John Wiley & Sons AWS Certified Data Analytics Study Guide Cover Move your career forward with AWS certification! Prepare for the AWS Certified Data Analytics Specia.. Product #: 978-1-119-64947-2 Regular price: $54.11 $54.11 In Stock

AWS Certified Data Analytics Study Guide

Specialty (DAS-C01) Exam

Abbasi, Asif


1. Edition February 2021
416 Pages, Softcover

ISBN: 978-1-119-64947-2
John Wiley & Sons

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Move your career forward with AWS certification! Prepare for the AWS Certified Data Analytics Specialty Exam with this thorough study guide

This comprehensive study guide will help assess your technical skills and prepare for the updated AWS Certified Data Analytics exam. Earning this AWS certification will confirm your expertise in designing and implementing AWS services to derive value from data. The AWS Certified Data Analytics Study Guide: Specialty (DAS-C01) Exam is designed for business analysts and IT professionals who perform complex Big Data analyses.

This AWS Specialty Exam guide gets you ready for certification testing with expert content, real-world knowledge, key exam concepts, and topic reviews. Gain confidence by studying the subject areas and working through the practice questions. Big data concepts covered in the guide include:
* Collection
* Storage
* Processing
* Analysis
* Visualization
* Data security

AWS certifications allow professionals to demonstrate skills related to leading Amazon Web Services technology. The AWS Certified Data Analytics Specialty (DAS-C01) Exam specifically evaluates your ability to design and maintain Big Data, leverage tools to automate data analysis, and implement AWS Big Data services according to architectural best practices. An exam study guide can help you feel more prepared about taking an AWS certification test and advancing your professional career. In addition to the guide's content, you'll have access to an online learning environment and test bank that offers practice exams, a glossary, and electronic flashcards.

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

References 142

Chapter 4 Data Processing and Analysis 143

Introduction 144

Types of Analytical Workloads 144

Amazon Athena 146

Apache Presto 147

Apache Hive 148

Amazon Athena Use Cases and Workloads 149

Amazon Athena DDL, DML, and DCL 150

Amazon Athena Workgroups 151

Amazon Athena Federated Query 153

Amazon Athena Custom UDFs 154

Using Machine Learning with Amazon Athena 154

Amazon EMR 155

Apache Hadoop Overview 156

Amazon EMR Overview 157

Apache Hadoop on Amazon EMR 158


Bootstrap Actions and Custom AMI 167

Security on EMR 167

EMR Notebooks 168

Apache Hive and Apache Pig on Amazon EMR 169

Apache Spark on Amazon EMR 174

Apache HBase on Amazon EMR 182

Apache Flink, Apache Mahout, and Apache MXNet 184

Choosing the Right Analytics Tool 186

Amazon Elasticsearch Service 188

When to Use Elasticsearch 188

Elasticsearch Core Concepts (the ELK Stack) 189

Amazon Elasticsearch Service 191

Amazon Redshift 192

What is Data Warehousing? 192

What is Redshift? 193

Redshift Architecture 195

Redshift AQUA 198

Redshift Scalability 199

Data Modeling in Redshift 205

Data Loading and Unloading 213

Query Optimization in Redshift 217

Security in Redshift 221

Kinesis Data Analytics 225

How Does It Work? 226

What is Kinesis Data Analytics for Java? 228

Comparing Batch Processing Services 229

Comparing Orchestration Options on AWS 230

AWS Step Functions 230

Comparing Different ETL Orchestration Options 230

Summary 231

Exam Essentials 232

Exercises 232

Review Questions 235

References 237

Recommended Workshops 237

Amazon Athena Blogs 238

Amazon Redshift Blogs 240

Amazon EMR Blogs 241

Amazon Elasticsearch Blog 241

Amazon Redshift References and Further Reading 242

Chapter 5 Data Visualization 243

Introduction 244

Data Consumers 245

Data Visualization Options 246

Amazon QuickSight 247

Getting Started 248

Working with Data 250

Data Preparation 255

Data Analysis 256

Data Visualization 258

Machine Learning Insights 261

Building Dashboards 262

Embedding QuickSight Objects into Other Applications 264

Administration 265

Security 266

Other Visualization Options 267

Predictive Analytics 270

What is Predictive Analytics? 270

The AWS ML Stack 271

Summary 273

Exam Essentials 273

Exercises 274

Review Questions 275

References 276

Additional Reading Material 276

Chapter 6 Data Security 279

Introduction 280

Shared Responsibility Model 280

Security Services on AWS 282

AWS IAM Overview 285

IAM User 285

IAM Groups 286

IAM Roles 287

Amazon EMR Security 289

Public Subnet 290

Private Subnet 291

Security Configurations 293

Block Public Access 298

VPC Subnets 298

Security Options during Cluster Creation 299

EMR Security Summary 300

Amazon S3 Security 301

Managing Access to Data in Amazon S3 301

Data Protection in Amazon S3 305

Logging and Monitoring with Amazon S3 306

Best Practices for Security on Amazon S3 308

Amazon Athena Security 308

Managing Access to Amazon Athena 309

Data Protection in Amazon Athena 310

Data Encryption in Amazon Athena 311

Amazon Athena and AWS Lake Formation 312

Amazon Redshift Security 312

Levels of Security within Amazon Redshift 313

Data Protection in Amazon Redshift 315

Redshift Auditing 316

Redshift Logging 317

Amazon Elasticsearch Security 317

Elasticsearch Network Configuration 318

VPC Access 318

Accessing Amazon Elasticsearch and Kibana 319

Data Protection in Amazon Elasticsearch 322

Amazon Kinesis Security 325

Managing Access to Amazon Kinesis 325

Data Protection in Amazon Kinesis 326

Amazon Kinesis Best Practices 326

Amazon QuickSight Security 327

Managing Data Access with Amazon QuickSight 327

Data Protection 328

Logging and Monitoring 329

Security Best Practices 329

Amazon DynamoDB Security 329

Access Management in DynamoDB 329

IAM Policy with Fine-Grained Access Control 330

Identity Federation 331

How to Access Amazon DynamoDB 332

Data Protection with DynamoDB 332

Monitoring and Logging with DynamoDB 333

Summary 334

Exam Essentials 334

Exercises/Workshops 334

Review Questions 336

References and Further Reading 337

Appendix Answers to Review Questions 339

Chapter 1: History of Analytics and Big Data 340

Chapter 2: Data Collection 342

Chapter 3: Data Storage 343

Chapter 4: Data Processing and Analysis 344

Chapter 5: Data Visualization 346

Chapter 6: Data Security 346

Index 349
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.