Data Engineering Using AWS Data Analytics is a comprehensive, hands-on training program designed to teach you how to build scalable, efficient, and production-ready data solutions on Amazon Web Services. Whether you're new to cloud data engineering or looking to deepen your expertise, this course covers the full lifecycle of modern data engineering—from ingestion and transformation to analytics and visualization.
 
You’ll explore the core AWS services that power large-scale data processing, including Kinesis, Lambda, Glue, Redshift, S3, Athena, and QuickSight. Through real-world examples and guided labs, you’ll learn how to design data lakes and data warehouses, build batch and streaming pipelines, optimize workloads for performance and cost, and enable your organization to extract meaningful insights from massive datasets.
 
By the end of the course, you’ll be equipped to architect and implement end-to-end cloud data solutions that are efficient, reliable, secure, and aligned with industry best practices.
 
What You’ll Learn
Foundations of AWS data engineering and analytics
Designing and building data lakes and data warehouses on AWS
Ingesting and processing data using Kinesis, Lambda, and Glue
Transforming, aggregating, and enriching data at scale
Implementing optimized pipelines for batch and streaming workloads
Using Redshift for large-scale analytics and warehousing
Visualizing insights with AWS QuickSight
Best practices for cloud security, governance, and cost optimization
 
Requirements
Basic understanding of cloud computing and data engineering concepts
Familiarity with AWS is helpful but not required
Ideal for data engineers, analysts, data scientists, and cloud professionals