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Google Cloud Platform for Architects

By : Vitthal Srinivasan, Loonycorn Ravi, Judy Raj

3.1 (12)

Google Cloud Platform for Architects

3.1 (12)

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

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

Free Chapter

The Case for Cloud Computing

The Case for Cloud Computing

Genesis

Why Google Cloud Platform (GCP)?

Autoscaling and autohealing

Capital expenditure (CAPEX) versus operating expenses (OPEX)

Career implications

Summary

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

Summary

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

Summary

GKE, App Engine, and Cloud Functions

GKE, App Engine, and Cloud Functions

GKE

Creating a Kubernetes cluster and deploying a WordPress container

Using the features of GKE

Google App Engine – flexible

Google App Engine – standard

Summary

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

Summary

Relational Databases

Relational Databases

Relational databases, SQL, and schemas

GCP Cloud SQL

Use case – managing replicas

Use case – managing certificates

Use case – operating Cloud SQL through VM instances

Automatic backup and restore

Cloud Spanner

Summary

NoSQL Databases

NoSQL Databases

NoSQL databases

Cloud Bigtable

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

Summary

BigQuery

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

Summary

Identity and Access Management

Identity and Access Management

Resource hierarchy of GCP

Permissions and roles

Summary

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

Summary

Load Balancing

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

Summary

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

Summary

Logging and Monitoring

Logging and Monitoring

Logging

Monitoring

Summary

Infrastructure Automation

Infrastructure Automation

Managed Instance Groups

Cloud deployment manager

Summary

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

Summary

Pricing Considerations

Pricing Considerations

Compute Engine

BigTable

BigQuery

Datastore

Cloud SQL

Google Kubernetes Engine

Pub/Sub

Cloud ML Engine

Stackdriver

Video Intelligence API

Key Management Service – KMS

Vision API

Summary

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!

Summary

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Summary

In this chapter, you learned that as we would expect, the prices of most GCP services vary widely, based on their usage, although the definition of usage varies widely by service. Some services, such as BigTable or Cloud Spanner, are inherently expensive and ought to be used with care. Several GCP services (for example, Cloud SQL and Kubernetes) are built atop GCE VMs, and the choices of VM type can dramatically change how much we pay.

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