Expert tips, techniques, and best practices to pass the AWS Certified Big Data - Specialty exam
About This Video
In Detail
This course covers all aspects of hosting big data on the Amazon Web Services …
Expert tips, techniques, and best practices to pass the AWS Certified Big Data - Specialty exam
About This Video
In Detail
This course covers all aspects of hosting big data on the Amazon Web Services (AWS) platform, and will prepare you to confidently perform distributed processing.
The course begins with an overview of exam details and the recommended AWS knowledge you need before starting the course. It then takes you through topics relating to big data on AWS such as cloud computing and deployment, databases and data warehousing in AWS, and AWS services for big data. Next, you’ll move on to learn about data collection within big data on AWS which will cover data producers and consumers, IoT and big data, and Kinesis Firehose. As you advance, you’ll get to grips with the storage and processing aspects of big data on AWS, covering DynamoDB, AWS aurora in big data, and Amazon EMR. Finally, you’ll delve into visualization and security, and create a project for analyzing large datasets.
By the end of this course, you will have learned about cloud-based big data solutions, and be able to use AWS Elastic MapReduce to process data and create big data environments.
Audience
This course is for anyone with the cloud practitioner or associate-level AWS certification and a minimum of 2 years’ experience in performing complex big data analysis, including solutions architects, SysOps administrators, data scientists, and data analysts. The course assumes an understanding of AWS security best practices and AWS service integration.
Chapter 1 : Exam Details
Course Introduction
Overview of Big Data on AWS Certification
Objective of Big Data on AWS Certification Course
Exam Pattern and Exam Syllabus
Recommended AWS Knowledge
Chapter 2 : Big Data on AWS Introduction
Learning Objectives
Cloud Computing Introduction, Advantages, and Types
Cloud Deployment Models
Cloud Service Categories
AWS Cloud Platform
AWS Cloud Architecture Design Principles - Part I
AWS Cloud Architecture Design Principles - Part II
Why AWS for Big Data - Reasons and Challenges
Databases in AWS
Data Warehousing in AWS
Redshift, Kinesis, and EMR
DynamoDB, Machine Learning, and Lambda
Elastic Search Services and EC2
Key Takeaways
Chapter 3 : Big Data on AWS - Collection
Learning Objective
Amazon Kinesis and Kinesis Stream
Kinesis Data Stream Architecture and Core Components
Data Producer
Data Consumer
Kinesis Stream Emitting Data to AWS Services and Kinesis Connector Library
Kinesis Firehose
Demo - Put and Get Records from Kinesis Data Stream
Transferring Data Using Lambda
Amazon SQS Lifecycle and Architecture
IoT and Big Data
IoT Framework
AWS Data Pipelines and Data Nodes
Activity, Pre-Condition, and Schedule
Demo - Importing Data from S3 into DynamoDB Using Data Pipeline
Key Takeaways
Chapter 4 : Big Data on AWS - Storage
Learning Objective
Amazon Glacier and Big Data
DynamoDB Introduction
DynamoDB and EMR
DynamoDB Partitions and Distributions
DynamoDB GSI LSI
DynamoDB Stream and Cross-Region Replication
DynamoDB Performance and Partition Key Selection
Snowball and AWS Big Data
AWS DMS
AWS Aurora in Big Data
Demo - Amazon Athena Interactive SQL Queries for Data in Amazon S3 Part I
Demo - Amazon Athena Interactive SQL Queries for Data in Amazon S3 Part II
Key Takeaways
Chapter 5 : Big Data on AWS - Processing
Learning Objective
Amazon EMR
Demo - Analysing Big Data with Amazon EMR
Apache Hadoop
EMR Architecture
EMR Operations - Releases and Cluster
EMR Operations - Choosing Instance and Monitoring
Demo - Advanced EMR Setting Options
Hive on EMR
HBase with EMR
Presto with EMR
Spark with EMR
EMR File Storage
Demo - Analysing Large Datasets Using Hive and Spark
AWS Lambda
Key Takeaways
Chapter 6 : Big Data on AWS - Analysis
Learning Objective
Redshift Intro and Use Cases
Redshift Architecture
MPP and Redshift in AWS Ecosystem
Columnar Databases
Redshift Table Design - Part I
Redshift Table Design - Part II
Demo - Generating Random Dataset in EC2 and Loading it in S3
Demo - Redshift Maintenance and Operations
Machine Learning Introduction
Machine Learning Algorithm
Amazon SageMaker
Amazon Elasticsearch
Amazon Elasticsearch Services
Demo - Loading Datasets into Elasticsearch
Logstash and RStudio
Demo - Fetching the File and Analysing it using RStudio
Athena
Demo - Running Query on S3 using the Serverless Athena
Demo - Creating a Redshift Cluster and Loading the Datasets into it from S3 - Part I
Demo - Creating a Redshift Cluster and Loading the Datasets into it from S3 - Part II
Key Takeaways
Chapter 7 : Big Data on AWS - Visualization
Learning Objective
Amazon QuickSight
Demo - Creating an Analysis with a Single Visual using Sample Data
Demo - Creating an Analysis using Your Own Amazon S3 Data
Visual Types
Stories
Big Data Visualization
Key Takeaways
Chapter 8 : Big Data on AWS - Security
Learning Objective
EMR Security and Security Group
Roles and Private Subnet
Encryption at Rest and In-Transit
Redshift Security
Encryption at Rest using CloudHSM
Cloud HSM versus AWS KMS
Limit Data Access
Key Takeaways