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.
By:
Asif Abbasi
Imprint: Sybex Inc.,U.S.
Country of Publication: United States
Dimensions:
Height: 234mm,
Width: 185mm,
Spine: 25mm
Weight: 612g
ISBN: 9781119649472
ISBN 10: 1119649471
Pages: 416
Publication Date: 25 November 2020
Audience:
Professional and scholarly
,
Undergraduate
Format: Paperback
Publisher's Status: Active
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 EMRFS 166 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.
