Book Image

Google Cloud Platform for Architects

By : Vitthal Srinivasan, Loonycorn Ravi, Judy Raj

Book Image

Google Cloud Platform for Architects

By: Vitthal Srinivasan, Loonycorn Ravi, Judy Raj

Overview of this book

Using a public cloud platform was considered risky a decade ago, and unconventional even just a few years ago. Today, however, use of the public cloud is completely mainstream - the norm, rather than the exception. Several leading technology firms, including Google, have built sophisticated cloud platforms, and are locked in a fierce competition for market share. The main goal of this book is to enable you to get the best out of the GCP, and to use it with confidence and competence. You will learn why cloud architectures take the forms that they do, and this will help you become a skilled high-level cloud architect. You will also learn how individual cloud services are configured and used, so that you are never intimidated at having to build it yourself. You will also learn the right way and the right situation in which to use the important GCP services. By the end of this book, you will be able to make the most out of Google Cloud Platform design.

Preface

Who this book is for

What this book covers

To get the most out of this book

Free Chapter

The Case for Cloud Computing

The Case for Cloud Computing

Why Google Cloud Platform (GCP)?

Autoscaling and autohealing

Capital expenditure (CAPEX) versus operating expenses (OPEX)

Career implications

Introduction to Google Cloud Platform

Introduction to Google Cloud Platform

Global, regional, and zonal resources

Accessing the Google Cloud Platform

Projects and billing

Setting up a GCP account

Using the Cloud Shell

Compute Choices – VMs and the Google Compute Engine

Compute Choices – VMs and the Google Compute Engine

Google Compute Engine – GCE

Persistent disks and local SSDs – block storage for GCE

More on working with GCE VMs

Modifying GCE VMs

GKE, App Engine, and Cloud Functions

GKE, App Engine, and Cloud Functions

Creating a Kubernetes cluster and deploying a WordPress container

Using the features of GKE

Google App Engine – flexible

Google App Engine – standard

Google Cloud Storage – Fishing in a Bucket

Google Cloud Storage – Fishing in a Bucket

Knowing when (and when not) to use GCS

Serving Static Content with GCS Buckets

Storage classes–Regional, multi-regional, nearline, and coldline

Working with GCS buckets

Creating buckets

Transferring data in and out of buckets

Use case – Object Versioning

Use case – object life cycle policies

Use case – restricting access with both ACLs and IAM

Use case – signed and timed URLs

Use case – reacting to object changes

Use case – using customer supplied encryption keys

Use case – auto-syncing folders

Use case – mounting GCS using gcsfuse

Use case – offline ingestion options

Relational Databases

Relational Databases

Relational databases, SQL, and schemas

Use case – managing replicas

Use case – managing certificates

Use case – operating Cloud SQL through VM instances

Automatic backup and restore

NoSQL Databases

NoSQL Databases

NoSQL databases

Creating and operating an HBase table using Cloud Bigtable

Scaling GCP Cloud BigTable

The Google Cloud Datastore

Comparison with traditional databases

Working with Datastore

Full indexing and perfect index

BigQuery

Underlying data representation of BigQuery

BigQuery public datasets

Legacy versus standard SQL

Working with the BigQuery console

Loading data into a table using BigQuery

Deleting datasets

Working with BigQuery using CLI

BigQuery pricing

Analyzing financial time series with BigQuery

Identity and Access Management

Identity and Access Management

Resource hierarchy of GCP

Permissions and roles

Managing Hadoop with Dataproc

Managing Hadoop with Dataproc

Hadoop and Spark

Hadoop on the cloud

Google Cloud Dataproc

Compute options for Dataproc

Working with Dataproc

Load Balancing

Why load balancers matter now

Taxonomy of GCP load balancers

HTTP(S) load balancing

Configuring HTTP(S) load balancing

Configuring Internal Load Balancing

Other load balancing

Networking in GCP

Networking in GCP

Why GCP's networking model is unique

VPC networks and subnets

The default VPC

Internal and external IP addresses

VPN and cloud router

Working with VPCs

Working with custom subnets

Working with firewall rules

Logging and Monitoring

Logging and Monitoring

Infrastructure Automation

Infrastructure Automation

Managed Instance Groups

Cloud deployment manager

Security on the GCP

Security on the GCP

Security features at Google and on the GCP

Google-provided tools and options for security

Some security best practices

BeyondCorp – Identity-Aware Proxy

Pricing Considerations

Pricing Considerations

Google Kubernetes Engine

Cloud ML Engine

Video Intelligence API

Key Management Service – KMS

Effective Use of the GCP

Effective Use of the GCP

Eat the Kubernetes frog

Careful that you don't get nickel-and-dimed

Pay for what you allocate not what you use

Make friends with the gsuite admins

Try to find reasons to use network peering

Understand how sustained use discounts work

Read the fine print on GCS pricing

Use BigQuery unless you have a specific reason not to

Use pre-emptible instances in your Dataproc clusters

Keep your Dataproc clusters stateless

Understand the unified architecture for batch and stream

Understand the main choices for ML applications

Understand the differences between snapshots and images

Don't be Milton!

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Summary

Having read this chapter, you should now be familiar with the concept of relational databases on the cloud, transactions, and ACID-support. You will have some clarity around how Cloud SQL can be used to set up MySQL or PostgreSQL databases on the GCP and some of the considerations in choosing between Cloud SQL and Cloud Spanner. You will also be familiar with the intricacies of replicas and backups on Cloud SQL. Finally, we have also discussed how Cloud Spanner represents a cutting-edge, Google-proprietary RDBMS that can scale horizontally to support pretty much any dataset size and level of QPS (queries-per-second).