This course was developed by members of AWS Technical Field Communities (TFC), an AWS community of technical experts. The content is intended to complement our standard training curriculum and augment your AWS learning journey. We are aware some courses have accessibility limitations and are working to address. If you require accommodation, please contact AWS Training and Certification Customer Support.
This course provides a comprehensive exploration of data engineering principles and practices for building generative AI applications on AWS. The course begins with the AWS Modern Data Strategy, covering how to align data initiatives with business goals through KPIs, assess organizational data maturity across four stages (transactional to data-driven), and execute strategy across people, process, and technology dimensions. Participants will learn the AWS Modern Data Architecture built on five pillars - scalable data lakes, purpose-built analytics services, unified data access, unified governance, and cost-effectiveness - and its five core components: databases, data lakes, analytics, machine learning, and data governance. The course explores the AWS Data Processing Pipeline covering ingestion, storage, processing, analytics, and insights stages with integrated security and governance throughout.
The course discusses Zero-ETL integration, demonstrating how to minimize or eliminate complex ETL pipelines by enabling near real-time data movement between systems like Amazon Aurora, Amazon RDS, Amazon DynamoDB, and Amazon Redshift without custom code. The course concludes with modern cloud-native applications, exploring the shift from rule-based to AI-native orchestration using Strands Agents, Amazon Bedrock AgentCore, and Amazon S3 Tables for building autonomous, intelligent data systems. Through hands-on demonstrations including Zero-ETL setup from Aurora MySQL to Redshift, Amazon SageMaker Unified Studio for collaborative data and AI development, and AI agents automating document processing workflows, attendees will gain practical skills for building modern data foundations that power generative AI applications.
