AWS Certified Big Data Specialty
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AWS Certified Big Data Specialty Workbook is developed by multiple engineers that are specialized in different fields e.g. AWS Certified Developer – Associate, AWS Certified Security Specialty, AWS certified Cloud Practitioner etc. Each of these engineers has developed content in his/her field of specialization, therefore, this training guide provides an in-depth understanding and complete course material to pass the AWS Certified Big Data Specialty Exam. The workbook is designed to develop a practical approach to learn real-life examples and case studies.

Overview of AWS Certified Big Data Specialty Exam

The AWS Certified Big Data Specialty exam validates the skills and experience in performing complex big data analyses using AWS technologies. Example concepts you should understand for this exam include:

  • Implementation of core AWS Big Data services as per basic architectural best practices
  • Design and maintain Big Data
  • Automation of data analysis

About AWS – Certified Big Data – Specialty Exam

Exam Questions

Multiple choice and multiple answer

Exam Number

BDS-C00

Time to Complete

170 minutes

Available Languages

English, Japanese, Korean, and Simplified Chinese

Exam Fee

300 USD

Recommended AWS Knowledge
  • Two or more years of hands-on experience using AWS technologies.
  • Best practices of AWS security.
  • Independently define AWS architecture and services and understand how they integrate with each other.
  • Define and architect AWS big data services and explain how they fit in the data life cycle of collection, ingestion, storage, processing and visualization.

 

Domain

%

Domain 1

Collection

17%

Domain 2

Storage

17%

Domain 3

Processing

17%

Domain 4

Analysis

17%

Domain 5

Visualization

12%

Domain 6

Data Security

20%

Total

100%

Chapter 01: Introduction to Cloud Computing

What is Cloud Computing?
Advantages of Cloud Computing
Types of Cloud Computing
Cloud Computing Deployments Models
Amazon Web Services Cloud Platform
The Cloud Computing Difference
AWS Cloud Economics
AWS Virtuous Cycle
AWS Cloud Architecture Design Principles
Databases
Relational Databases
Non-Relational Databases
Data Warehouse
Practice Questions:

Chapter 02: Collection

Introduction
Kinesis Stream Introduction
Amazon Kinesis
Amazon Kinesis Stream
Benefits of Amazon Kinesis Stream
Loading Data into Kinesis Stream
Kinesis Data Stream High-Level Architecture
Kinesis Stream Core Concepts
Shard
Records
Retention Period
Data Producer
Data Consumers
Kinesis Stream Emitting Data to AWS Services
Kinesis Connector Library
Kinesis Firehose
Key Concept
Transferring Data Using Lambda
Amazon SQS
Message Lifecycle
Amazon SQS Architecture
AWS IOT
Introduction
IoT and Big Data
IoT Frame Work
AWS Data Pipeline
Key Concept
AWS Data Pipeline Components
Data Nodes
Activity
Pre-Condition
Schedule
Mind Map
Practice Questions

Chapter 03: Storage

Introduction
Amazon Glacier
Glacierand Big Data
DynamoDB Introduction
The Architecture of the DynamoDB Table
DynamoDB in AWS Eco-System
DynamoDB Partitions
Data Distribution
Key Concepts
DynamoDB GSI/ LSI
Local Secondary Index (LSI)
Global Secondary Index (GSI)
DynamoDB Stream & Replication
DynamoDB Stream
Cross-Region Replication
DynamoDB Performance Deep Dive
Partition Key Selection
Global Secondary Index (GSI)
Mind Map
Practice Questions

Chapter 04: Processing

Introduction
Amazon Elastic MapReduce (EMR)
Use Cases
Apache Hadoop
Architecture Modules
Architecture
EMR Architecture
Master Node
Core Node
Task Node
HDFS (Hadoop Distributed File System)
Storage Options
Single-AZ Concept
EMR Operations
EMR Releases
Launching a Cluster
Quick and Advanced Setting Option
Long-running Cluster
Transient Cluster
Choosing Instance Type
Number of instances
Monitoring
Resizing of Cluster
Using Hue with EMR
Setup hue for LDAP
Hive on EMR
Use Cases
Architecture
HBase with EMR
Use Cases
Where to use HBase
Where not to use HBase
HBase v/s DynamoDB
HBase v/s Redshift
HBase Architecture
HBase and EMRFS
HBase integration
Presto with EMR
Advantages of Presto
Not To use Presto
Presto Architecture
Spark with EMR
Use Cases
Not to use Spark
Spark Components
Spark Integration With EMR
EMR File Storage and Compression
Compression
Supported File Format
File Size
Lab 4.1: EMR
AWS Lambda in AWS BigData Ecosystem
Limitations of Lambda
Lambda and Kinesis Stream
Lambda and Redshift
HCatalog
Lab 4.2: HCatalog
MindMap
Practice Question

Chapter 05: Analysis

Introduction
RedShift
Why learn RedShift?
Used cases of RedShift
RedShift Architecture
RedShift in the AWS Ecosystem
Columnar Databases
RedShift Table Design – Introduction
RedShift Table Design – Distribution Styles
RedShift Table Design – Sort Keys
RedShift Table Design – Data Types
RedShift Table Design – Compression
RedShift Table Design – Constraints
PRIMARY KEYS
RedShift Table Design – Design Review
RedShift Workload Management
RedShift Loading Data
RedShift Maintenance and Operations
Lab 5-01: Generating Dataset
Lab 5-02: RedShift
Machine Learning
Use Cases
Algorithms
Amazon SageMaker
Lab 5-03: Machine Learning
Elasticsearch
Amazon Elasticsearch Service
Loading of data into Elasticsearch
Logstash
RStudio
Characteristics
Lab 5-04: RStudio
Athena
Presto and Hive
Data Formats
Integration with AWS Glue
Comparison of Athena with other AWS services
Lab 5-05: Athena
Scenario
Mind Map
Practice Questions


Chapter 06: Visualization


Introduction
Amazon QuickSight Introduction
Use Cases
Data Set
QuickSight Visualization
Visual Types
Stories
Dashboard
Big Data Visualization
Apache Zeppelin
Jupyter Notebook
Comparison between Notebooks
D3.js (Data-Driven Documents)
MicroStrategy
Mind Map
Practice Question


Chapter 07: Security


Introduction
EMR Security
Security Group
Roles
Private Subnet
Encryption At rest and In-transit
RedShift Security
Encryption In-Transit
Encryption At-Rest
KMS vs CloudHSM
Limit data access
Mind Map
Practice Questions
Answers:
Acronyms:
References:
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