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GCP Essentials
In dieser Mission, der beliebtesten Mission aller Zeiten, können Sie erste praktische Erfahrungen mit Google Cloud sammeln. Machen Sie sich mit den Grundlagen vertraut, z. B. damit, wie Sie eine VM einrichten und wichtige Infrastrukturtools mithilfe fortgeschrittener Stackdriver- und Kubernetes-Konzepte konfigurieren können.
Baseline: Infrastructure
This Quest of all-Introductory-level Self-Paced Labs is intended for the complete beginner in Google Cloud. In this eleven-lab sequence, you will get your first-touch experiences with the Google Cloud services that provide basic infrastructure building blocks that make up core components of any production cloud environments.
Baseline: Data, ML, AI
This Quest of all-Introductory-level Self-Paced Labs is intended for the complete beginner in Google Cloud. In these short hands-on labs, you will get your first-touch experiences with a subset of the Google Cloud services that provide tools for working with big (and small!) data and machine learning / artificial intelligence services in Google Cloud.
Baseline: Deploy & Develop
This Quest of all-Introductory-level Self-Paced Labs is intended for the complete beginner in Google Cloud. In these short hands-on labs, you will get your first-touch experiences with a subset of the Google Cloud services that provide tools for creating and deploying applications: App Engine, the SDK, Source Tools, data stores for your application data, and useful tools for security and privacy, all provided by Google Cloud.
Cloud Architecture
This Quest is intended to provide hands-on practice for those preparing for the <A HREF="https://cloud.google.com/certification/cloud-architect">Google Cloud Certified Professional Cloud Architect Certification</A>. Students will get hands-on exposure to a wide range of Google Cloud Services and techniques which are closely mapped to the topic areas in the certification test. Be aware that while practice with these labs will increase your knowledge and abilities in these topic areas, you will need other preparation to succeed in the question and answer test itself (such as work experience).
Data Engineering
This Quest is intended to provide hands-on practice for those preparing for the <A HREF="https://cloud.google.com/certification/data-engineer">Google Cloud Certified Professional Data Engineer Certification</A>. Students will get hands-on exposure to a wide range of Google Cloud Services and techniques which are closely mapped to the topic areas in the certification test. Be aware that while practice with these labs will increase your knowledge and abilities in these topic areas, you will need other preparation to succeed in the question and answer test itself (such as work experience).
Advanced Design Patterns using Amazon DynamoDB
DynamoDB Workshop CloudFormation Template: Create an Amazon EC2 instance running the Amazon Linux with the applications required for running the DynamoDB workshop.
VPC Network Peering
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.
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.
Improving Network Performance I
In this lab, you'll work on practical elements of performance testing, with an eye to improving network bandwidth in your environment by using different core sizes and internal vs. external networks.
Network Performance Testing
In this lab, you'll learn important connectivity, performance, testing, and troubleshooting techniques in the Google Cloud Networking insfrastructure environment.
Improving Network Performance II
In this lab, you'll work on further practical elements of performance testing, toward improving network server performance by using load balancing and content caching.
Scanning User-generated Content Using the Cloud Video Intelligence and Cloud Vision APIs
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.
Building High Availability and High Bandwidth NAT Gateways
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.
Create a Custom Network Using Deployment Manager
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.
Working with Amazon Redshift
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.
Setting up Jenkins on Kubernetes Engine
This hands-on lab will show you how to set up Jenkins on Google Kubernetes Engine to help orchestrate your software delivery pipeline.
Rent-a-VM to Process Earthquake Data
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.
Using Ruby on Rails with Cloud SQL for PostgreSQL
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.
Cloud Source Repositories: Qwik Start
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.
Detect Labels, Faces, and Landmarks in Images with the Cloud Vision API
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.
Nichtflüchtigen Speicher erstellen (Aktivitätsverfolgung)
In diesem Lab lernen Sie, wie Sie einen nichtflüchtigen Speicher erstellen und in einer virtuellen Maschine von Google Compute Engine verwenden. Sie lernen auch alles Wichtige über Zonen, Regionen und verschiedene Laufwerktypen.
Cloud Security Scanner: Qwik Start
The Cloud Security Scanner identifies security vulnerabilities in your Google App Engine web applications.
Internal Load Balancer
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.
Introduction to Amazon Elastic Block Store (EBS)
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.
Automating AWS Services with Scripting and the AWS CLI
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.
BigQuery: Qwik Start - Command Line
This hands-on lab shows you how to query public tables and load sample data into BigQuery using the Command Line Interface. Watch the short video <A HREF="https://youtu.be/m0rqccviLNM">Get Meaningful Insights with Google BigQuery</A>.
Blue/Green Deployment Pattern with AWS Elastic Beanstalk
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".
Working with Elastic Load Balancing
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.
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. <br><br> For the lab to function as written, please DO NOT change the auto assigned region.
Entity and Sentiment Analysis with the Natural Language API
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.
Working with Alexa: Build a Fact Skill
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.
Deploy a Ruby on Rails App to App Engine Flexible Environment
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.
Networking 101
In this lab you will learn how to perform basic networking tasks on Google Cloud Platform by setting up a network and 3 subnetworks; and how GCP might differ from an on-premises setup.
Container-Optimized OS: Qwik Start
This hands-on lab shows you how to create Compute Engine instances running Container-Optimized OS.
Bereitstellungen mit Kubernetes Engine verwalten
In diesem Lab üben Sie die Container-Skalierung und -Verwaltung, um besser mit häufigen Szenarien umgehen zu können, in denen mehrere heterogene Bereitstellungen zum Einsatz kommen.
Getting Started with Cloud KMS
In this lab you'll work with advanced features of Google Cloud Security and Privacy APIs, including setting up a secure Cloud Storage bucket, managing keys and encrypted data using Key Management Storage, and viewing Cloud Storage audit logs.
Introduction to Amazon Route 53
The lab provides a basic understanding of Amazon Route 53. It will demonstrate the basic steps required to get started with Route 53, including creating, editing, and deleting simple DNS records within a Hosted Zone (HZ), and creating and testing simple health check and associated failover records. Prerequisites for this lab: basic understanding of IP networking, DNS addressing and host name resolution. Students should also have taken the following three labs at a minimum prior to taking this lab: 1) Introduction to Amazon Elastic Compute Cloud (EC2), 2) Introduction to Simple Storage Service (s3), and 3) Introduction to Amazon Virtual Private Cloud (VPC).For the lab to function as written, please DO NOT change the auto assigned region.
Using Windows Server Storage Spaces with Amazon EBS
This lab leads you through the steps to launch an Amazon Elastic Compute Cloud (EC2) instance using an Amazon Machine Image (AMI), add multiple disks to the instance, and build a tiered storage pool with SSD and magnetic volumes. You will also create a virtual disk using the storage pool. At the end of this lab, you will understand how to build a virtual disk pool with different storage tiers using Amazon EBS and Windows Storage Spaces and see how to connect to Storage Spaces from another machine via iSCSI.
Using Open Data with Amazon S3
This lab demonstrates how to upload data to Amazon S3 and make it available for anyone to access via a web browser. You will learn how to create an Amazon S3 bucket, configure it to host a website, upload objects to it, and use JavaScript to display those objects on a web page. Along the way, you’ll learn some best practices for creating open data. At the end of this lab you will have deployed a simple web site that makes data easy to access and provides basic documentation of the data.
Building Search into your Applications with Amazon CloudSearch
This lab will demonstrate the basics of search engines and Amazon CloudSerach. It will cover how to create a search domain, how to configure it, how to upload data, how to build queries, and how to tune your ranking. You will explore the features of the AWS Management Console and learn how easy it is to get started with Amazon CloudSearch.
Running Distributed TensorFlow on Compute Engine
This lab shows you how to use a distributed configuration of TensorFlow on multiple Compute Engine instances to train a convolutional neural network model using the MNIST dataset.
Deploy a Java EE Application on AWS Elastic Beanstalk Using Docker Containers
This lab demonstrates how to install a Java EE application into JBoss Wildfly, running in a custom Docker Container and then deploy your container to AWS Elastic Beanstalk.
Provision Services with Cloud Launcher
In this hands-on lab you'll learn how to use Cloud Launcher to quickly get started with common operating systems, web frameworks, and databases.
Automated Video Editing with YOU as the Star!
Learn how to auto-detect faces with Amazon Rekognition and automatically create a new video with Amazon Elastic Transcoder.
Running a Node.js Container in Kubernetes with Container Engine
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.
Introduction to Amazon DynamoDB
This lab teaches you about Amazon DynamoDB and walks you through how to create, query, view and delete a table in the AWS Management Console. For a demonstration, go to: https://www.youtube.com/watch?v=ujWV3-m1pLo For the lab to function as written, please DO NOT change the auto assigned region.
Using OpenTSDB to Monitor Time-Series Data on Cloud Platform
In this lab you will learn how to collect, record, and monitor time-series data on Google Cloud Platform (GCP) using OpenTSDB running on Google Kubernetes Engine and Google Cloud Bigtable.
Deployment Manager - Package and Deploy
In this lab you will develop an advanced Deployment Manager template using JINJA and YAML. You will learn how to install and run a Python application on an instance through Deployment Manager.
Introduction to Elastic Load Balancing
This lab takes you through how to automatically distribute incoming web traffic across multiple Amazon EC2 instances by using Elastic Load Balancing. We walk you through the process to create a basic load balancer, configure health checks, assign security groups, and review settings for your load balancer. For a demonstration, go to: https://www.youtube.com/watch?v=oEcEqN8PeeI <br><br> For the lab to function as written, please DO NOT change the auto assigned region.