Google Cloud Platform Fundamentals: Core Infrastructure

In this course, Google Cloud Platform Fundamentals: Core Infrastructure, you will learn about virtual machines and networks in the cloud, storage in the cloud. You’ll also learn about developing, deploying, and monitoring in the cloud.
Course info
Rating
(10)
Level
Intermediate
Updated
Jan 17, 2019
Duration
2h 57m
Table of contents
Introduction to Google Cloud Platform
Getting Started with Google Cloud Platform
Virtual Machines and Networks in the Cloud
Storage in the Cloud
Containers in the Cloud
Applications in the Cloud
Developing, Deploying, and Monitoring in the Cloud
Big Data and Machine Learning in the Cloud
Summary and Review
Description
Course info
Rating
(10)
Level
Intermediate
Updated
Jan 17, 2019
Duration
2h 57m
Description

This class provides an overview of Google Cloud Platform services. Through a combination of presentations, demos, and hands-on labs, participants learn the value of Google Cloud Platform and how its services work together. Participants also get foundational skills in how to use GCP.

About the author
About the author

Build, innovate, and scale with Google Cloud Platform.

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Section Introduction Transcripts
Section Introduction Transcripts

Introduction to Google Cloud Platform
Hi, I'm Brian Rice from the Google Cloud training team. In this course, my colleagues and I are going to introduce you to Google Cloud Platform. GCP offers four main kinds of services: compute, storage, big data, and machine learning. This course focuses mostly on the first two, together with the topic of networking. After all, you can't use resources in the cloud without cloud networking. The cloud is a great home for your applications and your data, because using it frees you from a lot of overhead chores, and the Google Cloud gives you reasonably-priced access to the same planet-scale infrastructure that Google runs on. What exactly is Google Cloud Platform, how is it organized, and what makes it unique? In this module, we'll orient you to the basics.

Virtual Machines and Networks in the Cloud
Of all the ways you can run workloads in the cloud, virtual machines may be the most familiar. Compute Engine lets you run virtual machines on Google's global infrastructure. In this module, we'll learn how Google Compute Engine works, with a focus on Google virtual networking. One of the nice things about virtual machines is that they have the power and generality of a full-fledged operating system in each. You configure a virtual machine much like you build out a physical server, by specifying its amounts of CPU power and memory, its amounts and types of storage, and its operating system. You can flexibly reconfigure them. And, a VM running on Google's cloud has unmatched worldwide network connectivity.

Storage in the Cloud
Every application needs to store data, maybe media to be streamed or sensor data from devices or customer account balances, or maybe the fact that my Dragonite has more than 2600 CP. Different applications and workloads require different storage database solutions. You already know that you can store data on your VM's persistent disk. Google Cloud Platform has other storage options to meet your needs for structured, unstructured, transactional, and relational data. In this module, I'll tell you about the core storage options, Cloud Storage, Cloud SQL, Cloud Spanner, Cloud Datastore, and Google Bigtable. Depending on your application, you might want to use one or several of these services to get the job done.

Containers in the Cloud
(Music) Welcome to this module on containers and Google Kubernetes Engine. We've already discussed Compute Engine, which is GCP's infrastructure as a Service offering with access to servers, file systems, and networking, and App Engine, which GCP's PaaS offering. Now, I'm going to introduce you to containers and Kubernetes Engine, which is a hybrid that conceptually sits between the two with benefits from both. I'll describe why you want to use containers and how to manage them in Kubernetes Engine.

Applications in the Cloud
So, we've discussed two GCP products that provide the compute infrastructure for applications, Compute Engine and Kubernetes Engine. What these have in common is that you choose the infrastructure in which your application runs based on virtual machines for Compute Engine and containers for Kubernetes Engine. But what if you don't want to focus on the infrastructure at all, you just want to focus on your code? That's what App Engine is for. I'll tell you more about it in this module. Let's start with PaaS. Recall that a PaaS is a Platform as a Service. The App Engine platform manages the hardware and networking infrastructure required to run your code. To deploy an application on App Engine, you just hand App Engine your code and the App Engine service takes care of the rest. App Engine provides you with the built-in services that many web applications need, NoSQL databases, in-memory caching, load balancing, health checks, logging, and a way to authenticate users. You code your application to take advantage of these services, and App Engine provides them. App Engine will scale your application automatically in response to the amount of traffic it receives, so you only pay for those resources you use. There are no servers for you to provision or maintain. That's why App Engine is especially suited for applications where the workload is highly variable or unpredictable, like web applications and mobile back end. App Engine offers two environments, standard and flexible. I'll explain what each is and how to choose.

Big Data and Machine Learning in the Cloud
Google believes that in the future every company will be a data company because making the fastest and best use of data is a critical source of competitive advantage. Google Cloud provides a way for everybody to take advantage of Google's investments in infrastructure and data processing innovation. Google Cloud has automated out the complexity of building and maintaining data and analytic systems. In this module, I'll tell you about Google's technologies for getting the most out of data fastest. Whether it's real-time analytics or machine learning, these tools are intended to be simple and practical for you to embed in your applications so that you can put data into the hands of your domain experts and get insights faster.