Learn how to run large-scale, data-intensive workloads with Compute Engine, Google’s cloud platform. Written by Google engineers, this tutorial walks you through the details of this Infrastructure as a Service by showing you how to develop a project with it from beginning to end. You’ll learn best practices for using Compute Engine, with a focus on solving practical problems.
With programming examples written in Python and JavaScript, you’ll also learn how to use Compute Engine with Docker containers and other platforms, frameworks, tools, and services. Discover how this IaaS helps you gain unparalleled performance and scalability with Google’s advanced storage and computing technologies.
- Access and manage Compute Engine resources with a web UI, command-line interface, or RESTful interface
- Configure, customize, and work with Linux VM instances
- Explore storage options: persistent disk, Cloud Storage, Cloud SQL (MySQL in the cloud), or Cloud Datastore NoSQL service
- Use multiple private networks, and multiple instances on each network
- Build, deploy, and test a simple but comprehensive cloud computing application step-by-step
- Use Compute Engine with Docker, Node.js, ZeroMQ, Web Starter Kit, AngularJS, WebSocket, and D3.js
Marc manages Google's Developer Relations Engineering team in London, which helps software developers get the most out of the Google APIs and services in the EMEA region. In a previous life,Marc helped design and build communication systems at Bell Labs and Lucent Technologies. When he's not working, Marc enjoys indie music and films, writing, teaching, and chess.
Kathryn Hurley is a Developer Programs Engineer at Google for Compute Engine. In this role, she helps developers learn how to use the Compute Engine API by developing sample applications. She received an MS in Web Science from the University of San Francisco and a BS in Genetics from the University of California, Davis. Prior work experience includes research in mobile and peer-to-peer computing.
Paul Newson is a Software Engineer at Google. Currently, he is focusing on helping developers harness the power of the Google Could Platform to solve their Big Data problems. Prior to his current role in Developer Relations, Paul was helping build Google's Cloud Platform as an engineer on Google Cloud Storage. Before joining Google, Paul cofounded a tiny game technology startup, sold it to Microsoft, where he then worked on DirectX, Xbox, Xbox Live, and Forza Motorsport, before spending some time working on interesting machine learning problems in Microsoft Research. Outside of work Paul enjoys rock climbing, motorcycling, and other activities that demand complete focus.
