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Cloud Data Engineering

Noah Gift

Discover the principles of data engineering and its role in building scalable, cloud-based systems.
Explore the challenges of the end of Moore's Law and learn to develop distributed systems.
Gain hands-on experience with big data technologies and best practices for implementing solutions.
Learn to build serverless data engineering pipelines and apply effective data governance strategies.
Develop expertise in key data engineering tasks, including ETL, cloud databases, and cloud storage.

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What's inside

Learning objectives

Evaluate best practices for dealing with the end of moore's law
Develop distributed systems applying software engineering best practices
Evaluate best practices for implementing solutions with big data
Analyze best practices in data engineering

Build serverless data engineering systems
Evaluate effective data governance strategies
Develop cloud etl (extract, load, transfer) pipelines
Evaluate best practices for cloud databases and cloud storage

Evaluate best practices for dealing with the end of moore's law
Develop distributed systems applying software engineering best practices
Evaluate best practices for implementing solutions with big data
Analyze best practices in data engineering
Build serverless data engineering systems
Evaluate effective data governance strategies
Develop cloud etl (extract, load, transfer) pipelines
Evaluate best practices for cloud databases and cloud storage

Syllabus

Here is the course structure formatted with bullets for each module:

1. Module 1: Methodologies in Data Engineering (12 hours)

- Videos:

- Introduction and Course Overview (4 minutes)

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what should give you pause

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Develops cloud-based systems, which is standard in industry

Taught by Noah Gift, who are recognized for their work in data engineering

Offers hands-on labs and interactive materials, which help learners apply skills

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Reviews summary

Practical cloud data engineering foundations

According to students, this Cloud Data Engineering course offers a largely positive experience, providing a strong foundation for professionals. Many appreciate the incredibly practical hands-on labs that cover a wide range of AWS and GCP services, helping them build real-world pipelines. The instructor's clarity in explaining complex concepts like distributed systems and serverless architectures is frequently highlighted. While generally considered up-to-date and highly relevant for modern data workflows, some older feedback pointed to outdated UI examples or a rapid pace in certain modules. Students note its comprehensive nature in covering topics such as ETL pipelines and data governance, though a few felt some crucial topics could benefit from more depth or clearer lab instructions.

Instructor effectively explains complex technical concepts.

"The instructor explains complex concepts with great clarity, especially on distributed systems and serverless architectures."

"The instructor's passion for the subject shines through."

"I found the instructor very knowledgeable and clear in their delivery."

Course provides directly applicable skills for professional roles.

"It truly prepared me for real-world scenarios."

"Directly applicable to professional data engineering roles."

"I learned how to use practical tools and strategies that I could apply immediately to my work."

Covers a broad spectrum of essential cloud data engineering topics.

"This course provided a superb foundation... covering topics like BigQuery and AWS Glue."

"It's comprehensive, well-taught, and directly applicable to professional data engineering roles."

"The course covers a lot of ground in cloud data engineering... especially for those new to the cloud aspects."

Provides practical experience building real-world data pipelines.

"The hands-on labs are incredibly practical and cover a wide range of AWS and GCP services, allowing me to build real-world pipelines."

"The ungraded labs were challenging but rewarding, reinforcing every lesson."

"I learned how to apply practical tools and strategies that I could apply immediately to my work."

Opinions vary on lab stability and the freshness of content/UI examples.

"Some lab setups felt a bit finicky, requiring external troubleshooting."

"Many labs were broken or required significant debugging on my end, which was not productive."

"Some of the videos felt a bit outdated in terms of UI for AWS services, which quickly changes."

"The content felt very up-to-date, which is essential in this fast-moving field."

Some modules felt rushed or lacked sufficient depth for certain topics.

"While it introduces many concepts, some crucial topics felt rushed or lacked the deep dive necessary for true mastery."

"I found this course somewhat challenging due to the rapid pace and assumption of prior knowledge in certain areas."

"The course tries to cover too many topics superficially without providing enough hands-on guidance."

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 Cloud Data Engineering with these activities:

Create a Comprehensive Study Guide

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Organize, expand, and review your notes, assignments, quizzes, and exams to create a comprehensive study guide that consolidates your learning from the course.

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Gather all your course materials, including notes, assignments, and exams.
Organize and categorize the materials by topic and subtopic.
Rewrite and expand your notes, ensuring clarity and completeness.
Include additional examples, explanations, and resources to enhance understanding.

Review Cloud Computing Basics

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Review the fundamentals of cloud computing, including key concepts such as virtualization, cloud storage, and service models.

Browse courses on Cloud Computing

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Read articles and tutorials on cloud computing concepts.
Complete online courses or workshops on cloud computing basics.
Practice hands-on exercises using cloud platforms such as AWS or GCP.

Follow Tutorials on Serverless Data Engineering

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Enhance your knowledge of serverless data engineering by following guided tutorials and demonstrations provided by industry experts and platforms like AWS or GCP.

Browse courses on Cloud Computing

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Identify and enroll in online tutorials on serverless data engineering.
Follow the tutorials step-by-step, taking notes and practicing the concepts.
Build and deploy serverless data pipelines using cloud services.

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ETL Code Challenges

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Practice your ETL skills by solving code challenges and exercises that require you to design and implement ETL pipelines using cloud-based tools.

Browse courses on ETL

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Find ETL code challenges and exercises online or on platforms like LeetCode.
Attempt to solve the challenges on your own.
Review solutions and best practices.
Implement ETL pipelines using cloud-based tools such as AWS Glue or GCP Dataflow.

Read 'Designing Data-Intensive Applications'

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Gain insights into the principles and patterns for designing and building data-intensive applications, which are essential for effective data engineering.

View Secret Colors on Amazon

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Read the book thoroughly, taking notes and highlighting key concepts.
Summarize the main ideas and principles discussed in the book.
Apply the concepts to your own data engineering projects and designs.

Design and Implement a Data Governance Framework

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Develop a data governance framework for your organization, ensuring that data is managed and protected effectively and in compliance with regulatory requirements.

Browse courses on Data Governance

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Research data governance best practices and standards.
Define the scope, principles, and policies of the data governance framework.
Design and implement data governance processes and procedures.
Monitor and enforce compliance with the framework.

Contribute to an Open-Source Data Engineering Project

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Contribute to the development and maintenance of open-source data engineering tools and projects to enhance your understanding and practical skills.

Browse courses on Open Source

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Identify a suitable open-source data engineering project on platforms like GitHub.
Review the project's documentation and contribute to documentation improvements.
Fix bugs or implement new features based on project requirements.
Collaborate with other contributors and maintainers.

Mentor Junior Data Engineers

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Share your knowledge and expertise by mentoring junior data engineers, providing guidance and support to help them develop their skills and grow their careers.

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Connect with junior data engineers through platforms or networking events.
Provide mentorship and guidance on technical skills, career development, and industry trends.
Review code, provide feedback, and suggest improvements.
Encourage and support their professional growth.

Career center

Learners who complete Cloud Data Engineering will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Cloud Data Engineering.

Designing Data-Intensive Applications

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Is widely considered a must-read for anyone working with distributed systems. It provides a comprehensive overview of the fundamental concepts and challenges in building data-intensive applications, which form the basis of many distributed systems. It's highly valuable as a reference tool and is often recommended as essential reading for both students and professionals.

Cloud Data Engineering

What's inside

Learning objectives

Syllabus

Traffic lights

Save this course

Reviews summary

Practical cloud data engineering foundations

Activities

Career center

Reading list

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