In this introductory-level quest, you will get hands-on practice with the Google Cloud Platform’s fundamental tools and services. GCP Essentials is Qwiklabs’ most popular quest and for good reason—you will come in with little, or no prior cloud knowledge and come out with practical experience that you can apply to any GCP project. From writing Cloud Shell commands and deploying your first virtual machine, to running applications on Kubernetes Engine with load balancing, GCP Essentials is a prime introduction to the platform’s features.
If you are a novice cloud developer looking for hands-on practice with GCP’s core infrastructure services, do yourself a favor and enroll in this quest. As a student, you will get practical experience by taking labs that dive into Cloud Storage, computing engines like Kubernetes, and key application services like Stackdriver and Deployment Manager. By taking this quest, you will develop invaluable skills that apply to any GCP project.
Big data, machine learning, and artificial intelligence are today’s hot computing topics, but these fields are quite specialized and introductory material is hard to come by. Fortunately, GCP provides user-friendly services in these areas and Qwiklabs has you covered with this introductory-level quest, so you can take your first steps with tools like Big Query, Cloud Speech API, and Cloud ML Engine. Want extra help? 1-minute videos walk you through key concepts for each lab.
In this introductory-level quest, you will learn the fundamentals of developing and deploying applications on the Google Cloud Platform. You will get hands-on experience with the Google App Engine framework by launching applications written in languages like Python, Ruby, and Java (just to name a few). You will see first-hand how straightforward and powerful GCP application frameworks are, and how easily they integrate with GCP database, data-loss prevention, and security services.
This advanced-level quest is unique amongst the other Qwiklabs offerings. The labs have been curated to give developers hands-on practice with topics and services that appear in the Google Cloud Certified Professional Cloud Architect Certification. From IAM, to networking, to Kubernetes engine deployment, this quest is composed of specific labs that will put your GCP architecture knowledge to the test. Be aware that while practice with these labs will increase your knowledge and abilities, you will need other preparation too. The exam is quite challenging and external studying, experience, and/or background in cloud architecture is recommended.
This advanced-level quest is unique amongst the other Qwiklabs offerings. The labs have been curated to give developers hands-on practice with topics and services that appear in the Google Cloud Certified Professional Data Engineer Certification. From Big Query, to Dataproc, to Tensorflow, this quest is composed of specific labs that will put your GCP data engineering knowledge to the test. Be aware that while practice with these labs will increase your knowledge and abilities, you will need other preparation too. The exam is quite challenging and external studying, experience, and/or background in cloud data engineering is recommended.
In this lab, you configure VPC Network Peering between two networks. Then, you verify private communication between two VMs in those networks.
Learn the process of analyzing a data set stored in BigQuery using Cloud Datalab to perform queries and present the data using various statistical plotting techniques.
In this lab, you create a Deployment Manager configuration along with templates to automate the deployment of a custom network.
This lab shows how to define new node IP address mappings by using network address translation (NAT) gateways.
This hands-on-lab uses Kubernetes and Cloud Vision API to create an example of how to use the Vision API to classify (label) images from Reddit’s /r/aww subreddit and display the labelled results in a web app.
In this hands-on-lab you will create infrastructure, a virtual machine, using Terraform in the cloud environment.
In this lab, you add images and video to an application. You store files as objects in a Cloud Storage bucket.
This lab shows you how to create a HTTPS load balancer to forward traffic to a custom URL map, which then sends traffic to the region closest to you with static assets being served from a Cloud Storage bucket.
In this lab, you create two nginx web servers and control external HTTP access to the web servers using tagged firewall rules. Then, you explore IAM policies and service accounts.
In this hands-on lab you will learn to create Cloud SQL instances with Terraform, then set up the Cloud SQL Proxy, testing the connection with both MySQL and PostgreSQL clients.
In Terraform, a Provider is the logical abstraction of an upstream API. This lab will show you how to setup a Kubernetes cluster and deploy Load Balancer type Nginx service on it.
This lab will demonstrate how to use the Regional Load Balancer GCP Terraform modules for setting up various load balancers.
In this lab, you create several VPC networks and VM instances and test connectivity across networks.
DynamoDB Workshop CloudFormation Template: Create an Amazon EC2 instance running the Amazon Linux with the applications required for running the DynamoDB workshop.
In this lab, you'll learn how to deploy a new Ruby on Rails application using Google Cloud SQL for PostgreSQL to Google App Engine Flexible environment.
In this lab, you will enhance the online Quiz application by developing a backend service to process user feedback and save scores.
This lab will show you how to deploy a set of Cloud Functions in order to process images and videos with the Cloud Vision API and Cloud Video Intelligence API.
In this lab, you will enhance the online Quiz application to use Firebase authentication.
In this lab, you configure an HTTP Load Balancer with global backends. Then, you stress test the Load Balancer and blacklist the stress test IP with Cloud Armor.
This lab shows you how to set up multiple NAT gateways with Equal Cost Multi-Path (ECMP) routing and autohealing enabled for a resilient and high-bandwidth deployment.
This lab builds a complete serverless application that demonstrates how to convert text-to-speech using Amazon Polly.
The lab demonstrates how to use Amazon RedShift to create a cluster, load data, run queries and monitor performance. Note: Students will download a free SQL client as part of this lab.
The Cloud Vision API lets you understand the content of an image by encapsulating powerful machine learning models in a simple REST API. In this lab you’ll send an image to the Cloud Vision API and have it identify objects, faces, and landmarks.
In this lab you spin up a virtual machine, configure its security, access it remotely, and then carry out the steps of an ingest-transform-and-publish data pipeline manually. This lab is part of a series of labs on processing scientific data.
In this lab you connect two networks using Cloud Routers. Cloud Routers build on top of VPN by enabling BGP Routing that dynamically discovers changes in network topology and passes updates to peers.
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.
In this lab you will create a local Git repository that contains files for a sample App Engine application, add a GCP repository as a remote, and push the contents of the local repository.
Google Cloud Platform (GCP) Virtual Private Cloud (VPC) Network Peering allows private connectivity across two VPC networks regardless of whether or not they belong to the same project or the same organization.
This lab demonstrates how to access and manage AWS services in three ways: through the AWS Management Console, the AWS Command Line Interface (CLI), and the AWS Software Development Kit (SDK). You will use one or more of these three options to access Amazon S3, Amazon EBS, Amazon EC2 and Amazon CloudWatch.
In this lab you'll create an instance with a custom machine type, a bastion host, and a NAT gateway using Terraform.
The Cloud Natural Language API lets you extract entities, and perform sentiment and syntactic analysis on a block of text. In this hands-on lab you’ll learn how to extract entities and sentiment from text using the Cloud Natural Language API.
In this lab, you create an auto-mode VPC network with firewall rules and 2 VM instances. Then, you explore the connectivity for the VM instances.
Learn the process for partitioning a data set into a training set that will be used to develop a model, and a test set that can then be used to evaluate the accuracy of the model and then independently evaluate predictive models in a repeatable manner.
Internal Load Balancer offers you the possibility to load balance TCP/UDP traffic without exposing your VMs via a public IP to the Internet. In this lab we will create a public facing web server to serve the result of a simple web application.
This lab introduces the concept of Elastic Load Balancing (ELB). In this lab you will use ELB to load balance a set of web servers in an Availability Zone. You will launch a pair of Amazon EC2 instances, bootstrap them to install web servers and content, and then access the instances independently using Amazon EC2 DNS records. Next, you will set up ELB, add your instances to the ELB, and then access the ELB DNS record to watch your requests load balance between servers. Finally, you will look at ELB metrics in CloudWatch. To successfully complete this lab, you should be familiar with the AWS Management Console.
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.
In this lab, you will learn how to deploy a new Ruby on Rails application or Rails app for short to Google App Engine Flexible environment. You will learn Cloud Shell and the Cloud SDK to get started without needing any downloads or installs.
In this lab, you'll build a Google App Engine proxy for the Google Places API web service.
The Cloud Security Scanner identifies security vulnerabilities in your Google App Engine web applications.
This lab will serve as an introduction to Kubeflow, an open-source project which aims to make running ML workloads on Kubernetes simple, portable and scalable.
In this lab, you will build a Fact Skill in the Amazon Developer Portal, then build a Lambda function to handle notifications from Alexa. You will use a sample Fact skill for this lab, which you can use as a template for your own Skill after completing the lab. You will use both the AWS Console and the Amazon Developer Portal in this lab. You do not need an Alexa device. Prerequisites: To successfully complete this lab, you should be familiar with AWS Lambda through taking the introductory lab. Familiarity with Node.js programming will be helpful, although full solution code is provided. You will need to have/create a no-cost, no-credit-card-required account in the Amazon Developer Portal. Familiarity with the Amazon Developer Portal and the Alexa Skills Kit is helpful, though not required. You do not need an Alexa device for this lab.
This hands-on lab will show you how to set up Jenkins on Google Kubernetes Engine to help orchestrate your software delivery pipeline.
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 creates a complex Deployment Manager (DM) configuration for deploying a custom network resource in Google Cloud Platform. It also explains the basic fundamental block of developing a Deployment Manager.