Cloud Analytics with Google Cloud Platform电子书

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Cloud Analytics with Google Cloud Platform

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Foreword

Contributors

About the author

About the reviewers

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Preface

Who this book is for

What this book covers

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Conventions used

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Introducing Cloud Analytics

What is cloud computing?

Major benefits of cloud computing

Cloud computing deployment models

Private cloud

Public cloud

Hybrid cloud

Differences between the private cloud, hybrid cloud, and public cloud models

Types of cloud computing services

Infrastructure as a Service

PaaS

SaaS

Differences between SaaS, PaaS, and IaaS

How PaaS, IaaS, and SaaS are separated at service level

Emerging cloud technologies and services

Different ways to secure the cloud

Risks and challenges with the cloud

What is cloud analytics?

10 major cloud vendors in the world

Google Cloud Platform introduction—video

Summary

Design and Business Considerations

A bit more about cloud computing and migration

Parameters before adopting cloud strategy

Developing and changing business needs

Security of data

Organizational requests on the in-house IT team

Cloud deployment models—public cloud, private cloud, and hybrid cloud

Legally binding responsibilities

Prerequisites for an application to be moved to the cloud

Performance

Portability

Simplifying cloud migration with virtualization

Infrastructure contemplation for cloud

Available deployment models while moving to cloud

IaaS

Advantages of IaaS

Disadvantages of IaaS

PaaS

Advantages of PaaS

Disadvantages of PaaS

SaaS

Cloud migration checklist

Architecture of a cloud computing ecosystem

Infrastructure for cloud computing

Constrictions on cloud infrastructure

Applications of cloud computing

Preparing a plan for moving to cloud computing

Methodology stage

Cloud computing proposal worth

Cloud computing methodology planning

Planning stage

Distribution stage

Making arrangements for a multi-provider methodology

Making a multi-provider design tactic

Technologies utilized by cloud computing

Grid computing

Service-oriented architecture

Virtualization

Utility computing

Summary

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

Different services offered by typical cloud vendors

Understanding cloud categories

Compute

Compute Engine

App engine

Kubernetes engine

Cloud function

Storage and databases

Cloud storage

Cloud SQL

Cloud Bigtable

Cloud Spanner

Cloud Datastore

Persistent Disk

Networking

Virtual Private Cloud

Cloud load balancing

Cloud CDN

Cloud interconnect

Cloud DNS

Network Service Tiers ALPHA

Big Data

BigQuery

Cloud Dataflow

Cloud Dataproc

Cloud Datalab

Cloud Dataprep BETA

Cloud Pub/Sub

Genomics

Google Data Studio BETA

Data transfer

Google Transfer appliance

Cloud Storage Transfer Service

Google BigQuery Data Transfer Service

Cloud AI

Cloud AutoML alpha

Cloud TPU beta

Cloud machine learning engine

Cloud job discovery private beta

Dialogflow enterprise edition beta

Cloud natural language

Cloud speech API, translation API, and vision API

Cloud video intelligence

Internet of Things

Cloud IoT Core beta

Management tools

Stackdriver overview

Monitoring, logging, error reporting, trace, and debugger

Cloud deployment manager

Cloud console

Cloud shell

Cloud console mobile app

Developer tools

Cloud SDK

Container Registry

Container builder

Cloud source repositories

Cloud tools for IntelliJ, Visual Studio, and Eclipse

Cloud tools for Powershell

Overview to Google Cloud Platform Console—Video

Summary

Ingestion and Storing – Bring the Data and Capture It

Cloud Dataflow

When to use

Special features

The Dataflow programming model

Pipelines

PCollection (data)

Transforms

I/O sources and sinks

Pipeline example

How to use Cloud Dataflow - Video

Cloud Pub/Sub

When to use

Special feature

Overview

Using the gcloud command-line tool

How to use Cloud Pub Sub - Video

Cloud storage

When to use it

Special feature

Cloud storage classes

Multi-regional storage

Regional storage

Nearline storage

Coldline storage

Standard storage

Working with storages

How to use Cloud Storage - Video

Cloud SQL

When to use

Special feature

Database engine (MySQL)

Database engine (PostgreSQL)

How to use Cloud SQL - Video

Cloud BigTable

When to use it

Special features

Cloud BigTable storage model

Cloud Bigtable architecture

Load balancing

How to use Cloud Bigtable—Video

Cloud Spanner

When to use

Special features

Schema and data model

Instances

How to use Cloud Spanner - Video

Cloud Datastore

When to use

Special features

How to use Cloud Datastore - Video

Persistent disks

When to use

Special feature

Standard hard disk drive

Solid-state drives

Persistent disk

How to attache Persistent Store to VM - Video

Summary

Processing and Visualizing – Close Encounter

Google BigQuery

Storing data in BigQuery

Features of BigQuery

Choosing a data ingestion format

Schema type of the data

External limitations

Embedded newlines

Supported data formats

Google Cloud Storage

Readable data source

Use case

How to use Google BigQuery - Video

Cloud Dataproc

When to use it

Features of Dataproc

Super-fast to build the cluster

Low cost

Easily integrated with other components

Available versions and supported components of Cloud DataProc

Accessibility of Google Cloud Dataproc

Placement of Dataproc

Dataflow versus Dataproc

Pricing

How to use Cloud Dataproc - Video

Google Cloud Datalab

Features of Cloud DataLab

Multi-language support

Integration with multiple Google services

Interactive data visualization

Machine learning

Use case

How to use Google Cloud Datalab - Video

Google Data Studio

Features of Data Studio

Data connections

Data visualization and customization

Usability

Data transformation

Sharing and collaboration

Report templates

Report customization

The flow of Data Studio

How to use Google Data Studio - Video

Google Compute Engine

Features

Advantages of Compute Engine

Batch processing

Predefined machine types

Persistent disks

Linux and Windows support

Per-second billing

Types of Compute Engine

Quickstart VM

Custom VM

Preemptible VM

Use case

How to use Google Compute Engine - Video

Google App Engine

Characteristics of flexible and standard environments

Google AppEngine architecture

Features

Multiple language support

Application versioning

Fully managed

Application security

Traffic splitting

Use case

How to use Google App Engine - Video

Google Container Engine

Container cluster architecture

Cluster master

Cluster master and the Kubernetes API

Master and node interaction

Nodes

Node machine type

How to use Google Container Engine - Video

Google Cloud Functions

Connecting and extending cloud services

Functions are serverless

Use cases

IoT

Data processing ETL

Mobile backend

How to use Google Cloud Functions - Video

Summary

Machine Learning, Deep Learning, and AI on GCP

Artificial intelligence

Machine learning

Google Cloud Platform

Google Cloud Machine Learning Engine

Pricing

Cloud Natural Language API

Use Cases

Using the goodbooks data set from GitHub

Using GCP services list and classify text based on categories

State choice management

How to use Natural Language API - Video

TensorFlow

Use case—text summarization

Cloud Speech API

How to use Speech API - Video

Cloud Translation API

Use cases

Rule-based Machine Translation

Local tissue response to injury and trauma

How to use Translation API - Video

Cloud Vision API

Use cases

Image detection using Android or iOS mobile device

Retinal Image Analysis – ophthalmology

How to use Vision API - Video

Cloud Video Intelligence

Dialogflow

Use cases

Interactive Voice Response System customer service

Checkout free shopping

AutoML

Use case – Listening to music by fingerprinting

Summary

Guidance on Google Cloud Platform Certification

Professional Cloud Architect Certification

Topics for cloud architect certification

Cloud virtual network

Google Compute Engine

Cloud IAM

Data Storage Services

Resource management and resource monitoring

Interconnecting network and load balancing

Autoscaling

Infrastructure automation with Cloud API and Deployment Manager

Managed services

Application infra services

Application development services

Containers

Job role description

Certification preparation

Sample questions

Use cases

Professional Data Engineer Certification

Topics for Cloud Data Engineer Certification

BigQuery

Dataflow

Dataproc

Machine Learning API and TensorFlow

Stream Pipeline, Streaming Analytics, and Dashboards

Job role description

Certification preparation

Sample questions

Use cases

When to use What

Choosing Cloud Storage

Choosing Cloud SQL

Choosing Cloud Spanner

Choosing DataStore

Choosing BigTable

Choosing right data storage

Dataproc versus Dataflow

Data Peering versus Carrier Peering versus IPSec VPN versus Dedicated Interconnect

Summary

Business Use Cases

Smart Parking Solution by Mark N Park

Abstract

Introduction

Problems

Brainstorming

Collection of sensor data in real time

Updating the right dataset/database

Storing periodic data

Transmitting the data to the end user

Reports and dashboard output required

Scaling infrastructure

Services

Architecture

Conclusion

DSS for web mining recommendation using TensorFlow

Abstract

Introduction

Problems

Brainstorming

Internet bandwidth

Local systems or mobile hardware configuration

Collection of data in real time

Updating the right database

Storing periodic data

Extracting the data to the end user

Report generation as per requirements of the end user

Scaling of infrastructure

Services

Architecture

Advantages of using TensorFlow

Limitations of TensorFlow

Conclusion

Building a Data Lake for a Telecom Client

Abstract

Introduction

Problems

Brainstorming

Challenges from phase 1

Identify source type (Batch or RDBMS or Stream)

Logging was carried out and logs were created for every file that was covered

RDBMS import to create files automatically for MySQL and Postgres

Ingesting live data into GCP

Different destinations for all the data sources

Code repository

Challenges from phase 2

Building Hadoop cluster

Data ingestion prioritization and then ingestion

Building strict policies between Data Lake and Hadoop cluster users

Maintaining high availability, enabled load balancer, auto scaled, and secured cluster

Maintaining cluster health

Alpha phase is bringing data from the Data Lake into an application cluster

Beta phase includes cleaning of data

Gamma phase performs transformation

Delta phase graphs and reports are generated on multiple BI tools

Code repository

Services

Architecture

Conclusion

Summary

Introduction to AWS and Azure

Amazon Web Services

Compute

Storage

Database

Networking and content

Developer tools

Management tools

Machine learning

Analytics

Security, identity, and compliance

Internet of Things

Migration

Other services

Overview to AWS Services

Microsoft Azure

Compute

Networking

Storage

Web and mobile

Containers

Databases

Analytics

AI and machine learning

Internet of Things

Security and Identity

Developer Tools

Management Tools

Overview to Azure Services

Head to head of Google Cloud Platform with Amazon Web Services and Microsoft Azure

Compute

Storage

Database

Analytics and big data

Internet of Things

Mobile Services

Application Services

Networking

Security and Identity

Monitoring and Management

Summary

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