Data Engineering on AWS - Foundations from Coursera

In this course, you will learn essential data engineering concepts such as what does a data engineer do, how to perform data discovery, and which AWS data services are available for running, automating, and securing your data solutions and projects. The course introduces you to data engineering tools for continuous integration and continuous delivery (CI/CD), infrastructure as code (IaC), serverless applications, cost optimization, and authentication. You will also learn what networking concepts should be taken into consideration for data engineers.

The course is divided into different modules. With the learning modules, you will learn new concepts and get introduced to AWS services to build your solution.

In this course you will learn to:

- Describe the data engineering role and personas they work with.

- Identify how to perform data discovery to help define requirements and review considerations.

- Identify common data architectures and appropriate AWS services.

- Recognize how to orchestrate and automate data pipelines using AWS services.

- Identify AWS security and monitoring services.

- Identify common CI/CD tools used by data engineers on AWS.

- Identify IaC tools used by data engineers on AWS.

- Describe AWS Serverless Application Model (AWS SAM).

- Identify AWS cost optimization tools.

- Identify different AWS security mechanisms.

What's inside

Syllabus

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Introduces essential data engineering concepts for professionals with various backgrounds

Suitable for individuals seeking to enhance their understanding of AWS services for data engineering

Covers fundamental topics, serving as a strong foundation for aspiring data engineers

AI and ML are not explicit focuses of this course

Reviews summary

Solid foundation in aws data engineering

Learners say this course provides a solid foundation in data engineering on AWS, especially for beginners. Many highlight the well-structured content and clear explanations that make complex AWS services accessible. The hands-on labs and practical examples are frequently praised for solidifying understanding and offering valuable real-world application. While the course covers a broad range of essential AWS data services, some learners with prior experience note that it maintains a foundational level and could benefit from more in-depth coverage for intermediate or advanced topics.

Well-organized with clear and accessible explanations.

"The content is well-structured and easy to follow, making complex topics accessible."

"The logical flow of modules, from data discovery to CI/CD, was very intuitive."

"The explanations are clear, and the real-world examples given truly help in grasping the concepts."

Labs offer valuable hands-on experience and solidify learning.

"The labs were particularly helpful, providing hands-on experience that solidified my understanding."

"The labs were practical and guided me through setting up real AWS services."

"The practical labs are excellent for learning by doing."

An excellent starting point for AWS data engineering.

"This course is an excellent starting point for anyone interested in data engineering on AWS."

"Absolutely brilliant! As someone new to AWS, this course demystified so many concepts."

"The best foundational course for AWS Data Engineering! Covers all the essentials from discovery to CI/CD."

Minor issues with some older labs requiring adjustments.

"My main critique is that some of the older labs or demos seemed slightly outdated, requiring minor adjustments to follow along..."

Some sections could be more engaging and less theoretical.

"I found some sections to be quite dry and theoretical. The delivery could be more engaging."

"My main critique is that sometimes the pace felt uneven, and some sections could use more practical applications."

"Some parts felt a bit rushed, though."

May lack depth for intermediate or advanced learners.

"I felt some topics could have gone into more depth, especially for intermediate learners."

"I was looking for more advanced use cases and practical problem-solving scenarios, but it stayed mostly at a foundational level."

"If you have any prior experience, you might find it superficial. The hands-on parts were okay, but not challenging enough."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Engineering on AWS - Foundations with these activities:

Organize Course Materials

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Enhance your learning experience by organizing course materials, notes, and assignments to improve your retention and recall.

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Create a system for organizing notes, assignments, and quizzes.
Review and summarize key concepts from each module.
Identify areas where additional research or practice is needed.

Seek Guidance from Experienced Data Engineers

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Enhance your learning by connecting with experienced data engineers who can provide valuable insights and mentorship.

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Attend industry events or conferences to meet data engineers.
Reach out to data engineering professionals on LinkedIn or other networking platforms.
Request informational interviews to learn from their experiences and advice.

Explore AWS Data Services

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Supplement your learning by exploring tutorials on AWS data services to enhance your understanding of their functionality.

Browse courses on AWS Cloud Services

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Search for tutorials on AWS S3, Redshift, and Athena.
Follow step-by-step guides to create and manage data pipelines using AWS services.
Experiment with different AWS data services to gain practical experience.

Two other activities

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Solve Data Engineering Challenges

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Sharpen your data engineering skills by solving real-world challenges to reinforce your understanding of the course material.

Browse courses on Data Transformation

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Find online platforms or resources that provide data engineering challenges.
Attempt to solve challenges related to data ingestion, transformation, and analytics.
Analyze your solutions and identify areas for improvement.

Mentor Junior Data Engineers

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Reinforce your understanding by mentoring junior data engineers, answering their questions, and sharing your knowledge.

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Volunteer to mentor junior data engineers through online forums or organizations.
Provide guidance on data engineering concepts, tools, and best practices.
Review and critique their work, offering constructive feedback.

Career center

Learners who complete Data Engineering on AWS - Foundations will develop knowledge and skills that may be useful to these careers:

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Data Engineering on AWS - Foundations

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Reviews summary

Solid foundation in aws data engineering

Activities

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