Fermé

AWS DATA ENGINEER VERY VERY URGENT

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

Compétences : Amazon Web Services, Informatique en Nuage, Big Data Sales, GCP AI, PostgreSQL

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Concernant l'employeur :
( 0 commentaires ) kovilpatti, India

Nº du projet : #32244984

6 freelances font une offre moyenne de 11567 ₹ pour ce travail

(9 Commentaires)
4.0
shanmup

I am an experienced aws engineer and ready to train the people. How many members are there to take the class. Thanks

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2.3
Ganeshmuruganr

As a solution architect i am have very good knowledge of all AWS services. Over all 10years of experience in Data warehousing with vast knowledge in data analytics. Please message to proceed further

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kt4techie

Hello & Greetings, I am interested in your Project and have relevant experience in Various Cloud Products i.e AWS, GCP, Azure. Please send me a message for further discussions. Thanks

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rohitgupta2432

Hi, I have more than 7+ years of experience developing products, stack I have used is spring boot, JPA, MongoDB, PostgreSQL, aws and microservice architecture, we can discuss this requirement.

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Umeyakassim

IT professional very familiar with tech industry. Good at programming, Networking ccna certification Have both technical and soft skills .

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