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AWS Data Engineer Certification Training
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About Course
🌐 What Is AWS Data Engineer?
An AWS Data Engineer is a professional who designs, builds, and manages data pipelines and analytical solutions using Amazon Web Services (AWS) cloud technologies. AWS Data Engineering focuses on efficiently collecting, storing, transforming, and analyzing large volumes of structured and unstructured data to enable business intelligence and machine learning. This role combines data engineering, cloud computing, and analytics expertise to deliver scalable, secure, and high-performance data architectures.
Its core capabilities include:
- Data Pipeline Development: Design and orchestrate pipelines using AWS Glue, Lambda, and Step Functions.
- Data Storage Management: Implement data lakes and warehouses with Amazon S3, Redshift, and DynamoDB.
- ETL (Extract, Transform, Load): Automate data ingestion and transformation for analytics.
- Big Data Processing: Utilize EMR, Athena, and Kinesis for real-time and batch processing.
- Security & Monitoring: Apply IAM, CloudWatch, and encryption for secure and optimized operations.
📊 Course Features Typically Included
- ✅ Live instructor-led sessions and recorded tutorials
- ✅ Hands-on labs using real AWS environments
- ✅ Real-world projects on data lakes, ETL pipelines, and analytics
- ✅ Certification and interview preparation guidance
- ✅ Lifetime access to course materials and updates
- ✅ Practical exercises on AWS Glue, Redshift, and S3
🎓 Key Learning Outcomes
After completing the AWS Data Engineer Online Training, learners will be able to:
- Understand AWS data architecture and services for analytics
- Build data pipelines and integration workflows
- Implement data lakes and warehouses on AWS
- Optimize ETL processes using AWS Glue and Lambda
- Perform real-time data streaming and analytics using Kinesis and Athena
- Secure and monitor AWS data resources effectively
These skills prepare learners for in-demand roles such as:
AWS Data Engineer, Big Data Engineer, Cloud Data Architect, ETL Developer, Data Pipeline Specialist
📍 Bonus: Certification Tracks
- AWS Certified Data Engineer – Associate
- AWS Certified Solutions Architect – Associate
- AWS Certified Big Data – Specialty (Legacy
Viswa Online Trainings – AWS Data Engineer Professional Certification
AWS Data Engineer Training Course Syllabus
AWS Data Engineer 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
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
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
Processing 1
- 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
- 1Lesson End Project
Processing 2
- 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
Analysis 1
- 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
Analysis 2
- 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
Visualization
- 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
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
AWS Data Engineer Course Key Features
- Live Instructor based training with software
- Certification Oriented content
- Hands-on complete Realtime training
- Flexible schedule demo's & classes
- Live recorded videos access
- Study material provided
- JOB Assistance
Course completion certificate
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- Instructor-Led training real time projects
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- Complete set of live-online training session recorded videos.
- Learn technology at your own pace.
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- Learn as a full day schedule with discussions, exercises.
- Practical use cases.
- Design your own syllabus based
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AWS Data Engineer Online Training FAQ'S
What does a Data Engineer do on AWS?
- A Data Engineer designs and maintains data pipelines, ETL processes, and data storage solutions using AWS services
Which AWS services are commonly used for Data Engineering?
- Key services include S3, Glue, Redshift, EMR, Kinesis, Lambda, DynamoDB, and Athena.
What is the difference between Redshift and Athena?
- Redshift is a data warehouse for structured, large-scale queries, while Athena is a serverless query engine for analyzing S3 data using SQL.
How does AWS Glue help in Data Engineering?
- AWS Glue is a serverless ETL service that automates schema discovery, data cataloging, and transformation jobs.
What is a Data Lake on AWS?
A Data Lake is a centralized repository built on Amazon S3 that stores structured and unstructured data for analytics and machine learning.
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