Chevron Left
返回到 Cloud Data Engineering

學生對 杜克大学 提供的 Cloud Data Engineering 的評價和反饋

55 個評分


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)....

1 - Cloud Data Engineering 的 14 個評論(共 14 個)

創建者 Pineapple P


Material covered mostly on the surface level.

創建者 Joshua S


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

創建者 dave t


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 Some advice provided is out of date or not good advice.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.

創建者 Jesse B


Wow, lots of lectures that discuss tools without actually a mention of the goal(s), objectives of the tools. It seems almost the entire course lost the forest for all the trees/tooling and doesn't present a cohesive theme of what data engineering is about and tools to support that endeavor.

創建者 Maria Y


The course is a mess - there is no continuity, and the clips are basically snippets from different times, i.e. they were not created specifically for this course. The tests are a mess - week 2 tests week 3 knowledge. There is a lot of repetition (been introduced to Cloud9 and Codespaces at least 3 times each) which makes it difficult to concentrate on one thing. the course would be a lot shorter and more efficient if it was filmed with the purpose to create a course, at the moment it feels like just a money-grabbing opportunity.

創建者 Andrés C


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

創建者 Matias L M


Great introduction to the concepts, very good practical examples, with relevant technologies and platforms.

創建者 Taozheng Z


Need to be revised

創建者 Renato M


Great course

創建者 Rogério A


Very important information and concepts was shared.

創建者 Umut A


Very well designed course for data engineering

創建者 dumebi j



創建者 Alejandro A


It doesn't provide labs for AWS

創建者 Holly S


no inbuilt labs