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.

Material covered mostly on the surface level.

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.

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

Upon completing this course, I was left feeling rather unsatisfied.

- The content appears to be an amalgamation of material taken from various other courses or screen recordings.

- The instructor frequently made mistakes during the lessons, which were then corrected live, leading to unnecessary time wastage.

- Explanations provided were superficial and lacked the required technical depth.

- Many lessons were simply "follow-along" sessions with a conspicuous absence of diagrams or conceptual discussion about the architecture. This was unexpected, given the course's promise of being provider-agnostic.

- The sample projects lacked engagement, and it took a considerable stretch of the imagination to discern their real-world applications.

In light of these observations, I'd suggest prospective learners to consider a more refined course - I am currently considering the GCP Official Courses. Additionally, I would approach future courses by the same instructor with caution, as I felt this course lacked a carefully curated, cohesive learning experience.

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 Some advice provided is out of date or not good advice.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

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

It doesn't provide labs for AWS

The course, unfortunately, lacks a cohesive structure, presenting itself more as a collection of disparate videos with varying levels of difficulty. As a result, it leaves learners struggling to grasp a comprehensive understanding of data engineering and its practical applications in real-world scenarios. The absence of a clear and organized curriculum hinders the effectiveness of the learning experience, leaving participants without a cohesive and actionable knowledge of data engineering concepts.

Reused materials, overall chaos, mistakes in tests...

However, some useful material is there...hence 2 stars rather than 1!

no inbuilt labs

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

Need to be revised

Great course

thks a lot

good

good

Very important information and concepts was shared.

Very well designed course for data engineering

good