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Voltar para Cloud Data Engineering

Comentários e feedback de alunos de Cloud Data Engineering da instituição Universidade Duke

32 classificações
10 avaliações

Sobre o curso

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)....
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1 — 10 de 10 Avaliações para o Cloud Data Engineering

por Joshua S

5 de nov de 2021

They should add more examples using the tools and services, however Iearned a lot of thing related to data engineering an the aws cloud, I think this knowledge is going to be very useful in my professional dimesion

por dave t

10 de abr de 2022

I am only on week 1 but already I have lots of suggestions. This course could be improved by

1. If you use an acronym explain what it stands for.

2. Make it very clear where the gists, github repos are.

3. Some advice provided is out of date or not good advice. Debugging lambdas can be done using other approaches which have zero cost and do not tie you into a specific vendor (e.g. GitHub ). I have left comments in the discusssion forum on this.

4. Improve the explanations and content , testing isn't covered at all well. Why is Pytest ( a dependency) better than Unittest (a cor elibrary) I agree its beter but what if I do not know, what should I test and how. All this wasn't covered and it wasn't made clear I should know it.

5. Improve editing , in some lectures the lecturer is answering questions and there are a lot of gaps between the answers and unheard questions.

6. Imporve the delivery, sometimes the explanations provided are unclear or lack good reasons why the course of action is being taken.

7. Provide more hands on labs.

por Pineapple P

24 de jul de 2021

Material covered mostly on the surface level.

por Taozheng Z

2 de jul de 2021

Need to be revised

por Renato M

15 de jun de 2022

G​reat course

por Rogério A

31 de dez de 2021

Very important information and concepts was shared.


16 de nov de 2021


por Andrés C

13 de nov de 2021

A lot of reused material, a lot of random stuff and just jumping to examples without actually giving context.

por Holly S

5 de mai de 2022

no inbuilt labs

por Kenneth N

27 de jun de 2022

feels like watching youtube videos