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.

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.

Quite hard to follow

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

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.

The practical hands on lab can be further improved by making them more interactive rather then just copy pasting the code. Can be re-designed to have more room for experimentation.

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.

Most of the labs need to be review to make sure the instructions are still correct. I spent a substanial part of my time in the labs finding work arounds because of poor instructions.

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.