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AWS DATA ENGINEER VERY VERY URGENT -- 2

AWS DATA ENGINER TRAINER REQUIRED IMMEDIATE BASIS

5 DAYS OF TRAINIG UREGENT BASIS NEED TO START IMMEDAITELY

Module 2 - Big Data on AWS Introduction

1. Learning Objective

2. Cloud computing and it's advantages

3. Cloud Computing Models

4. Cloud Service Categories

5. AWS Cloud Platform

6. Design Principles - Part One

7. Design Principles - Part Two

8. [login to view URL] AWS for Big Data - Reasons and Challenges

9. [login to view URL] in AWS

10. Data Warehousing in AWS

11. Redshift, Kinesis and EMR

12. DynamoDB, Machine Learning and Lambda

13. Elastic Search Services and EC2

14. Key Takeaways

Module 3 - AWS Big Data Collection Services

1. Learning Objective

2. Amazon Kinesis and Kinesis Stream

3. Kinesis Data Stream Architecture and Core Components

4. Data Producer

5. Data Consumer

6. Kinesis Stream Emitting Data to AWS Services and Kinesis Connector Library

7. Kinesis Firehose

8. Transferring Data Using Lambda

9. Amazon SQS, Lifecycle and Architecture

10. IoT and Big Data

11. IoT Framework

12. AWS Data Pipelines and Data Nodes

13. Activity, Pre-condition and Schedule

14. Key Takeaways

Module 4 - AWS Big Data Storage Services

1. Learning Objective

2. Amazon Glacier and Big Data

3. DynamoDB Introduction

4. DynamoDB and EMR

5. DynamoDB Partitions and Distributions

6. DynamoDB GSI LSI

7. DynamoDB Stream and Cross Region Replication

8. DynamoDB Performance and Partition Key Selection

9. Snowball and AWS Big Data

10. AWS DMS

11. AWS Aurora in Big Data

12. Key Takeaways

Module 5 - AWS Big Data Processing Services

1. Learning Objective

2. Amazon EMR

3. Apache Hadoop

4. EMR Architecture

5. EMR Releases and Cluster

6. Choosing Instance and Monitoring

7. Demo - Advance EMR Setting Options

8. Hive on EMR

9. HBase with EMR

10. Presto with EMR

11. Spark with EMR

12. EMR File Storage

13. AWS Lambda

14. Key Takeaways

Module 6 - Analysis

1. Learning Objective

2. Redshift Intro and Use cases

3. Redshift Architecture

4. MPP and Redshift in AWS Eco-System

5. Columnar Databases

6. Redshift Table Design - Part 2

7. Demo - Redshift Maintenance and Operations

8. Machine Learning Introduction

9. Machine Learning Algorithm

10. Amazon SageMaker

11. Amazon Elasticsearch

12. Amazon Elasticsearch Services

13. Demo - Loading Dataset into Elasticsearch

14. Logstash and R Studio

15. Demo - Fetching the File and Analyzing it using RStudio

16. Athena

17. Demo - Running Query on S3 using the Serverless Athena

18. Key Takeaways

Module 7 - Visualization

1. Introduction to Amazon QuickSight

2. Visual Types

3. Story

4. Big Data Visualization

5. Key Takeaways

Module 8 - Security

Skills: Amazon Web Services, Hadoop, Database Administration, Cloud Computing, Linux

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Ganeshmuruganr

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