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E-grāmata: AWS Certified Data Analytics Study Guide - Specialty (DAS-C01) Exam: Specialty (DAS-C01) Exam [Wiley Online]

  • Formāts: 416 pages
  • Izdošanas datums: 08-Feb-2021
  • Izdevniecība: Sybex Inc.,U.S.
  • ISBN-10: 111964948X
  • ISBN-13: 9781119649489
Citas grāmatas par šo tēmu:
  • Wiley Online
  • Cena: 61,11 €*
  • * this price gives unlimited concurrent access for unlimited time
AWS Certified Data Analytics Study Guide - Specialty (DAS-C01) Exam: Specialty (DAS-C01) ExamPalielināt
  • Formāts: 416 pages
  • Izdošanas datums: 08-Feb-2021
  • Izdevniecība: Sybex Inc.,U.S.
  • ISBN-10: 111964948X
  • ISBN-13: 9781119649489
Citas grāmatas par šo tēmu:
Wiley Online E-booksMore info about Wiley Online
Rokasgrāmatas mājas lapa: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119649489

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(30)
Evolution of Analytics Architecture Over the Years
3(2)
The New World Order
5(1)
Analytics Pipeline
6(4)
Data Sources
7(1)
Collection
8(1)
Storage
8(1)
Processing and Analysis
9(1)
Visualization, Predictive and Prescriptive Analytics
9(1)
The Big Data Reference Architecture
10(6)
Data Characteristics: Hot, Warm, and Cold
11(1)
Collection/Ingest
12(1)
Storage
13(1)
Process/Analyze
14(1)
Consumption
15(1)
Data Lakes and Their Relevance in Analytics
16(3)
What Is a Data Lake?
16(3)
Building a Data Lake on AWS
19(4)
Step 1 Choosing the Right Storage - Amazon S3 Is the Base
19(2)
Step 2 Data Ingestion - Moving the Data into the Data Lake
21(1)
Step 3 Cleanse, Prep, and Catalog the Data
22(1)
Step 4 Secure the Data and Metadata
23(1)
Step 5 Make Data Available for Analytics
23(1)
Using Lake Formation to Build a Data Lake on AWS
23(1)
Exam Objectives
24(3)
Objective Map
25(2)
Assessment Test
27(2)
References
29(2)
Chapter 2 Data Collection
31(62)
Exam Objectives
32(1)
AWS IoT
33(5)
Common Use Cases for AWS IoT
35(1)
How AWS IoT Works
36(2)
Amazon Kinesis
38(26)
Amazon Kinesis Introduction
40(1)
Amazon Kinesis Data Streams
40(14)
Amazon Kinesis Data Analytics
54(7)
Amazon Kinesis Video Streams
61(3)
AWS Glue
64(8)
Glue Data Catalog
66(2)
Glue Crawlers
68(1)
Authoring ETL Jobs
69(2)
Executing ETL Jobs
71(1)
Change Data Capture with Glue Bookmarks
71(1)
Use Cases for AWS Glue
72(1)
Amazon SQS
72(2)
Amazon Data Migration Service
74(3)
What Is AWS DMS Anyway?
74(1)
What Does AWS DMS Support?
75(2)
AWS Data Pipeline
77(4)
Pipeline Definition
77(1)
Pipeline Schedules
78(1)
Task Runner
79(2)
Large-Scale Data Transfer Solutions
81(6)
AWS Snowcone
81(1)
AWS Snowball
82(3)
AWS Snowmobile
85(1)
AWS Direct Connect
86(1)
Summary
87(1)
Review Questions
88(2)
References
90(1)
Exercises & Workshops
91(2)
Chapter 3 Data Storage
93(50)
Introduction
94(1)
Amazon S3
95(8)
Amazon S3 Data Consistency Model
96(1)
Data Lake and S3
97(3)
Data Replication in Amazon S3
100(1)
Server Access Logging in Amazon S3
101(1)
Partitioning, Compression, and File Formats on S3
101(2)
Amazon S3 Glacier
103(1)
Vault
103(1)
Archive
104(1)
Amazon DynamoDB
104(13)
Amazon DynamoDB Data Types
105(3)
Amazon DynamoDB Core Concepts
108(1)
Read/Write Capacity Mode in DynamoDB
108(3)
DynamoDB Auto Scaling and Reserved Capacity
111(1)
Read Consistency and Global Tables
111(2)
Amazon DynamoDB: Indexing and Partitioning
113(1)
Amazon DynamoDB Accelerator
114(1)
Amazon DynamoDB Streams
115(1)
Amazon DynamoDB Streams - Kinesis Adapter
116(1)
Amazon DocumentDB
117(4)
Why a Document Database?
117(2)
Amazon DocumentDB Overview
119(1)
Amazon Document DB Architecture
120(1)
Amazon DocumentDB Interfaces
120(1)
Graph Databases and Amazon Neptune
121(2)
Amazon Neptune Overview
122(1)
Amazon Neptune Use Cases
123(1)
Storage Gateway
123(4)
Hybrid Storage Requirements
123(2)
AWS Storage Gateway
125(2)
Amazon EFS
127(6)
Amazon EFS Use Cases
130(2)
Interacting with Amazon EFS
132(1)
Amazon EFS Security Model
132(1)
Backing Up Amazon EFS
132(1)
Amazon FSx for Lustre
133(2)
Key Benefits of Amazon FSx for Lustre
134(1)
Use Cases for Lustre
135(1)
AWS Transfer for SFTP
135(1)
Summary
136(1)
Exercises
137(3)
Review Questions
140(2)
Further Reading
142(1)
References
142(1)
Chapter 4 Data Processing and Analysis
143(100)
Introduction
144(1)
Types of Analytical Workloads
144(2)
Amazon Athena
146(9)
Apache Presto
147(1)
Apache Hive
148(1)
Amazon Athena Use Cases and Workloads
149(1)
Amazon Athena DDL, DML, and DCL
150(1)
Amazon Athena Workgroups
151(2)
Amazon Athena Federated Query
153(1)
Amazon Athena Custom UDFs
154(1)
Using Machine Learning with Amazon Athena
154(1)
Amazon EMR
155(33)
Apache Hadoop Overview
156(1)
Amazon EMR Overview
157(1)
Apache Hadoop on Amazon EMR
158(8)
EMRFS
166(1)
Bootstrap Actions and Custom AMI
167(1)
Security on EMR
167(1)
EMR Notebooks
168(1)
Apache Hive and Apache Pig on Amazon EMR
169(5)
Apache Spark on Amazon EMR
174(8)
Apache HBase on Amazon EMR
182(2)
Apache Flink, Apache Mahout, and Apache MXNet
184(2)
Choosing the Right Analytics Tool
186(2)
Amazon Elasticsearch Service
188(4)
When to Use Elasticsearch
188(1)
Elasticsearch Core Concepts (the ELK Stack)
189(2)
Amazon Elasticsearch Service
191(1)
Amazon Redshift
192(33)
What Is Data Warehousing?
192(1)
What Is Redshift?
193(2)
Redshift Architecture
195(3)
Redshift AQUA
198(1)
Redshift Scalability
199(6)
Data Modeling in Redshift
205(8)
Data Loading and Unloading
213(4)
Query Optimization in Redshift
217(4)
Security in Redshift
221(4)
Kinesis Data Analytics
225(4)
How Does It Work?
226(2)
What Is Kinesis Data Analytics for Java?
228(1)
Comparing Batch Processing Services
229(1)
Comparing Orchestration Options on AWS
230(1)
AWS Step Functions
230(1)
Comparing Different ETL Orchestration Options
230(1)
Summary
231(1)
Exam Essentials
232(1)
Exercises
232(3)
Review Questions
235(2)
References
237(6)
Recommended Workshops
237(1)
Amazon Athena Blogs
238(2)
Amazon Redshift Blogs
240(1)
Amazon EMR Blogs
241(1)
Amazon Elasticsearch Blog
241(1)
Amazon Redshift References and Further Reading
242(1)
Chapter 5 Data Visualization
243(36)
Introduction
244(1)
Data Consumers
245(1)
Data Visualization Options
246(1)
Amazon QuickSight
247(20)
Getting Started
248(2)
Working with Data
250(5)
Data Preparation
255(1)
Data Analysis
256(2)
Data Visualization
258(3)
Machine Learning Insights
261(1)
Building Dashboards
262(2)
Embedding QuickSight Objects into Other Applications
264(1)
Administration
265(1)
Security
266(1)
Other Visualization Options
267(3)
Predictive Analytics
270(3)
What Is Predictive Analytics?
270(1)
The AWS ML Stack
271(2)
Summary
273(1)
Exam Essentials
273(1)
Exercises
274(1)
Review Questions
275(1)
References
276(1)
Additional Reading Material
276(3)
Chapter 6 Data Security
279(60)
Introduction
280(1)
Shared Responsibility Model
280(2)
Security Services on AWS
282(3)
AWS IAM Overview
285(4)
IAM User
285(1)
IAM Groups
286(1)
IAM Roles
287(2)
Amazon EMR Security
289(12)
Public Subnet
290(1)
Private Subnet
291(2)
Security Configurations
293(5)
Block Public Access
298(1)
VPC Subnets
298(1)
Security Options during Cluster Creation
299(1)
EMR Security Summary
300(1)
Amazon S3 Security
301(7)
Managing Access to Data in Amazon S3
301(4)
Data Protection in Amazon S3
305(1)
Logging and Monitoring with Amazon S3
306(2)
Best Practices for Security on Amazon S3
308(1)
Amazon Athena Security
308(4)
Managing Access to Amazon Athena
309(1)
Data Protection in Amazon Athena
310(1)
Data Encryption in Amazon Athena
311(1)
Amazon Athena and AWS Lake Formation
312(1)
Amazon Redshift Security
312(5)
Levels of Security within Amazon Redshift
313(2)
Data Protection in Amazon Redshift
315(1)
Redshift Auditing
316(1)
Redshift Logging
317(1)
Amazon Elasticsearch Security
317(8)
Elasticsearch Network Configuration
318(1)
VPC Access
318(1)
Accessing Amazon Elasticsearch and Kibana
319(3)
Data Protection in Amazon Elasticsearch
322(3)
Amazon Kinesis Security
325(2)
Managing Access to Amazon Kinesis
325(1)
Data Protection in Amazon Kinesis
326(1)
Amazon Kinesis Best Practices
326(1)
Amazon QuickSight Security
327(2)
Managing Data Access with Amazon QuickSight
327(1)
Data Protection
328(1)
Logging and Monitoring
329(1)
Security Best Practices
329(1)
Amazon DynamoDB Security
329(5)
Access Management in DynamoDB
329(1)
IAM Policy with Fine-Grained Access Control
330(1)
Identity Federation
331(1)
How to Access Amazon DynamoDB
332(1)
Data Protection with DynamoDB
332(1)
Monitoring and Logging with DynamoDB
333(1)
Summary
334(1)
Exam Essentials
334(1)
Exercises/Workshops
334(2)
Review Questions
336(1)
References and Further Reading
337(3)
Appendix Answers to Review Questions 339(10)
Chapter 1 History of Analytics and Big Data
340(2)
Chapter 2 Data Collection
342(1)
Chapter 3 Data Storage
343(1)
Chapter 4 Data Processing and Analysis
344(2)
Chapter 5 Data Visualization
346(1)
Chapter 6 Data Security
346(3)
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.
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