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學生對 Google 云端平台 提供的 Building Batch Data Pipelines on GCP 的評價和反饋

4.5
1,429 個評分
180 條評論

課程概述

Data pipelines typically fall under one of the Extra-Load, Extract-Load-Transform or Extract-Transform-Load paradigms. This course describes which paradigm should be used and when for batch data. Furthermore, this course covers several technologies on Google Cloud Platform for data transformation including BigQuery, executing Spark on Cloud Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Cloud Dataflow. Learners will get hands-on experience building data pipeline components on Google Cloud Platform using Qwiklabs....

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UB
2020年5月27日

A great course to help understand the various wonderful options Google Cloud has to offer to move on-premise Hadoop workload to Google Cloud Platform to leverage scalability of clusters.

AD
2020年7月16日

Great course learning what it is the big advantages of using GCP for data given they have big implementations and with better performance of what it is today in on premises scenarios

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1 - Building Batch Data Pipelines on GCP 的 25 個評論(共 181 個)

創建者 RLee

2020年2月12日

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

創建者 Roger S P M

2020年1月24日

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.

創建者 Polla T

2020年2月2日

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.

創建者 Prashanth T

2020年4月24日

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.

創建者 James W

2020年4月1日

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.

創建者 Rahul J S

2020年4月17日

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

創建者 Léo Z

2020年2月27日

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

創建者 Xavier A A

2020年5月6日

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

創建者 Scott P

2020年2月6日

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.

創建者 Adolfo C Y

2020年5月2日

The Labs need to be tested

創建者 Thomas M

2021年1月6日

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.

創建者 Johnny C

2020年5月3日

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

thanks

創建者 Divyangana P

2020年4月12日

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

創建者 Hendra D S C

2020年4月10日

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

創建者 Mahmoud M

2020年4月5日

Quite hard to follow

創建者 Kip O

2021年4月18日

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.

創建者 g m

2020年10月2日

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!

創建者 Kitt M

2020年8月1日

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

創建者 Jaap K

2021年4月21日

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.

創建者 Alexander T

2020年5月14日

It's too easy you don't learn anything

創建者 Yuri M

2020年9月8日

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.

創建者 Jeanmann P

2020年4月26日

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.

創建者 James H

2020年8月21日

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.

創建者 ARVIND K S

2020年6月28日

An excellent course imparting tremendous knowledge and skills in several technologies for data transformation, executing Spark and Hadoop with Dataproc and serverless Dataflow. It's a great sense of achievement to actually build data pipelines in various course projects.

創建者 Iman R

2020年5月23日

Great course. This course tell about developing pipeline for various situation, so the learned can gain the experience in the field too. Because sometime I think just doing it at the course lab and gain the experience from it, it's just not enought