures that you can write policies using software development practices such as automated testing, deployment, and version control.
Google Cloud Run is the latest addition to the serverless compute family. While it may look similar to existing services of public cloud, the feature set makes Cloud Run unique:
Cloud Run is targeted very specifically at stateless web applications. It uses ephemeral containers, and each execution is limited to 15 minutes.
This post describes an early version of Crosswalk for Kubernetes. Some of the links, examples, and implementation details may have changed. For the most up-to-date information, see the Pulumi Kubernetes documentation.
Running Kubernetes in production can be challenging. This past year, Pulumi has collected common patterns of usage informed by best practices for provisioning Kubernetes infrastructure and running containerized applications. We call this Pulumi Crosswalk for Kubernetes: a collection of playbooks and libraries to help you to successfully configure, deploy, and manage Kubernetes in a way that works for teams in production.