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Google

Google Cloud Solutions I: Scaling Your Infrastructure

Google via Qwiklabs

Overview

In this advanced-level quest, you will learn how you to harness serious Google Cloud power and infrastructure. The hands-on labs will give you use cases and you will be tasked with implementing scaling practices utilized by Google's very own Solutions Architecture team. From developing enterprise grade load balancing and autoscaling, to building continuous delivery pipelines, Google Cloud Solutions I: Scaling your Infrastructure will teach you best practices for taking your Google Cloud projects to the next level.

Syllabus

  • Autoscaling an Instance Group with Custom Cloud Monitoring Metrics
    • This lab describes how to deploy an autoscaling Compute Engine instance group that is automatically scaled using a custom Cloud monitoring metric
  • 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.
  • Continuous Delivery Pipelines with Spinnaker and Kubernetes Engine
    • Create a Kubernetes Engine cluster, deploy an application, and use Spinnaker to continuously deploy the application when changes are made to the application.
  • Deploying a Fault-Tolerant Microsoft Active Directory Environment
    • In this Qwiklab, you set up a redundant pair of Windows Domain Controllers (DC) with AD using a new Virtual Private Cloud (VPC) network and multiple subnets on Google Cloud Platform (GCP).
  • Deploying Memcached on Kubernetes Engine
    • In this lab you'll learn how to deploy a cluster of distributed Memcached servers on Kubernetes Engine using Kubernetes, Helm, and Mcrouter.
  • Running Dedicated Game Servers in Google Kubernetes Engine
    • This lab will show you how to use an expandable architecture for running a real-time, session-based multiplayer dedicated game server using Kubernetes on Google Container Engine.
  • Distributed Load Testing Using Kubernetes
    • Lab has instructions to conduct distributed load testing with Kubernetes, which includes a sample web application, Docker image, and Kubernetes deployments/services.

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