AWS Academy Data Engineering

Ready to power data-driven decisions? Develop practical skills in designing, building, and managing scalable data pipelines in this comprehensive data engineering course. 

Course Description

The online, self-guided course is for students who want to learn about the tasks, tools, and strategies used to collect, store, prepare, analyze, and visualize data for use in analytics and machine learning (ML) applications. 

Content is delivered through pre-recorded videos, student guides, instructional labs, and knowledge checks. Students will explore use cases from real-world applications, enabling them to make informed decisions while building data pipelines for their applications. The course Capstone project provides an integrative project-based learning experience to reinforce the course’s technical skills. 

This course introduces you to many aspects of data analytics and machine learning. It helps you identify the next steps to prepare for the AWS Certified Data Engineer – Associate and AWS Certified Machine Learning – Specialty certifications. You will learn about the training resources available to help you prepare for the certification exams. 

Want a bigger picture of your potential AWS Academy Certification journey? Check out other AWS Academy courses and potential certification pathways. Stay tuned for these course launches! 

The University of Denver is proud to be a member institution of the AWS Academy, giving students access to official Amazon Web Services (AWS) curriculum and learning resources that prepare them for industry-recognized certifications.

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Who should take this course?

  • Students and aspiring data engineers with foundational technical knowledge ready to build expertise in data engineering 

  • Data analysts and data scientists seeking to understand complete data pipeline architecture and implementation 

  • Career changers and technical practitioners with programming experience transitioning into data engineering, ETL development, or ML practitioner roles 

Course Details

Requirements

This is an intermediate-level course that requires a strong foundation in IT concepts and skills. To help ensure your success in this online, self-guided course, the following general competencies are recommended:

  • Worked with Structured Query Language (SQL) 

  • Worked with databases 

  • Introduced to general networking concepts 

  • Understanding of decision-making knowledge in math, probability, and statistics 

Format

Designed with your busy schedule in mind, this course is:  

  • 100% online: Learn from anywhere 

  • Self-guided: Built for self-starters who want to drive their own learning experience and work at their own pace 

  • 40 hours of content: Advance your skills and accelerate your career 

  • Skills-Based: Earn a digital badge by scoring 70%+ on graded activities 

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Ready to Enroll?

Sessions start monthly! Enroll and begin your course anytime within that month. You’ll have 5 months of access to course materials.

AWS Academy Data Engineering - January

Professional and Workforce Development (PWD)
$400.00 Starts 01/01/2026

AWS Academy Data Engineering - February

Professional and Workforce Development (PWD)
$400.00 Starts 02/02/2026

AWS Academy Data Engineering - March

Professional and Workforce Development (PWD)
$400.00 Starts 03/02/2026

What You'll Learn

  • Outcomes

    When you’ve completed this course, you should be able to: 

    • Summarize the role and value of data science in a data-driven organization. 

    • Recognize how the elements of data influence decisions about the infrastructure of a data pipeline. 

    • Illustrate a data pipeline by using AWS services to meet a generalized use case. 

    • Identify the risks and approaches to secure and govern data at each step and each transition of the data pipeline. 

    • Identify scaling considerations and best practices for building pipelines that handle large-scale datasets. 

    • Design and build a data collection process while considering constraints such as scalability, cost, fault tolerance, and latency. 

    • Select a data storage option that matches the requirements and constraints of a given data analytics use case. 

    • Implement the steps to process structured, semistructured, and unstructured data formats in a data pipeline that is built with AWS. 

    • Explain the concept of MapReduce and how Amazon EMR is used in big data pipelines. 

    • Differentiate the characteristics of an ML pipeline and its specific processing steps. 

    • Analyze data by using AWS tools that are appropriate to a given use case. 

    • Implement a data visualization solution that is aligned to an audience and data type. 

  • Course Outline

    • Module 1: Welcome to AWS Academy Data Engineering 

    • Module 2: Data-Driven Organizations 

    • Module 3: The Elements of Data 

    • Module 4: Design Principles and Patterns for Data Pipelines 

    • Module 5: Securing and Scaling the Data Pipeline 

    • Module 6: Ingesting and Preparing Data 

    • Module 7: Ingesting by Batch or by Stream 

    • Module 8: Storing and Organizing Data 

    • Module 9: Processing Big Data 

    • Module 10: Processing Data for ML 

    • Module 11: Analyzing and Visualizing Data 

    • Module 12: Automating the Pipeline 

    • Module 13: Bridging to Certification 

    • Capstone Project

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AWS Skill Builder Access

Enroll today and receive a complimentary 12-month subscription to AWS Skill Builder. This platform of over 600 self-paced courses, labs, and certification prep resources is designed to reinforce what you've learned and help you prepare for your AWS certification exam.