When building with Kubernetes for the first time, we often need to stand up a lot of infrastructure just to get to the point of having a base to build an application. Let’s explore how we can wire together two of our architecture templates to generate a base for a web application running on Kubernetes on Google Cloud with Python and Poetry.
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.
As AWS Lambda has matured as a serverless platform, there are two key ways the service has evolved: New capabilities that extend the platform to support new use cases like Lambda Container support, Lambda URLs and attribute-based access control support. Performance enhancements that enable Lambda functions to be more responsive and cost-effective such as Tiered compilation, and Graviton2 support are just a few examples of the investments AWS made in this space.
Portions of this blog post are out of date. See the AWS guides for an updated overview and examples. We introduced Crosswalk for AWS three years ago as a library of components on top of the core AWS platform to make it easier to get from zero to production on AWS, easier to adopt AWS best practices by default, and easier to evolve your AWS infrastructure as your application needs mature.
Earlier this year we announced the experimental introduction of Update Plans as we heard from many of you that you need a strong guarantee about exactly which changes an update will make to your infrastructure, especially in critical and production environments. We have been making steady progress on this feature and are excited to further integrate it into your workflows. In the latest release of the Pulumi CLI (v3.48.0), there’s a new prompt to use experimental Update Plans when running an update.
Self-service infrastructure is the holy grail of DevOps. When platform engineering teams can empower application teams to provision their own infrastructure without needing to understand the details of configuring networking, storage, and compute resources, IT organizations can drastically increase their ability to deliver on organizational goals. The first step in this process is to codify infrastructure best practices using platforms like Pulumi and the next step is to make these best practices available in a workflow that fits into the application team’s software development tools and process.
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.
In addition to our Cloud Engineering Days launches, we have been busy shipping improvements in the last 2 months. Let’s walk through the release highlights across Pulumi engineering areas from September and October. If you want to learn more between release blogs, follow the CLI improvements in the pulumi/pulumi repo changelog and Pulumi Service features in the new features blogs.
Whether you’re building a new application or moving an existing application over from another provider, the basic framework of your infrastructure probably isn’t something you want to worry about if you don’t have to. The cloud is complicated enough as it is. With Architecture Templates, Pulumi takes on some of the work involved in deploying your application to the cloud provider of your choice. Let’s take a tour of the new Serverless Templates for AWS, GCP, and Azure!
If you’ve deployed resources to your favorite cloud provider, you have probably found yourself sitting in the console thinking: “I don’t know how long this is going to take.” Then you deploy the resource and think: “When did I even start this?” When using Pulumi, the CLI prints out how long the update took after it ran, but while you’re in the moment, it feels like ages. We’re excited to announce a CLI usability enhancement You can now see how long each of your resources are taking to deploy.
