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Google Cloud Platform Cookbook
Google Cloud Platform Cookbook

Google Cloud Platform Cookbook: Implement, deploy, maintain, and migrate applications on Google Cloud Platform

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Google Cloud Platform Cookbook

Storage and Databases

In this chapter, we will cover:

  • Hosting a static application on Google Cloud Storage
  • Image resizing using Google Cloud Storage and Cloud Functions
  • Migrating a MariaDB to Cloud Spanner
  • Loading temperature data to Cloud Bigtable and BigQuery-ing it
  • Storage metadata in Google Cloud Datastore

Introduction

In this chapter, we'll discover some use cases for Google's Cloud Storage offerings. Storage is one of the main pillars of any workload to be hosted on Google Cloud Platform (GCP). GCP offers various options for data to be stored. The nature of the data, nature of the workload, scalability and latency needs to dictate the choice of the storage. We'll cover a few examples on Google Storage, Cloud Spanner, Cloud Bigtable, BigQuery and Cloud Datastore.

In addition to Node.js, we'll use Python for a few recipes in this chapter.

The following page is a wonderful guide for choosing a storage option for your particular workload https://cloud.google.com/storage-options/.

Hosting a static application using Google Cloud Storage

Google Storage offers a way of hosting static web applications using buckets. All client-side technologies (HTML, CSS, and JavaScript) can be hosted in a bucket. This option is very inexpensive to host and can scale to a great extent. The costs incurred will be on the assets stored, outbound network traffic and retrieval from storage. We'll use Hugo (https://gohugo.io), a static website generator and host the generated code in a storage bucket. In addition to hosting a static website, the static contents (CSS, JavaScript, and images) of a dynamic website hosted on Google Storage is also a widely accepted use case.

Getting ready

The following are the initial setup...

Image resizing using Google Cloud Storage and Cloud Functions

Google Storage is extensively used to store static images to be served by different platforms. One of the needs of the modern-day digital world is to have images created for different form factors—desktop, mobile and tablet screens. In this recipe, we'll develop a Cloud Function to resize the images uploaded to a bucket:

Getting ready

The following are the initial setup verification steps to be carried out before the recipe can be executed:

  1. Create or select a GCP project
  2. Enable billing and enable the default APIs (some APIs such as BigQuery, storage, monitoring, and a few others are enabled automatically)
  3. Verify that Node.js and Google Cloud SDK are...

Migrating a MariaDB to Cloud Spanner

Google Cloud Spanner is a relational database service which is highly consistent, available and scalable horizontally. It is a fully managed service suitable for OLTP workloads and low-latency/high-throughput environments. In this recipe, we'll understand the features of Spanner in relation to a popular RDBMS, MariaDB. We'll take a simple MariaDB database and move the schema and the data to Cloud Spanner. At the time of writing, there is no automated import/export tool for Cloud Spanner, the manual method gives us a glimpse of the way Spanner is built and its strengths/weaknesses. Firstly, we'll export the data from our MariaDB database using the mysqldump utility. Then, we'll create the database and tables via the Google Console. Finally, we'll insert the data into the table via a Python script.

...

Loading temperature data to Cloud Bigtable and BigQuery-ing it

Google Bigtable is a fully managed NoSQL service with a focus on low latency and high throughput. It is well suited for storing data for large analytical processing, operation data from IoT devices, other time-series data, and so on. Cloud Bigtable is designed for performance, for huge databases (~1 TB) and it optimizes your data over time. In this recipe, we'll have a look at a small database generated by an IoT temperature sensor. We'll write a Python script to simulate a sensor sending temperature data located in a garden to the  Bigtable. Then, we'll use BigQuery to connect to the Bigtable database to query and make meaning out of the data store.

Getting ready

...

Storage metadata in Google Cloud Datastore

Google Datastore is a highly scalable, NoSQL document database similar to Amazon's DynamoDB and MongoDB. Individual data objects in the Datastore are called entities. An entity has one or more properties and a category of objects is called a kind. Each object is uniquely identified by a key. In this recipe, we would simulate a storage drive environment. Employees of a company are given the ability to upload files to Google Storage. The metadata is stored in Cloud Datastore to avoid overhead to the Storage API and for efficiency. The file uploaded to the Storage follows this file format:

<5-digit hash>/<[year][month][day][hour][minute][second]>/<Employee ID>/<file name>

After the data is uploaded to Storage, the metadata collected should enable us to query:

  • List all objects uploaded by an employee
  • List...
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Key benefits

  • • Implement Google Cloud services in your organization
  • • Leverage Google Cloud components to secure your organization’s data
  • • A recipe-based guide that promises hands-on experience in deploying a highly scalable and available environment

Description

Google Cloud Platform is a cloud computing platform that offers products and services to host applications using state-of-the art infrastructure and technology. You can build and host applications and websites, store data, and analyze data on Google's scalable infrastructure. This book follows a recipe-based approach, giving you hands-on experience to make the most of Google Cloud services. This book starts with practical recipes that explain how to utilize Google Cloud's common services. Then, you'll see how to make full use of Google Cloud components such as networking, security, management, and developer tools. Next, we'll deep dive into implementing core Google Cloud services into your organization, with practical recipes on App Engine, Compute Engine, Cloud Functions, virtual networks, and Cloud Storage. Later, we'll provide recipes on implementing authentication and security, Cloud APIs, command-line management, deployment management, and the Cloud SDK. Finally, we'll cover administration and troubleshooting tasks on applications with Compute services and we'll show how to monitor your organization's efficiency with best practices. By the end of this book, you'll have an overall understanding and hands-on implementation of Google Cloud services in your organization with ease.

Who is this book for?

This book is for IT professionals, engineers, and developers looking at implementing Google Cloud in their organizations. Administrators and architects planning to make their organization more efficient with Google Cloud will also find this book useful. Basic understanding of Cloud services and the Google Cloud platform is necessary.

What you will learn

  • • Host a Python application on Google Compute Engine
  • • Host an application using Google Cloud Functions
  • • Migrate a MySQL DB to Cloud Spanner
  • • Configure a network for a highly available application on GCP
  • • Learn simple image processing using Storage and Cloud Functions
  • • Automate security checks using Policy Scanner
  • • Understand tools for monitoring a production environment in GCP
  • • Learn to manage multiple projects using service accounts
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Publication date, Length, Edition, Language, ISBN-13
Publication date : Apr 16, 2018
Length: 280 pages
Edition : 1st
Language : English
ISBN-13 : 9781788291996
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Languages :

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Estimated delivery fee Deliver to Ukraine

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Product Details

Publication date : Apr 16, 2018
Length: 280 pages
Edition : 1st
Language : English
ISBN-13 : 9781788291996
Vendor :
Google
Languages :

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Table of Contents

8 Chapters
Compute Chevron down icon Chevron up icon
Storage and Databases Chevron down icon Chevron up icon
Networking Chevron down icon Chevron up icon
Security Chevron down icon Chevron up icon
Machine Learning and Big Data Chevron down icon Chevron up icon
Management Tools Chevron down icon Chevron up icon
Best Practices Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
(4 Ratings)
5 star 25%
4 star 0%
3 star 25%
2 star 50%
1 star 0%
avarma Nov 15, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Hard to find content, especially if you are working on GCP. Don't get fooled by the size - it is small, yet very informative. Although, at a high level, you may know how to use a cloud function to automatically ingest and resize an image, it helps to have a walkthrough. Lots of other gems - especially on the networking and the devops pieces. Great job by the author!The kindle formatting may be bad - the hardcopy worked for me.
Amazon Verified review Amazon
Dheeraj Khanna Oct 19, 2018
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
This book (kindle version as I am hsing it on Kindle) very hard to follow for examples. One example takes more than 3-4 days to complete and majority of the time you keep troubleshooting errors to make the code working. There is no support to get it going to learn . The kestone example given does not work at all and few of the steps are missing.It is a complete waste of money. I dont recomend to buy this.
Amazon Verified review Amazon
Gerardo Santovena May 24, 2018
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
The content is good but the format is very bad, many exercises can't be read on Kindle at all.
Amazon Verified review Amazon
George A. Quintas May 21, 2018
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
Not compatible with Kindle Cloud Reader. Are you testing@Packt> are you testing your online contents on all available online readers out there? This is the second kindle book that I bought under your name?I will ask for a refund but I will buy the paper version.
Amazon Verified review Amazon
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