In this introductory-level quest, you will get hands-on practice with the Google Cloud Platform’s fundamental tools and services. GCP Essentials is the recommended first Quest for the Google Cloud learner—you will come in with little or no prior cloud knowledge, and come out with practical experience that you can apply to your first GCP project. From writing Cloud Shell commands and deploying your first virtual machine, to running applications on Kubernetes Engine or with load balancing, GCP Essentials is a prime introduction to the platform’s basic features. 1-minute videos walk you through key concepts for each lab.
In this series of labs you will learn how to use BigQuery to analyze NCAA basketball data with SQL and end with building a Machine Learning Model to predict the outcomes of NCAA March Madness basketball tournament games.
In this advanced-level quest, you will learn the ins and outs of developing GCP applications in Java. The first labs will walk you through the basics of environment setup and application data storage with Cloud Datastore. Once you have a handle on the fundamentals, you will get hands-on practice deploying Java applications on Kubernetes and App Engine (the latter is the same framework that powers Snapchat!) With specialized bonus labs that teach user authentication and backend service development, this quest will give you practical experience so you can start developing robust Java applications straight away.
When it comes to hosting websites and web applications, you want a framework that’s robust, fast, and secure. By choosing the Google Cloud Platform, you will have all of those needs covered. In this fundamental-level quest, you will get hands-on practice with GCPs key infrastructure and computing services for the web. From deploying your first web app, to integrating Cloud SQL with Ruby on Rails, to mapping the NYC subway system on App Engine, you will learn all the skills needed to harness GCPs web hosting power.
In this Quest, you will learn how to write functions with the AWS Lambda Service that respond to events and integrate other AWS Services. You will create applications that write records to Amazon DynamoDB, send messages with Amazon SNS, and monitor events in Amazon CloudWatch and external services. You will even write a back-end function in Lambda for creating a voice-response app for Alexa and the Amazon Echo.
This lab takes you through how to create an Amazon Elastic Block Store (EBS) volume, attach it to an Amazon EC2 instance, take a snapshot of the volume, and increase the size and IOPS.
AWS Elastic Beanstalk provides a quick and easy way to deploy your web applications to the AWS cloud without requiring knowledge of the individual pieces that make up the infrastructure. This lab demonstrates the common steps of developing a web application and deploying it to production on AWS, using the EB command line interface. In this lab you will learn how to deploy a simple web application continuously using the Elastic Beanstalk Command Line Interface (EB CLI) in two ways, Rolling Deployment and Blue/Green Deployment. The lab also demonstrates many interesting command line tools to interact with, monitor, scale, and ssh into your running Elastic Beanstalk deployment completely from the command line. Prerequisites: for success with this lab, you should be familiar with systems administration of Linux servers, have comfort with Unix/Linux text editors, and should have at least taken the lab "Introduction to AWS Elastic Beanstalk".
This lab provides you with a basic overview of launching, resizing, managing, and monitoring an Amazon EC2 instance. Please DO NOT change the auto assigned region.
This lab provides the basic hands-on experience of Amazon EC2 Auto Scaling -- setting up Auto Scaling to automatically launch compute instances in response to conditions that you specify. You will use Auto Scaling via the AWS console to create the basic infrastructure of a Launch Configuration and an Auto Scaling group. You will test the configuration by terminating a running instance and viewing the results as Auto Scaling responds by scaling up and starting another instance. For the lab to function as written, please DO NOT change the auto assigned region.
Containers are becoming a popular way to run and scale applications across multiple cloud providers or on both cloud and on premise hardware. This lab provides a quick introduction to running a website on Google Container Engine using Docker.