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Noah Gift
Welcome to the third course in the Building Cloud Computing Solutions at Scale Specialization! In this course, you will learn how to apply Data Engineering to real-world projects using the Cloud computing concepts introduced in the first two courses of this series. By the end of this course, you will be able to develop Data Engineering applications and use software development best practices to create data engineering applications. These will include continuous deployment, code quality tools, logging, instrumentation and monitoring. Finally, you will use Cloud-native technologies to tackle complex data engineering solutions.
Read more
Welcome to the third course in the Building Cloud Computing Solutions at Scale Specialization! In this course, you will learn how to apply Data Engineering to real-world projects using the Cloud computing concepts introduced in the first two courses of this series. By the end of this course, you will be able to develop Data Engineering applications and use software development best practices to create data engineering applications. These will include continuous deployment, code quality tools, logging, instrumentation and monitoring. Finally, you will use Cloud-native technologies to tackle complex data engineering solutions.
Read more
Welcome to the third course in the Building Cloud Computing Solutions at Scale Specialization! In this course, you will learn how to apply Data Engineering to real-world projects using the Cloud computing concepts introduced in the first two courses of this series. By the end of this course, you will be able to develop Data Engineering applications and use software development best practices to create data engineering applications. These will include continuous deployment, code quality tools, logging, instrumentation and monitoring. Finally, you will use Cloud-native technologies to tackle complex data engineering solutions.
This course is ideal for beginners as well as intermediate students interested in applying Cloud computing to data science, machine learning and data engineering. Students should have beginner level Linux and intermediate level Python skills. For your project in this course, you will build a serverless data engineering pipeline in a Cloud platform: Amazon Web Services (AWS), Azure or Google Cloud Platform (GCP).
Register for this course and see more details by visiting:
OpenCourser.com/course/bi4ac5/cloud
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What's inside
Syllabus
Getting Started with Cloud Data Engineering
This week, you will learn about the methodologies involved in Data Engineering. You will also learn to evaluate best practices for dealing with the end of Moore’s Law, develop distributed systems that apply software engineering best practices and evaluate best practices for implementing solutions with Big Data. You will apply these practices to build a GPU programming project using Numba and the CUDA SDK.
Read more
Syllabus
Getting Started with Cloud Data Engineering
This week, you will learn about the methodologies involved in Data Engineering. You will also learn to evaluate best practices for dealing with the end of Moore’s Law, develop distributed systems that apply software engineering best practices and evaluate best practices for implementing solutions with Big Data. You will apply these practices to build a GPU programming project using Numba and the CUDA SDK.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Sets up a strong foundation for intermediate learners
Builds a strong foundation for beginners
May require foundational skills in Linux and Python
Instructs students with clear learning objectives
Provides comprehensive coverage of cloud data engineering
Teaches industry best practices for data engineering
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Reviews summary
Practical cloud data engineering for professionals
According to students, this course offers an incredibly practical and hands-on approach to cloud data engineering, enabling them to build real-world projects and apply concepts immediately. Many appreciate its strong foundation in software engineering best practices, including continuous deployment and data governance, and find the content highly relevant and current. While the instructors are seen as experts making complex topics approachable, some learners note the pace can be fast and it assumes a strong technical background, particularly in Python and potentially GPU programming, making it challenging for beginners.
Instructors are knowledgeable and clarify complex topics effectively.
"The instructor's explanations were clear and the content was up-to-date."
"The instructors are clearly experts and make complex topics approachable."
"Instructor engagement was good."
Covers highly relevant and current topics for cloud data engineering.
"A solid introduction to cloud data engineering. The modules on ETL and cloud databases were particularly useful."
"I found the content largely relevant to current industry needs. The section on data governance was a pleasant surprise and very relevant."
"This course truly delivers on its promise of practical cloud data engineering... and the material was current."
Strong emphasis on real-world application through hands-on projects.
"This course was incredibly practical and hands-on. The projects, especially building the serverless data engineering pipeline in AWS, were invaluable."
"Excellent course! The practical application of concepts through projects was a game-changer. I particularly enjoyed the AWS Rekognition API lab."
"The hands-on components using AWS were stellar. I learned a ton and immediately applied some concepts at work."
Some found the course organization and debugging support lacking.
"I found this course somewhat disorganized. The jump from theory to practical application sometimes felt abrupt..."
"...some explanations for the projects were not as detailed as I hoped. I struggled with debugging some of the labs and felt there wasn't enough support on the forum."
"I experienced some minor issues with lab environments occasionally, but generally it was workable."
This specific module was challenging and felt out of place for some.
"The GPU programming part, as I'm more of a beginner in that area, was quite difficult for me."
"The GPU programming section felt a bit out of place with the overall data engineering theme for me."
"I found some parts felt disorganized, and the jump to practical application sometimes felt abrupt."
Fast-paced course requires a strong technical and Python foundation.
"The practical assignments were a bit challenging for me... It felt like some topics assumed more background than the stated prerequisites."
"I found the pace very fast, and some concepts were introduced without enough foundational context... It's definitely for those with a strong technical background."
"The Python skills assumed are definitely intermediate, so be prepared... maybe more guidance for less experienced learners."
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:
Organize and Review Course Materials
Show steps
Prepare for the course by organizing and reviewing the provided materials.
Show steps
-
Review the course syllabus and schedule
-
Obtain and organize lecture notes, readings, and assignments
-
Preview the materials and identify areas for clarification
-
Create a study plan and schedule
Review cloud computing concepts
Show steps
Refreshing your knowledge of cloud computing concepts will provide a stronger foundation for this course.
Browse courses on
Cloud Computing Concepts
Show steps
-
Review the syllabus for the first two courses in this series to recap the concepts covered.
-
Visit the course website to review the course syllabus and any introductory materials.
-
Read through the textbooks for the first two courses to brush up on key concepts.
Follow Cloud Data Engineering Best Practices Tutorials
Show steps
Enhance your knowledge and skills by following guided tutorials from industry experts.
Show steps
-
Identify reputable sources for Cloud Data Engineering tutorials
-
Select tutorials that align with your learning objectives
-
Follow the tutorials step-by-step and experiment with the provided examples
-
Implement the learned techniques in your own projects
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Solve Cloud Data Engineering Coding Challenges
Show steps
Reinforce Data Engineering concepts by solving hands-on coding challenges.
Show steps
-
Find online coding challenges related to Cloud Data Engineering
-
Attempt to solve the challenges independently
-
Review solutions and identify areas for improvement
-
Practice regularly to enhance problem-solving skills
Build a Serverless Data Engineering Pipeline
Show steps
Build a serverless data engineering pipeline to gain practical experience and apply course concepts.
Show steps
-
Choose a cloud platform (AWS, Azure, or GCP)
-
Design and architect the pipeline
-
Implement the pipeline using serverless technologies
-
Test and validate the pipeline
-
Deploy and monitor the pipeline
Practice building data engineering pipelines
Show steps
Practicing building data engineering pipelines will help you develop the skills necessary for this course.
Browse courses on
Data Engineering
Show steps
-
Set up a development environment with the necessary tools and software.
-
Find a dataset to work with.
-
Design and implement a data engineering pipeline to process the data.
Follow tutorials on data engineering best practices
Show steps
Following tutorials on data engineering best practices will help you learn the best approaches to use in your projects.
Show steps
-
Search for tutorials on data engineering best practices.
-
Follow the steps in the tutorials to learn how to implement best practices in your own projects.
Join Data Engineering Study Groups
Show steps
Collaborate and learn from peers to enhance your understanding and retention.
Show steps
-
Find or create a study group with other learners
-
Establish regular meeting times for discussion and review
-
Share knowledge, insights, and resources
-
Work on projects together to apply course concepts
Develop Data Engineering Cheat Sheets
Show steps
Create your own reference materials to aid in your understanding and recall.
Show steps
-
Identify key concepts and techniques covered in the course
-
Create concise and informative cheat sheets summarizing these topics
-
Use diagrams, examples, and concise explanations
-
Review and update the cheat sheets regularly
Read documentation and contribute to open source data engineering projects
Show steps
Reading documentation and contributing to open source projects will allow you to learn from the work of others and contribute to the data engineering community.
Browse courses on
Data Engineering
Show steps
-
Find open source data engineering projects that interest you.
-
Read the documentation for the projects to learn about their design and implementation.
-
Identify areas where you can contribute to the projects.
-
Submit your contributions to the project.
Build a serverless data engineering pipeline
Show steps
Building a serverless data engineering pipeline will allow you to apply the concepts you learn in this course to a real-world project.
Browse courses on
Data Engineering
Show steps
-
Choose a cloud platform (AWS, Azure, or GCP) to build your pipeline on.
-
Design and implement your pipeline using serverless technologies.
-
Test and deploy your pipeline.
Participate in a data engineering competition
Show steps
Participating in a data engineering competition will allow you to test your skills against other data engineers and learn from their approaches.
Browse courses on
Data Engineering
Show steps
-
Find a data engineering competition to participate in.
-
Form a team or work individually on the competition.
-
Develop and implement a data engineering solution to the competition problem.
-
Submit your solution and compete for prizes or recognition.
Organize and Review Course Materials
Show steps
Prepare for the course by organizing and reviewing the provided materials.
Show steps
Show steps
Show steps
- Review the course syllabus and schedule
- Obtain and organize lecture notes, readings, and assignments
- Preview the materials and identify areas for clarification
- Create a study plan and schedule
Review cloud computing concepts
Show steps
Refreshing your knowledge of cloud computing concepts will provide a stronger foundation for this course.
Browse courses on
Cloud Computing Concepts
Show steps
Show steps
Show steps
- Review the syllabus for the first two courses in this series to recap the concepts covered.
- Visit the course website to review the course syllabus and any introductory materials.
- Read through the textbooks for the first two courses to brush up on key concepts.
Follow Cloud Data Engineering Best Practices Tutorials
Show steps
Enhance your knowledge and skills by following guided tutorials from industry experts.
Show steps
Show steps
Show steps
- Identify reputable sources for Cloud Data Engineering tutorials
- Select tutorials that align with your learning objectives
- Follow the tutorials step-by-step and experiment with the provided examples
- Implement the learned techniques in your own projects
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Solve Cloud Data Engineering Coding Challenges
Show steps
Reinforce Data Engineering concepts by solving hands-on coding challenges.
Show steps
Show steps
Show steps
- Find online coding challenges related to Cloud Data Engineering
- Attempt to solve the challenges independently
- Review solutions and identify areas for improvement
- Practice regularly to enhance problem-solving skills
Build a Serverless Data Engineering Pipeline
Show steps
Build a serverless data engineering pipeline to gain practical experience and apply course concepts.
Show steps
Show steps
Show steps
- Choose a cloud platform (AWS, Azure, or GCP)
- Design and architect the pipeline
- Implement the pipeline using serverless technologies
- Test and validate the pipeline
- Deploy and monitor the pipeline
Practice building data engineering pipelines
Show steps
Practicing building data engineering pipelines will help you develop the skills necessary for this course.
Browse courses on
Data Engineering
Show steps
Show steps
Show steps
- Set up a development environment with the necessary tools and software.
- Find a dataset to work with.
- Design and implement a data engineering pipeline to process the data.
Follow tutorials on data engineering best practices
Show steps
Following tutorials on data engineering best practices will help you learn the best approaches to use in your projects.
Show steps
Show steps
Show steps
- Search for tutorials on data engineering best practices.
- Follow the steps in the tutorials to learn how to implement best practices in your own projects.
Join Data Engineering Study Groups
Show steps
Collaborate and learn from peers to enhance your understanding and retention.
Show steps
Show steps
Show steps
- Find or create a study group with other learners
- Establish regular meeting times for discussion and review
- Share knowledge, insights, and resources
- Work on projects together to apply course concepts
Develop Data Engineering Cheat Sheets
Show steps
Create your own reference materials to aid in your understanding and recall.
Show steps
Show steps
Show steps
- Identify key concepts and techniques covered in the course
- Create concise and informative cheat sheets summarizing these topics
- Use diagrams, examples, and concise explanations
- Review and update the cheat sheets regularly
Read documentation and contribute to open source data engineering projects
Show steps
Reading documentation and contributing to open source projects will allow you to learn from the work of others and contribute to the data engineering community.
Browse courses on
Data Engineering
Show steps
Show steps
Show steps
- Find open source data engineering projects that interest you.
- Read the documentation for the projects to learn about their design and implementation.
- Identify areas where you can contribute to the projects.
- Submit your contributions to the project.
Build a serverless data engineering pipeline
Show steps
Building a serverless data engineering pipeline will allow you to apply the concepts you learn in this course to a real-world project.
Browse courses on
Data Engineering
Show steps
Show steps
Show steps
- Choose a cloud platform (AWS, Azure, or GCP) to build your pipeline on.
- Design and implement your pipeline using serverless technologies.
- Test and deploy your pipeline.
Participate in a data engineering competition
Show steps
Participating in a data engineering competition will allow you to test your skills against other data engineers and learn from their approaches.
Browse courses on
Data Engineering
Show steps
Show steps
Show steps
- Find a data engineering competition to participate in.
- Form a team or work individually on the competition.
- Develop and implement a data engineering solution to the competition problem.
- Submit your solution and compete for prizes or recognition.
Career center
Learners who complete Cloud Data Engineering will develop knowledge and skills
that may be useful to these careers:
Data Engineer
Data Engineer
A Data Engineer designs and develops data pipelines and systems to manage and analyze large volumes of data. They work closely with data scientists and other stakeholders to ensure that data is accessible, reliable, and secure. This course can help you develop the skills you need to succeed as a Data Engineer, including data engineering principles, best practices, and tools. You will also learn how to build and deploy data engineering pipelines in the cloud.
Data Scientist
Data Scientist
A Data Scientist uses data to solve business problems. They collect, clean, and analyze data to identify trends and patterns. They then use this information to develop models and make predictions. This course can help you develop the skills you need to succeed as a Data Scientist, including data engineering principles, best practices, and tools. You will also learn how to build and deploy data engineering pipelines in the cloud.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs and develops machine learning models. They work closely with data scientists and other stakeholders to ensure that models are accurate, reliable, and scalable. This course can help you develop the skills you need to succeed as a Machine Learning Engineer, including data engineering principles, best practices, and tools. You will also learn how to build and deploy data engineering pipelines in the cloud.
Cloud Architect
Cloud Architect
A Cloud Architect designs and develops cloud-based solutions. They work closely with customers to understand their business needs and then design and implement cloud solutions that meet those needs. This course can help you develop the skills you need to succeed as a Cloud Architect, including data engineering principles, best practices, and tools. You will also learn how to build and deploy data engineering pipelines in the cloud.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. They work closely with customers and other stakeholders to understand their needs and then design and implement software solutions that meet those needs. This course can help you develop the skills you need to succeed as a Software Engineer, including data engineering principles, best practices, and tools. You will also learn how to build and deploy data engineering pipelines in the cloud.
Data Analyst
Data Analyst
A Data Analyst collects, cleans, and analyzes data to identify trends and patterns. They then use this information to develop insights and make recommendations. This course can help you develop the skills you need to succeed as a Data Analyst, including data engineering principles, best practices, and tools. You will also learn how to build and deploy data engineering pipelines in the cloud.
Business Analyst
Business Analyst
A Business Analyst works with businesses to identify and solve problems. They use data and other information to analyze business processes and make recommendations. This course can help you develop the skills you need to succeed as a Business Analyst, including data engineering principles, best practices, and tools. You will also learn how to build and deploy data engineering pipelines in the cloud.
Project Manager
Project Manager
A Project Manager plans, executes, and closes projects. They work closely with stakeholders to define project goals and objectives and then develop and implement project plans. This course can help you develop the skills you need to succeed as a Project Manager, including data engineering principles, best practices, and tools. You will also learn how to build and deploy data engineering pipelines in the cloud.
Product Manager
Product Manager
A Product Manager develops and launches new products. They work closely with customers and other stakeholders to identify and define product requirements. This course can help you develop the skills you need to succeed as a Product Manager, including data engineering principles, best practices, and tools. You will also learn how to build and deploy data engineering pipelines in the cloud.
Sales Engineer
Sales Engineer
A Sales Engineer helps customers to understand and purchase products and services. They work closely with customers to identify their needs and then develop and implement solutions that meet those needs. This course can help you develop the skills you need to succeed as a Sales Engineer, including data engineering principles, best practices, and tools. You will also learn how to build and deploy data engineering pipelines in the cloud.
For more career information including salaries, visit:
OpenCourser.com/course/bi4ac5/cloud
Reading list
We've selected ten 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.
Save
Takes a deep dive into various aspects of big data, including storage, processing, and analysis. It will be a useful reference in examining principles of data engineering.
Covers theoretical and practical aspects of data-intensive text processing using Hadoop and MapReduce. It will be useful as a background reading in text processing for data engineering applications.
Focuses on using Python for data analysis. It will be a useful reference for data engineering applications.
Is an introduction to machine learning using Python libraries. It will be useful as a reference for data engineering applications.
Covers more theoretical aspects of designing scalable systems, and useful as a background reference for data engineering principles.
Save
Is an introduction to database systems. It will be a useful reference for those looking to explore the fundamentals of data storage and retrieval.
Save
Is an introduction to the Spark framework. It will be useful as a background reference or for additional reading.
Save
Covers cloud computing concepts and architectures. It will be a useful reference for those interested in exploring the background of cloud computing beyond the course.
Save
Is an introduction to deep learning. Like Machine Learning Yearning, it is not directly related to data engineering, but may be useful for the course project.
Covers foundations of data science and useful as a background text or reference.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/bi4ac5/cloud
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Effort
4 weeks of study, 3-5 hours/week
Level
Intermediate
Via
Coursera
Institution
Duke University
Instructor
Noah Gift
Language
English
Transcripts
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This course belongs to a
collection
called
Building Cloud Computing Solutions at Scale. It's comprised of four courses:
- Cloud Machine Learning Engineering and MLOps
- Cloud Virtualization, Containers and APIs
- Cloud Data Engineering (viewing)
- Cloud Computing Foundations
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