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AWS Certified Big Data - Specialty Certification
Exam Details
Course Introduction (2:51)
Overview of Big Data on AWS Certification (1:23)
Objective of Big Data on AWS Certification Course (0:18)
Exam Pattern and Exam Syllabus (1:51)
Recommended AWS Knowledge (0:43)
Big Data on AWS Introduction
Learning Objectives (0:52)
Cloud Computing Introduction, Advantages, and Types (3:11)
Cloud Deployment Models (5:02)
Cloud Service Categories (4:14)
AWS Cloud Platform (3:55)
AWS Cloud Architecture Design Principles – Part I (4:17)
AWS Cloud Architecture Design Principles – Part II (2:52)
Why AWS for Big Data – Reasons and Challenges (1:45)
Databases in AWS (6:40)
Data Warehousing in AWS (2:02)
Redshift, Kinesis, and EMR (5:26)
DynamoDB, Machine Learning, and Lambda (5:02)
Elastic Search Services and EC2 (2:52)
Key Takeaways (0:34)
Big Data on AWS – Collection
Learning Objective (0:49)
Amazon Kinesis and Kinesis Stream (2:34)
Kinesis Data Stream Architecture and Core Components (3:46)
Data Producer (4:29)
Data Consumer (3:03)
Kinesis Stream Emitting Data to AWS Services and Kinesis Connector Library (4:21)
Kinesis Firehose (6:10)
Demo – Put and Get Records from Kinesis Data Stream (5:13)
Transferring Data Using Lambda (4:28)
Amazon SQS Lifecycle and Architecture (6:19)
IoT and Big Data (3:22)
IoT Framework (4:14)
AWS Data Pipelines and Data Nodes (5:08)
Activity, Pre-Condition, and Schedule (2:06)
Demo – Importing Data from S3 into DynamoDB Using Data Pipeline (7:05)
Key Takeaways (0:45)
Big Data on AWS – Storage
Learning Objective (0:33)
Amazon Glacier and Big Data (3:48)
DynamoDB Introduction (5:15)
DynamoDB and EMR (1:44)
DynamoDB Partitions and Distributions (4:01)
DynamoDB GSI LSI (2:42)
DynamoDB Stream and Cross-Region Replication (4:01)
DynamoDB Performance and Partition Key Selection (3:24)
Snowball and AWS Big Data (1:12)
AWS DMS (1:20)
AWS Aurora in Big Data (3:20)
Demo – Amazon Athena Interactive SQL Queries for Data in Amazon S3 Part I (18:23)
Demo – Amazon Athena Interactive SQL Queries for Data in Amazon S3 Part II (3:59)
Key Takeaways (0:30)
Big Data on AWS – Processing
Learning Objective (0:40)
Amazon EMR (3:38)
Demo – Analysing Big Data with Amazon EMR (8:13)
Apache Hadoop (4:13)
EMR Architecture (6:52)
EMR Operations – Releases and Cluster (2:12)
EMR Operations – Choosing Instance and Monitoring (6:05)
Demo – Advanced EMR Setting Options (3:30)
Hive on EMR (1:06)
HBase with EMR (5:49)
Presto with EMR (2:08)
Spark with EMR (6:14)
EMR File Storage (2:46)
Demo – Analysing Large Datasets Using Hive and Spark (8:27)
AWS Lambda (3:46)
Key Takeaways (0:32)
Big Data on AWS – Analysis
Learning Objective (0:43)
Redshift Intro and Use Cases (2:56)
Redshift Architecture (5:59)
MPP and Redshift in AWS Ecosystem (5:04)
Columnar Databases (3:29)
Redshift Table Design – Part I (6:00)
Redshift Table Design – Part II (4:59)
Demo – Generating Random Dataset in EC2 and Loading it in S3 (5:23)
Demo – Redshift Maintenance and Operations (1:44)
Machine Learning Introduction (3:58)
Machine Learning Algorithm (4:38)
Amazon SageMaker (0:57)
Amazon Elasticsearch (4:08)
Amazon Elasticsearch Services (4:28)
Demo – Loading Datasets into Elasticsearch (1:18)
Logstash and RStudio (1:48)
Demo – Fetching the File and Analysing it using RStudio (3:48)
Athena (2:36)
Demo – Running Query on S3 using the Serverless Athena (4:58)
Demo – Creating a Redshift Cluster and Loading the Datasets into it from S3 – Part I (6:07)
Demo – Creating a Redshift Cluster and Loading the Datasets into it from S3 – Part II (7:05)
Key Takeaways (0:25)
Big Data on AWS – Visualization
Learning Objective (0:36)
Amazon QuickSight (4:54)
Demo – Creating an Analysis with a Single Visual using Sample Data (5:07)
Demo – Creating an Analysis using Your Own Amazon S3 Data (7:51)
Visual Types (3:48)
Stories (2:17)
Big Data Visualization (3:30)
Key Takeaways (0:26)
Big Data on AWS – Security
Learning Objective (0:40)
EMR Security and Security Group (2:20)
Roles and Private Subnet (2:28)
Encryption at Rest and In-Transit (2:37)
Redshift Security (5:34)
Encryption at Rest using CloudHSM (2:43)
Cloud HSM versus AWS KMS (1:42)
Limit Data Access (1:25)
Key Takeaways (0:27)
DynamoDB, Machine Learning, and Lambda
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