Cloud Analytics with Google Cloud Platform

Combine the power of analytics and cloud computing for faster and efficient insights
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Cloud Analytics with Google Cloud Platform

Sanket Thodge
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Combine the power of analytics and cloud computing for faster and efficient insights
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Book Details

ISBN 139781788839686
Paperback282 pages

Book Description

With 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 implementation

Table of Contents

Chapter 1: Introducing Cloud Analytics
What is cloud computing?
Major benefits of cloud computing
Cloud computing deployment models
Types of cloud computing services
How PaaS, IaaS, and SaaS are separated at service level
Emerging cloud technologies and services
Risks and challenges with the cloud
What is cloud analytics?
10 major cloud vendors in the world
Google Cloud Platform introduction—video
Summary
Chapter 2: Design and Business Considerations
A bit more about cloud computing and migration
Parameters before adopting cloud strategy
Prerequisites for an application to be moved to the cloud
Infrastructure contemplation for cloud
Available deployment models while moving to cloud
Cloud migration checklist
Architecture of a cloud computing ecosystem
Applications of cloud computing
Preparing a plan for moving to cloud computing
Technologies utilized by cloud computing
Summary
Chapter 3: GCP 10,000 Feet Above – A High-Level Understanding of GCP
Different services offered by typical cloud vendors
Understanding cloud categories
Overview to Google Cloud Platform Console—Video
Summary
Chapter 4: Ingestion and Storing – Bring the Data and Capture It
Cloud Dataflow
Cloud Pub/Sub
Cloud storage
Cloud SQL
Cloud BigTable
Cloud Spanner
Cloud Datastore
Persistent disks
Summary
Chapter 5: Processing and Visualizing – Close Encounter
Google BigQuery
Cloud Dataproc
Google Cloud Datalab
Google Data Studio
Google Compute Engine
Google App Engine
Google Container Engine
Google Cloud Functions
Summary
Chapter 6: Machine Learning, Deep Learning, and AI on GCP
Artificial intelligence
Machine learning
Google Cloud Platform
Google Cloud Machine Learning Engine
Cloud Natural Language API
TensorFlow
Cloud Speech API
Cloud Translation API
Cloud Vision API
Cloud Video Intelligence
Dialogflow
AutoML
Summary
Chapter 7: Guidance on Google Cloud Platform Certification
Professional Cloud Architect Certification
Professional Data Engineer Certification
When to use What
Summary
Chapter 8: Business Use Cases
Smart Parking Solution by Mark N Park
DSS for web mining recommendation using TensorFlow
Building a Data Lake for a Telecom Client
Summary
Chapter 9: Introduction to AWS and Azure
Amazon Web Services
Microsoft Azure
Head to head of Google Cloud Platform with Amazon Web Services and Microsoft Azure
Summary

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 GCP

Authors

Table of Contents

Chapter 1: Introducing Cloud Analytics
What is cloud computing?
Major benefits of cloud computing
Cloud computing deployment models
Types of cloud computing services
How PaaS, IaaS, and SaaS are separated at service level
Emerging cloud technologies and services
Risks and challenges with the cloud
What is cloud analytics?
10 major cloud vendors in the world
Google Cloud Platform introduction—video
Summary
Chapter 2: Design and Business Considerations
A bit more about cloud computing and migration
Parameters before adopting cloud strategy
Prerequisites for an application to be moved to the cloud
Infrastructure contemplation for cloud
Available deployment models while moving to cloud
Cloud migration checklist
Architecture of a cloud computing ecosystem
Applications of cloud computing
Preparing a plan for moving to cloud computing
Technologies utilized by cloud computing
Summary
Chapter 3: GCP 10,000 Feet Above – A High-Level Understanding of GCP
Different services offered by typical cloud vendors
Understanding cloud categories
Overview to Google Cloud Platform Console—Video
Summary
Chapter 4: Ingestion and Storing – Bring the Data and Capture It
Cloud Dataflow
Cloud Pub/Sub
Cloud storage
Cloud SQL
Cloud BigTable
Cloud Spanner
Cloud Datastore
Persistent disks
Summary
Chapter 5: Processing and Visualizing – Close Encounter
Google BigQuery
Cloud Dataproc
Google Cloud Datalab
Google Data Studio
Google Compute Engine
Google App Engine
Google Container Engine
Google Cloud Functions
Summary
Chapter 6: Machine Learning, Deep Learning, and AI on GCP
Artificial intelligence
Machine learning
Google Cloud Platform
Google Cloud Machine Learning Engine
Cloud Natural Language API
TensorFlow
Cloud Speech API
Cloud Translation API
Cloud Vision API
Cloud Video Intelligence
Dialogflow
AutoML
Summary
Chapter 7: Guidance on Google Cloud Platform Certification
Professional Cloud Architect Certification
Professional Data Engineer Certification
When to use What
Summary
Chapter 8: Business Use Cases
Smart Parking Solution by Mark N Park
DSS for web mining recommendation using TensorFlow
Building a Data Lake for a Telecom Client
Summary
Chapter 9: Introduction to AWS and Azure
Amazon Web Services
Microsoft Azure
Head to head of Google Cloud Platform with Amazon Web Services and Microsoft Azure
Summary

Book Details

ISBN 139781788839686
Paperback282 pages
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