Cloud Analytics with Google Cloud Platform

Sanket Thodge

• Publisher: Packt Publishing
• Year: 2018
• eBook ISBN: 9781788838597
• Edition: 1
• Topic: Business
• Subtopic: Business Intelligence
• Index: Business

Combine the power of analytics and cloud computing for faster and efficient insightsAbout This Book• Master the concept of analytics on the cloud: and how organizations are using it• Learn the design considerations and while applying a cloud analytics solution• Design an end-to-end analytics pipeline on the cloudWho This Book Is ForThis book is targeted at CIOs, CTOs, and even analytics professionals looking for various alternatives to implement their analytics pipeline on the cloud. Data professionals looking to get started with cloud-based analytics will also find this book useful. Some basic exposure to cloud platforms such as GCP will be helpful, but not mandatory.What You Will Learn• Explore the basics of cloud analytics and the major cloud solutions • Learn how organizations are using cloud analytics to improve the ROI• Explore the design considerations while adopting cloud services • Work with the ingestion and storage tools of GCP such as Cloud Pub/Sub• Process your data with tools such as Cloud Dataproc, BigQuery, etc• Over 70 GCP tools to build an analytics engine for cloud analytics• Implement machine learning and other AI techniques on GCPIn DetailWith the ongoing data explosion, more and more organizations all over the world are slowly migrating their infrastructure to the cloud. These cloud platforms also provide their distinct analytics services to help you get faster insights from your data. This book will give you an introduction to the concept of analytics on the cloud, and the different cloud services popularly used for processing and analyzing data. If you're planning to adopt the cloud analytics model for your business, this book will help you understand the design and business considerations to be kept in mind, and choose the best tools and alternatives for analytics, based on your requirements. The chapters in this book will take you through the 70+ services available in Google Cloud Platform and their implementation for practical purposes. From ingestion to processing your data, this book contains best practices on building an end-to-end analytics pipeline on the cloud by leveraging popular concepts such as machine learning and deep learning.By the end of this book, you will have a better understanding of cloud analytics as a concept as well as a practical know-how of its implementationStyle and approachComprehensive guide with a perfect blend of theory, examples, and implementation of real-world use-cases

GCP 10,000 Feet Above – A High-Level Understanding of GCP

In this chapter, we are going to dive into the Google Cloud Platform and all its components. This chapter will give a better purpose of GCP tools. We are going to categorize all the GCP tools and then understand each of them in brief. Along with gaining complete understanding of the services, we will also gain knowledge of differences between the tools, such as two tools that might be serving the same purpose. Then I will be explaining the major differences and use cases that you will using that tool in.

The following this chapter, we will have a deep dive into all the major services that GCP provides. For now, this will be the first chapter completely focused on Google Cloud Platform.

As we understand the utility of these components, we will gain insight into a complete GCP ecosystem.

We will cover the following topics in this chapter:

• Different services offered by typical cloud vendors
• Understanding cloud categories

Different services offered by typical cloud vendors

Before starting with GCP, let's try to understand the service categorization by major competitors of GCP, that is, Amazon Web Services (AWS) and Microsoft Azure.

First let's get started with AWS. AWS has 21 broad categories, namely Compute, Storage, Database, Migration, Network and Content Delivery, Developer Tools, Management Tools, Media Services, Machine Learning, Analytics, Security Identity and Compliance, Mobile Service, AR and VR, Application Integration, Customer Engagement, Business Productivity, Desktop and App Streaming, Internet of Things, Game Development, and Software and Cost Management.

Similarly, Azure has categorized services into 13 parts, namely Compute, Networking, Storage, Web and Mobile, Containers, Databases, Analytics, AI and Machine Learning, Internet of Things, Enterprise Integration, Security and Identity, Developer Tools, and Management Tools.

If you have a close look at the naming mechanism of both the vendors, you will find at least eight categories having the same or very similar names.

Now, we are going to see what GCP has to offer us with this. GCP has close to 11 different categories, and they are the following:

• Compute
• Storage and Databases
• Networking
• Big Data
• Data Transfer
• Cloud AI
• API Platforms and Ecosystems
• Identity and Security
• Management Tools
• Developer Tools
• Internet of Things

The picture is very clear now—almost all cloud vendors have the same or very similar terminologies to all the services they provide. The reason for this? To make it more appealing and more accessible to the consumer. If we as consumers are jumping from one vendor to another, the naming should not be a constraint in terms of generic categories at least.

But if we try to explore the specific names of the services then there is a huge gap with naming conventions between all the vendors. And that is legally required.

So now that we know broadly the different categories of GCP, we will start exploring each of them and all the services they comprise.

Understanding cloud categories

Now, as we have seen the 11 broad categories of the services in GCP, we will try to understand each of them. The following table specifies the different features of these categories:

Cloud category	Features
Compute	High-performance, scalable VMs Build apps, scale automatically Automated container management Private docker container images Serverless applications on Google's infrastructure
Storage and databases	Managed databases Object storage Archival
Networking	Worldwide autoscaling and load balancing Manage networking for your resources Highly available global DNS network Fast, high availability interconnect Content delivery network
Big data	Analytics data warehouse Batch and stream data processing Managed Hadoop and Spark Powerful data exploration Tell great data stories to support better business decisions Intelligent data preparation Scalable event ingestion and messaging middleware
Data transfer	Online transfer Cloud storage transfer service Transfer appliance beta BigQuery data transfer service
Cloud AI	Train custom machine learning models Hardware optimized for machine learning Large scale machine learning service Powerful job search and discovery Create...

Table of contents

• Title Page
• Copyright and Credits
• Packt Upsell
• Foreword
• Contributors
• Preface
• Introducing Cloud Analytics
• Design and Business Considerations
• GCP 10,000 Feet Above – A High-Level Understanding of GCP
• Ingestion and Storing – Bring the Data and Capture It
• Processing and Visualizing – Close Encounter
• Machine Learning, Deep Learning, and AI on GCP
• Guidance on Google Cloud Platform Certification
• Business Use Cases
• Introduction to AWS and Azure
• Other Books You May Enjoy