Key concepts like data lake, warehouse, or ETL/ELT/EL need to be explained more precisely and in stand-alone modules. Many modules/videos repeat some information on previous topics and add some new information on that topic which gives insight and is valuable but this information is not present in the video about the specific topic. This makes it more difficult to get a good foundation of the key concepts. The labs need to be more interactive and show a more diverse set of scenarios for the given concept at hand. The demos are too unorganised, the view of the GCP interface is too small to see what is going on. The demos should probably be labs instead.

The course offers a nice description of what modern DWH means, which are the differences between classic DWH and cloud DWH. You have the chance to work with the new cloud concept on GCP.

The course is not engaging. I watched "big-picture" videos. I don't need to discuss the theory of GCP. I enrolled in the course to get my hands dirty. The labs are not exercises; they are recipes to be blindly followed. I suspect my retention to be limited due to the course. The pro - GCP is a great tool!

Demo's are presented by persons that click and scroll and click and scroll while mumbling along. Really worthless. Transcriptions below video's are full of misspellings and buggy sentences. A 'data lake' is called a 'data leg', 'disk writes' are called 'disk rights' and many, many more. Check for yourself and have a good laugh, or a cry is your career depends on this kind of rubbish. PROOF that Coursera doesn't care a fig for your learning. They never checked there product before publishing. Probably trusted Google ML/AI.

I am not able to do the labs because of some qwiklabs bugs

Very detailed explanation on Data Lake and Data Ware house and use cases. Concepts of the Data types such as STRUCT and ARRAYS are explained very well and beneficial in Data modeling.

Simply Extraordinary !! The way course took made me finish entire course with a continuous flow.Kudos to course faculty.

Limited timing on labs stresses you out and doesn't let you discover enough about various functionalities. Some links are outdated ( data engineering course folder under Evan Jones' Github does not exist on Github but shown in his video)

1 punto

Case studies in this course are intended to develop the skill of defining the solution while analyzing the circumstance. This is a key test-taking and job skill.

Practice Exam Questions help develop the skill of being aware of how certain you are of an answer. This is not only a test-taking skill and a job skill, but also helps you understand where you may want to study more to prepare.

This course provided an exhaustive list of basic principles and concepts and tested you repeatedly on your ability to remember them.

This course introduced "touchstone" concepts that are based on many fundamental concepts. If you don't feel confident about a "touchstone" concept, it is an indicator that you might want to study the underlying concepts and technologies.

2.

Great course that is organised in a way that makes the concepts easy to understand. Clustering is still a little confusing in terms of how it actual works behind the scenes, but how implement it and the value it adds in making quires efficient is crystal clear.

Without a doubt the best course, I have learned a lot not only from GCP but from many aspects of cloud computing and the skills necessary for a data engineer. Thank you very much for the opportunity.

Good introduction and overview of the field of data engineering, data lakes and modern data warehouses and a hands-on walkthrough of all the technologies related to solving these problems on GCP.

A better understanding of BigQuery starts here. A vital resource for consultants, data analysts, and product managers, and an important reference source for engineers and data scientists.

Lectures are much too fast. Lecturers are talking very indistinctly and subtitles are incorrect in those places. Additionally majority videos are just recording of someone talking without any visual helps - slides are not very useful. I gave 2 stars, because some labs were pretty cool to do.

Week 1 was great, however, the videos for week 2 were really hard to follow. It could be because of the way it was read off of the prompter. I really struggled staying focus.

Video quality and content could be improved. Videos (especially for week 2) felt very monotonous.

Too easy...

Paid for this course and received certificate, but now I am no longer paying the monthly subscription can no longer access course materials. What a complete waste of money.

Coursera has no real support system that I can see to raise this with, appears to be largely community of FAQ based. Seems they have turned it into a cash making cow with as little support as possible.

None of the Qwiklabs are accessible, even when accessing them in an Incognito browser. Very frustrating. The material is outdated.

Big Query part was very clear and how to leverage it for data warehousing. While using Cloud Storage for Data Lake was at a high level. Currently Data Lake often have many laters and unless latency is a big issues data is curated and transformed into enterprise layers in a data lake itself. A more realistic use case of data lake that has already multiple layers of ETL would have helped.

Highly recommended course for any data engineer. In addition to an introduction to data engineering, this course builds an awareness on data warehousing and goes about it in an extremely user-friendly way by demonstrating the whole thing on the GCP. Of course, programming in sql should be learnt through a dedicated course on the subject, as this course provides the required code.

Really useful, here you learn how to properly use BigQuery, and how to calculate and monitor pricing of its usage.

It's highly recommended having a solid foundation of SQL and database basic design before deep diving into BigQuery.

The Labs were pretty instructive and detailed, I will repeat the course for sure to practice and further interiorize what I've learn.

Nice module! Harder than the previous course, but as long as you follow everything carefully and keep good notes, it's manageable. Very helpful labs with more complex subject matter e.g. Arrays, nested and repeated data and working with JSON files. I'm looking forward to the next module now, Building Batch Data Pipelines on Google Cloud.

Topics are very informative and helpful. The only thing I felt is lab time for the bigquery lab where i had to practice struc, arrays etc. was too short. I understand there are several attempts, but it could have been better to get 30mins more in a single attempt as it completes the flow and helps to understand it completely.

Lots of useful labs and examples. Lacks references to documentation, but that is secondary. The course is mostly produced by AI (probably to cut costs) which is not the best approach in my humble opinion, but at the same time there are a lot of other courses like this as well nowadays.