AWS Certified Data Analytics Study Guide - Asif Abbasi

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Résumé :

Introduction xxi

Assessment Test xxx

Chapter 1 History of Analytics and Big Data 1

Evolution of Analytics Architecture Over the Years 3

The New World Order 5

Analytics Pipeline 6

Data Sources 7

Collection 8

Storage 8

Processing and Analysis 9

Visualization, Predictive and Prescriptive Analytics 9

The Big Data Reference Architecture 10

Data Characteristics: Hot, Warm, and Cold 11

Collection/Ingest 12

Storage 13

Process/Analyze 14

Consumption 15

Data Lakes and Their Relevance in Analytics 16

What is a Data Lake? 16

Building a Data Lake on AWS 19

Step 1: Choosing the Right Storage - Amazon S3

Is the Base 19

Step 2: Data Ingestion - Moving the Data into

the Data Lake 21

Step 3: Cleanse, Prep, and Catalog the Data 22

Step 4: Secure the Data and Metadata 23

Step 5: Make Data Available for Analytics 23

Using Lake Formation to Build a Data Lake on AWS 23

Exam Objectives 24

Objective Map 25

Assessment Test 27

References 29

Chapter 2 Data Collection 31

Exam Objectives 32

AWS IoT 33

Common Use Cases for AWS IoT 35

How AWS IoT Works 36

Amazon Kinesis 38

Amazon Kinesis Introduction 40

Amazon Kinesis Data Streams 40

Amazon Kinesis Data Analytics 54

Amazon Kinesis Video Streams 61

AWS Glue 64

Glue Data Catalog 66

Glue Crawlers 68

Authoring ETL Jobs 69

Executing ETL Jobs 71

Change Data Capture with Glue Bookmarks 71

Use Cases for AWS Glue 72

Amazon SQS 72

Amazon Data Migration Service 74

What is AWS DMS Anyway? 74

What Does AWS DMS Support? 75

AWS Data Pipeline 77

Pipeline Definition 77

Pipeline Schedules 78

Task Runner 79

Large-Scale Data Transfer Solutions 81

AWS Snowcone 81

AWS Snowball 82

AWS Snowmobile 85

AWS Direct Connect 86

Summary 87

Review Questions 88

References 90

Exercises & Workshops 91

Chapter 3 Data Storage 93

Introduction 94

Amazon S3 95

Amazon S3 Data Consistency Model 96

Data Lake and S3 97

Data Replication in Amazon S3 100

Server Access Logging in Amazon S3 101

Partitioning, Compression, and File Formats on S3 101

Amazon S3 Glacier 103

Vault 103

Archive 104

Amazon DynamoDB 104

Amazon DynamoDB Data Types 105

Amazon DynamoDB Core Concepts 108

Read/Write Capacity Mode in DynamoDB 108

DynamoDB Auto Scaling and Reserved Capacity 111

Read Consistency and Global Tables 111

Amazon DynamoDB: Indexing and Partitioning 113

Amazon DynamoDB Accelerator 114

Amazon DynamoDB Streams 115

Amazon DynamoDB Streams - Kinesis Adapter 116

Amazon DocumentDB 117

Why a Document Database? 117

Amazon DocumentDB Overview 119

Amazon Document DB Architecture 120

Amazon DocumentDB Interfaces 120

Graph Databases and Amazon Neptune 121

Amazon Neptune Overview 122

Amazon Neptune Use Cases 123

Storage Gateway 123

Hybrid Storage Requirements 123

AWS Storage Gateway 125

Amazon EFS 127

Amazon EFS Use Cases 130

Interacting with Amazon EFS 132

Amazon EFS Security Model 132

Backing Up Amazon EFS 132

Amazon FSx for Lustre 133

Key Benefits of Amazon FSx for Lustre 134

Use Cases for Lustre 135

AWS Transfer for SFTP 135

Summary 136

Exercises 137

Review Questions 140

Further Reading 142

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Biographie:
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Sommaire:

ASIF ABBASI has over 20 years of experience working in various Data & Analytics engineering, consulting and advisory roles with some of the largest customers across the globe to help them in their quest to become more data driven. Asif is the author of Learning Apache Spark 2.0 and is an AWS Certified Data Analytics & Machine Learning Specialist, AWS Certified Solutions Architect (Professional), Hortonworks Certified Hadoop Professional and Administrator, Certified Spark Developer, SAS Certified Predictive Modeler, and Sun Certified Enterprise Architect. Asif is also a Project Management Professional.