AWS Data Engineering

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AWS Data Engineering is the study of Data Pipelines, Data Storage, and Data Transfer. Multisoft Systems has come up with AWS Data Engineering Training to promote the careers of Data Engineers, Data Scientists, Solutions Architects, and Data Analysts. This course will help you in learning the techniques of hosting big data and performing distributed processing on the AWS platform. To pursue this AWS Data Engineering Training, an aspirant needs to carry a fundamental level understanding of big data and Hadoop concepts and basic knowledge of AWS technical essentials.

Our offered course is developed by industry expert trainers to give in-depth knowledge of AWS Cloud Economics, Amazon DynamoDB, AWS Virtuous Cycle, Amazon DynamoDB, Kinesis Firehose, Knowledge Checks, DynamoDB Stream, EMR Operations, Spark Components, AWS Data Pipeline, RedShift Architecture. It is aligned with the AW Certified Data Analytics exam.

AWS Data Engineering Course Objectives:
  • How to work with AWS Cloud Architecture Design Principles?
  • How do I use Amazon EMR to process data with the use of Hadoop ecosystem tools
  • How do I use Amazon Kinesis for big data processing in real-time?
  • How to use Kinesis Streams to analyze and transform big data?
  • How to use Amazon QuickSight to visualize data and perform queries?
  • How to load Data into Kinesis Stream?
AWS Data Engineering Online Training
  • 32 Hrs. Instructor-led Online Training
  • Recorded Videos After Training
  • Digital Learning Material
  • Course Completion Certificate
  • Lifetime e-Learning Access
  • 24x7 After Training Support
Target Audience
  • Data Engineers
  • Data Scientists
  • Solutions Architects
  • Data Analysts
AWS Data Engineering Course Prerequisites
  • Basic knowledge of AWS technical essentials
  • A fundamental level understanding of big data and Hadoop concepts
AWS Data Engineering Course Certification
  • Multisoft Systems will provide you with a training completion certificate after completing this AWS Data Engineering Training.

Module 1: AWS in Big Data introduction

  • Introduction to 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
  • Why AWS for Big Data - Reasons
  • Why AWS for Big Data - Challenges
  • Databases in AWS
  • Relational vs Non-Relational Databases
  • Data Warehousing in AWS
  • Services for Collecting, Processing, Storing, and Analyzing Big Data
  • Amazon Redshift
  • Amazon Kinesis
  • Amazon EMR
  • Amazon DynamoDB
  • Amazon Machine Learning
  • AWS Lambda
  • Amazon Elasticsearch Service
  • Amazon EC2 (big data analytics software on EC2 instances)
  • Amazon Redshift
  • Amazon Kinesis
  • Amazon EMR
  • Amazon DynamoDB
  • Amazon Machine Learning
  • AWS Lambda
  • Amazon Elasticsearch Service
  • Amazon EC2 (big data analytics software on EC2 instances)
  • Key Takeaway
  • Knowledge Checks
  • Lesson End Project

Module 2 - Collection

  • Objectives
  • Amazon Kinesis Fundamentals
  • Loading Data into Kinesis Stream
  • Kinesis Data Stream High-Level Architecture
  • Kinesis Stream Core Concepts
  • Kinesis Stream Emitting Data to AWS Services
  • Kinesis Connector Library
  • Kinesis Firehose
  • Transferring Data Using Lambda
  • Amazon SQS
  • IoT and Big Data
  • IoT Framework
  • AWS Data Pipeline
  • AWS Data Pipeline Components
  • Key Takeaway
  • Knowledge Checks
  • Lesson End Project

Module 3 - Storage

  • Objectives
  • Introduction to AWS Big Data Storage Services
  • Amazon Glacier
  • Glacier and Big Data
  • DynamoDB Introduction
  • The Architecture of the DynamoDB Table
  • DynamoDB in AWS Ecosystem
  • DynamoDB Partitions
  • Data Distribution
  • Local Secondary Index (LSI) **
  • Global Secondary Index (GSI) **
  • DynamoDB GSI vs LSI
  • DynamoDB Stream
  • Cross-Region Replication in DynamoDB
  • Partition Key Selection
  • Snowball & AWS Big Data
  • AWS DMS
  • AWS Aurora in Big Data
  • Key Takeaway
  • Knowledge Checks
  • Lesson End Project

Module 4 - Processing I

  • Objectives
  • Introduction to AWS Big Data Processing Services
  • Amazon Elastic MapReduce (EMR)
  • Apache Hadoop
  • EMR Architecture
  • Storage Options
  • EMR File Storage and Compression
  • Supported File Format and File Size
  • Single-AZ Concept
  • EMR Operations
  • EMR Releases
  • AWS Cluster
  • Launching a Cluster
  • Advanced EMR Setting Option
  • Choosing Instance Type
  • Number of Instances
  • Monitoring EMR
  • Resizing of Cluster
  • Using Hue with EMR
  • Setup Hue for LDAP
  • Hive on EMR
  • Hive Use Cases
  • Key Takeaway
  • Knowledge Checks
  • Lesson End Project

Module 5: Processing II

  • HBase with EMR
  • HBase Use Cases
  • Comparison of HBase with Redshift and DynamoDB
  • HBase Architecture HBase on S3
  • HBase and EMRFS
  • HBase Integration
  • HCatalog
  • Presto with EMR
  • Advantages of Presto
  • Presto Architecture
  • Spark with EMR
  • Spark Use Cases
  • Spark Components
  • Spark Integration With EMR
  • AWS Lambda in AWS Big Data Ecosystem
  • Limitations of Lambda
  • Lambda and Kinesis Stream
  • Lambda and Redshift
  • Key Takeaway
  • Knowledge Checks
  • Lesson End Project

Module 6: Analysis I

  • Objectives
  • Introduction to AWS Big Data Analysis Services
  • RedShift
  • RedShift Architecture
  • RedShift in the AWS Ecosystem
  • Columnar Databases
  • RedShift Table Design
  • RedShift Workload Management
  • RedShift Loading Data
  • RedShift Maintenance and Operations
  • Key Takeaway
  • Knowledge Checks
  • Lesson End Project

Module 7: Analysis II

  • Machine Learning
  • Machine Learning - Use Cases
  • Algorithms
  • Amazon SageMaker
  • Elasticsearch
  • Amazon Elasticsearch Service
  • Loading of Data into Elasticsearch
  • Logstash
  • Kibana
  • RStudio
  • Characteristics
  • Athena
  • Presto and Hive
  • Integration with AWS Glue
  • Comparison of Athena with Other AWS Services
  • Lab Run Query on S3 Using Serverless Athena
  • Key Takeaway
  • Knowledge Checks
  • Lesson End Project

Module 8: Visualisation

  • Objectives
  • Introduction to AWS Big Data Visualization Services
  • Amazon QuickSight
  • Amazon QuickSight - Use Cases
  • LAB Create an Analysis with a Single Visual Using Sample Data
  • Working with Data
  • Assisted Practice: TBD
  • QuickSight Visualization
  • Big Data Visualization
  • Apache Zeppelin
  • Jupyter Notebook
  • Comparison Between Notebooks
  • D3.js (Data-Driven Documents)
  • MicroStrategy
  • Key Takeaway
  • Knowledge Checks
  • Lesson End Project

Module 9: Security

  • Objectives
  • Introduction to AWS Big Data Security Services
  • EMR Security
  • Roles
  • Private Subnet
  • Encryption At Rest and In Transit
  • RedShift Security
  • KMS Overview
  • SloudHSM
  • Limit Data Access
  • STS and Cross Account Access
  • Cloud Trail
  • Key Takeaway
  • Knowledge Checks
  • Lesson End Project
     

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