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Curriculum Designed by Experts

Multisoft Virtual Academy’s AWS Data Engineering Corporate Training is designed to help organizations equip their teams with the essential skills required for building robust data pipelines, processing big data, and managing cloud-based infrastructure on Amazon Web Services. Delivered by certified trainers, this live, instructor-led training focuses on real-time applications, including AWS Glue, Redshift, S3, Lambda, and more.
Whether your team is working on migration, data warehousing, or machine learning projects, this course ensures they understand the architecture, security, best practices, and automation techniques needed to thrive in data-driven roles. The training is fully customizable to match your business requirements and comes with project-based learning, case studies, and expert support for enterprise-scale upskilling.
Multisoft Virtual Academy is offering the AWS Data Engineering Online Training to give in-depth knowledge of AWS Virtuous Cycle, AWS Cloud Economics, Amazon DynamoDB, Kinesis Firehose, Knowledge Checks, DynamoDB Stream, Amazon DynamoDB, EMR Operations, Spark Components, AWS Data Pipeline, RedShift Architecture. Inclined to the AW Certified Data Analytics exam, the offered course is ideal for Solutions Architects, Data Scientists, Data Engineers, and Data Analysts.
- 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

- 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

- 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

- 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

- 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

- 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

- 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

- 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

- 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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Free AWS Data Engineering Corporate Training Assessment
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AWS Data Engineering Corporate Training Certification
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