G Suite: Integrations
This Quest of hands-on labs demonstrates the power of integrating Google Cloud Platform services and tools with G Suite applications. With integration technologies such as App Script and the Clasp Command Line environment, you will create and publish web apps and add-ons for G Suite products: Sheets, Docs, Forms, and Slides. With App Maker you will build a ready-to-use app that has a Google Cloud SQL Database, Google Maps integration, and a Mobile Responsive Design. Other labs create direct connections to GCP data sources-- using the BigQuery API, Sheets, and Slides to collect, analyze and present data.
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. 1-minute videos walk you through key concepts for each lab.
This fundamental-level quest is unique amongst the other Qwiklabs offerings. The labs have been curated to give IT professionals hands-on practice with topics and services that appear in the Google Cloud Certified Associate Cloud Engineer Certification. From IAM, to networking, to Kubernetes engine deployment, this quest is composed of specific labs that will put your GCP knowledge to the test. Be aware that while practice with these labs will increase your skills and abilities, we recommend that you also review the exam guide and other available preparation resources.
Baseline: Data, ML, AI
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.
This advanced-level quest is unique amongst the other Qwiklabs offerings. The labs have been curated to give IT professionals 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 skills 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.
Containerized applications have changed the game and are here to stay. With Kubernetes, you can orchestrate containers with ease, and integration with the Google Cloud Platform is seamless. In this advanced-level quest, you will be exposed to a wide range of Kubernetes use cases and will get hands-on practice architecting solutions over the course of 9 labs. From building Slackbots with NodeJS, to deploying game servers on clusters, to running the Cloud Vision API, Kubernetes Solutions will show you first-hand how agile and powerful this container orchestration system is.
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. 1-minute videos walk you through key concepts for each lab.
Introduction to Amazon EC2 Auto Scaling
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.
Exploring Google Ngrams with Amazon EMR
This lab demonstrates how to launch an Amazon Elastic MapReduce (EMR) cluster for Big Data processing and use Hive with SQL-style queries to analyze data. You will create a Hadoop cluster using Amazon EMR which will allow to run interactive Hive queries against data stored in Amazon S3. You will use Hive to normalize the data in a more useful way, and you will run queries to analyze the data.
Creating an Amazon Virtual Private Cloud (VPC) with AWS CloudFormation
This lab will demonstrate how to create an Amazon Virtual Private Cloud (VPC) network using AWS CloudFormation. Note: This lab is a more of a walkthrough of a template rather than "learn how to build it". You will walk through the sections of an AWS CloudFormation template and get explanations for each step. You will then launch the AWS CloudFormation template to create a four-subnet Amazon VPC that spans two Availability Zones and a NAT that allows servers in the private subnets to communicate with the Internet in order to download packages and updates.
Introduction to Amazon Simple Storage Service (S3)
Этот самоучитель посвящен использованию корзин Amazon S3, а также управлению файлами и объектами в корзинах. Вы научитесь создавать корзины, добавлять, просматривать и перемещать объекты, а также удалять корзины и объекты с помощью Консоли управления AWS. Ознакомительная демонстрация доступна по адресу http://youtu.be/O4jEzZVU3eo
APIs Explorer: Compute Engine
Use the APIs Explorer to create a Compute Engine instance, then use Stackdriver to monitor the CPU usage.
Simulating a Data Warehouse in the Cloud Using BigQuery and Dataflow
In this lab you build several Data Pipelines that will ingest data from the USA Babynames dataset into BigQuery, simulating a batch transformation
App Dev - Deploying the Application into Kubernetes Engine - Java
In this lab, you will deploy the quiz application into Kubernetes Engine, leveraging Google Cloud Platform resources including Container Builder and Container Registry, and Kubernetes resources including Deployments, Pods, and Services.
Build a Serverless Text-to-Speech Application with Amazon Polly
This lab builds a complete serverless application that demonstrates how to convert text-to-speech using Amazon Polly.
Video on Demand with AWS Elemental MediaConvert
In this lab, you will utilize the AWS Elemental MediaConvert Service to convert input video into multiple output formats, combine multiple videos into one during the conversion process, add captions/watermarks to the videos, and work with ad insertion metadata.
Become A Cloud Expert
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