Overview

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Build a strong foundation in data engineering concepts and AWS distributed storage, focusing on S3 and essential data modeling techniques. Learn how to design, secure, and optimize storage solutions for analytics and reporting. This course introduces the core principles of data engineering, including OLTP, OLAP, data marts, data lakes, and data lakehouse architectures. Learners will explore AWS S3 in depth, covering storage classes, security, performance, and architectural patterns. The course also covers data modeling fundamentals such as normalization, ER diagrams, and dimensional modeling, preparing learners to structure data effectively for analytics workloads. Learners progress through a blend of conceptual explanations and practical demonstrations, focusing on real-world scenarios and AWS best practices. The course emphasizes understanding foundational concepts and applying them to design robust, secure, and scalable storage and modeling solutions. This course is part one of a three-course Specialization designed to build a complete and cohesive understanding of the subject. While it offers valuable skills on its own, you'll gain the most benefit by progressing through all three courses as a structured learning journey. This course is based on Data Engineering on AWS Vol 1 - OLAP & Data Warehouse, by Soumyadeep Dey. This course is licensed and distributed by Packt. All rights reserved. Packt is one of the world's most prolific publishers of cutting-edge technical content. For over two decades we've made it our mission to curate and publish the knowledge of only the very best technical experts. We focus on real-world courses that help our customers get the job done, with coverage that extends across a wide range of established and cutting-edge technical topics. If you're an individual or an organisation that embraces learning by doing, Packt is the perfect fit for you.

Syllabus

Introduction - Data Engineering Volume 1 on AWS
- This module introduces the fundamental concepts of data engineering on AWS, outlining the course structure, learning goals, and practical applications. Learners will gain an understanding of the course workflow and how to apply key concepts in real-world scenarios. It serves as a foundation for deeper exploration of AWS data engineering tools and techniques.
Data Engineering Introduction
- This module introduces key data engineering concepts, including data systems like OLTP and OLAP, and explores modern data architectures such as data lakes, data marts, and data mesh. Learners will gain an understanding of how these technologies support data processing and decision-making in organizations.
AWS Distributed Storage - S3 (Simple Storage Service) for Data Engineers
- This module provides a comprehensive overview of Amazon S3, focusing on object storage principles, security measures, performance optimization, and integration with cloud architectures. Learners will gain hands-on experience with key S3 features like versioning, encryption, and lifecycle policies. The content prepares data engineers to design and manage scalable, secure, and cost-effective storage solutions on AWS.
Data Modelling - Normalization, ER Diagram, Dimensional Modelling
- This module introduces key concepts in data modeling, including normalization, entity-relationship diagrams, and dimensional modeling. Learners will explore how to design efficient database structures and understand the role of facts, dimensions, and grains in data warehousing. The content equips students with foundational skills for building relational and non-relational database schemas.

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Packt - Course Instructors

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