Thank you for joining the PulumiUP event. We had a stellar set of speakers and panelists discussing the future of DevOps and how Cloud Engineering is providing the tools and processes that enable faster delivery, the right mix of architecture, and foster collaboration among teams in an organization. Here are some of the highlights and takeaways from our speakers.
Earth is just the beginning. We are putting down the foundations of space so our children can build their future. At Pulumi, we are committed to making life multi-planetary. We are excited to announce Pulumi Interstellar, a collection of resource providers that will help us reach the future of a space-faring and multi-planet species.
Companies that have suffered data breaches are, unfortunately, frequently in the news. A data breach is when information that should be private, such as credit card numbers or even trade secrets, is stolen. These thefts can be because of an actual cyber-attack, but they can also be due to simple carelessness, such as disposing of computer equipment without taking proper precautions.
Data science has advanced because tools like Jupyter Notebook hide complexity by running high level code for the specific problem they are trying to solve. Increasing the level of abstraction lets a data scientist be more productive by reducing the effort to try multiple approaches to near zero, which encourages experimentation and better results.
Data scientists typically work locally, but they often store data for analyses and models in the cloud. There are clear advantages to using cloud resources for these tasks:
SDKs like AWS’ Python library, boto3, can create resources, but they still require domain expertise to manage and properly architect a solution. The Pulumi Automation API improves on raw SDKs by providing high-level abstractions for creating and managing cloud services, letting data scientists concentrate on analyses and models without being well-versed in cloud APIs.
Policies set the guardrails for your applications and infrastructure. They define many aspects of how your company manages its applications and infrastructure. Security, safe use of resources, and compliance with external standards are just a few examples of what a policy can define.
Currently available in public preview, Pulumi’s Automation API enables you to provision your infrastructure programmatically using the Pulumi engine. Today, we are excited to announce C# support for Automation API, enabling .NET developers to automate infrastructure deployments, create complex orchestration workflows, build custom ops tooling, and build cloud frameworks. Read more about the Automation API here.
Whether you are working with Kubernetes or serverless, your application uses containers. If you use the Docker desktop client, images are pushed to Docker Hub by default. Pulling images from Docker Hub is convenient, but there are many reasons to store images in your own registry. For example, Docker Hub doesn’t guarantee to produce the same image on repeated pulls, i.e., your base image might have changed. It’s also possible to inadvertently expose secrets in an intermediate image used to build the image stored on Docker Hub. There is also the possibility of vulnerabilities in even official images. This article shows how to create a repository and how to build and push images to that repository
General-purpose languages enable Infrastructure as Software – bringing tested toolchains and best practices to building infrastructure, e.g., languages, IDEs, testing, debugging, componentization, packaging, and versioning. Available in public preview, Pulumi’s Automation API is a robust programmatic layer on top of Pulumi’s infrastructure engine. It exposes Pulumi programs and stacks as strongly-typed and composable building blocks. Automation API allows you to embed the Pulumi engine inside your software projects so you can build software automation around entire infrastructure provisioning processes that normally require humans to operate.
Today, we are excited to announce Python support for this powerful feature, opening up a world of possibilities for Python developers.
Starting can be daunting. Before you take your first step, there’s a lot to consider, but you can prepare your development environment ahead of time to make your first steps in cloud engineering smooth and productive. In this article, we’ll cover how to set up your development environment to work across cloud providers, multiple languages, and different operating systems.
Whether you’re migrating to the cloud or have existing infrastructure, cloud spend can be a significant barrier to your success. Too small of a budget could prevent your organization from meeting your performance metrics. You can use different strategies to reduce cloud spend, such as using Spot Instances, which cost less than On-Demand Instances or scaling your infrastructure based on peak usage times.
With the addition of Graviton2 based EC2 Instances, AWS offers an on-demand alternative for decreasing cloud spend. Both Amazon and independent testing demonstrated that the general-purpose M6g instance delivered up to a 40% gain of price/performance compared to Intel m5.large instances. In addition to the M6g general-purpose instance, AWS offers instances general-purpose burstable (T4g), compute-optimized (C6g), and memory-optimized (R6g) EC2 instances.
