More teaching should be focused on how to build the python file of each task, rather than ready for us to run.

Google did not work very hard to convey this information in its lectures. They are just bullet slides with a talking head. They need to learn how better course developers are creating courses.

There were some minor problem and mistake in the lab file. The python/java scripts were not explained at all. There are questions about the code itself, but then the questions were not answered.

The last lab has an error so this course can never be finished and the certificate never be obtained. Further more some teachers use ununderstandable slang, and the transcripts are unreadable due to misspellings and transcription errors. The screenshots on the videos are blurred so the tears spring to your eyes when you try to read them.

Last few videos in Week2 have too much python, the only relief is that we have templates to save time. I have to spend more time in understanding the python scripts used in the last 4-5 videos. I have worked with python only for wrangling data using pandas, only grep.py & grepc.py were easy, all others were deep for me to understand. Maybe the course has to reiterate the importance of python programming. What if I want to build it custom, templates may not help. So, please stress the importance of python/java skills in the course

Quite hard to follow

This is a course on how to buy Google products, that's it. They explain the very basics of batch data pipelines but it's so intertwined with their own products you barely learn anything about data pipelines themselves. On top of that, exercises consist in pasting things they don't even care to explain.

Course was great. Easy to understand and has many labs to try. One issue was that the first Dataflow lab was not working due to the Apache Beam issue. I worked with a rep and he said he would follow up with me after resolving the issue. But he never contacted me again. Probably the issue was nor resolved. So I never completed that lab.

I'm not from a programming (Java/.NET/Python etc) background but Informatica ETL, Oracle PL/SQL and UNIX. I wish the code as part of Serverless Data Analysis with Dataflow: Side Inputs (Python) explained step by step like where it starts and then step by step. If it sounds redundant, a document reference to each step would help.

Great addition to the Data Engineering line up. In addition to the updated content on newer tools like Cloud Composer and Data Fusion, I feel like the detail on tools like Dataproc and Dataflow is better than it used to be.

questions given in the quiz are pretty simple.. need those questions which can ensure learners to brainstorm

Informative and interactive course introducing to DataProc, DataFlow, Apache Beam, Apache Airflow.

There were too many labs with services that take 30-40 minutes just to spin up. I wouldn't have a problem with all the labs if the services took 2-5 minutes to spin up.

The Labs need to be tested

Its obvious some of the narators don't even understand the subjects they are talking about because they can't pronounce the words correctly. Come on. Let's at least read the scripts a few times before getting in front the camera. In this whole course sofar there's only two narators who obviously have played around with this technology. The rest just read the scripts which is so boring to listen to. I can read the scripts myself. Just smiling doesn't cut it. You need to understand what you are talking about so you accent things properly and sound like an expert.

a lot to digest on this course... pace is too high in my opinion, it should be slower. good course anyways.

thanks

Dataflow Labs should be explained in greater detail so as to provide comprehensive learning

Some labs could be better in term of the thinking rationale, error handling, etc.

The labs had numerous problems and didn't always work. Most of this course felt like "Hands On With Marketing Material". It would have more effective if they repeatedly remembered who the primary role taking this course is (Data Engineer) and explain why we're doing various steps from that context. As it is, there's way too much copy/paste that just handwaves by why we're doing things.

Do not waste your time doing this course/specialisation. The labs are extremely buggy and do not fully work making this course similar to studying chemistry without ever doing a single lab; instead the labs are just described to you. The video lectures are good and could be better the pace of the lectures was a little quicker - which you can adjust yourself if needed.

I'm going to see if I can cancel my subscription to this dodgy specialisation - a first for my time with Coursera!

This course is a joke, labs are all copy/paste, I think that the most difficult thing that I did was change and environment variable. Quizzes are 1-2 questions about the most basic/least useful topics. Does not prepare you to do any real work on your own. Needs to require users to write more code / at least do something more than copy/paste, click a few buttons to pass

It's too easy you don't learn anything

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.

Definitely the most difficult module, but then it's the module where you will learn the most. Excellent, real world labs where you can create visual batch data pipelines using all the latest technologies, practice executing cloud shell commands and deploy a finished pipeline via Airflow. A very worthwhile, practical experience.

I learned how to manage GCP services to process big data, including the usage of cluster computing services, and serverless computing. I additionally learned how to build batch data pipelines, including creation of visual data pipelines, learn about Python code to process data, and the architecture of a batch data pipeline.