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學生對 Google 云端平台 提供的 Leveraging Unstructured Data with Cloud Dataproc on Google Cloud Platform 的評價和反饋

2,977 個評分
360 個審閱


This 1-week, accelerated course builds upon previous courses in the Data Engineering on Google Cloud Platform specialization. Through a combination of video lectures, demonstrations, and hands-on labs, you'll learn how to create and manage computing clusters to run Hadoop, Spark, Pig and/or Hive jobs on Google Cloud Platform. You will also learn how to access various cloud storage options from their compute clusters and integrate Google’s machine learning capabilities into their analytics programs. In the hands-on labs, you will create and manage Dataproc Clusters using the Web Console and the CLI, and use cluster to run Spark and Pig jobs. You will then create iPython notebooks that integrate with BigQuery and storage and utilize Spark. Finally, you integrate the machine learning APIs into your data analysis. Pre-requisites • Google Cloud Platform Big Data & Machine Learning Fundamentals (or equivalent experience) • Some knowledge of Python...



Apr 23, 2019

The course has introduced me to hadoop tools. I have learned how easy it is to setup a hadoop cluster using Dataproc. Will sure look for cases that have implemented hadoop and replicate on GCP.


Dec 29, 2017

Really enjoyed it, woudl have liked to spend more time with the APIs and integrate with real time web downloads. There are a few bugs and misprints, but wasn't too hard to find them.


1 - Leveraging Unstructured Data with Cloud Dataproc on Google Cloud Platform 的 25 個評論(共 373 個)

創建者 Joe S

May 12, 2019

I think the quizzes should have had more questions, there were many more cool things you could have asked.

創建者 Justin E

Jun 04, 2019

This course is far longer than any of the other courses in the Data Engineering specialisation. I felt that it rehashed a lot of the first course as well.

創建者 haiyang l

Jun 22, 2019

Wish it went in more depth about RDD transformation, which was a little bit confusing... A lab about how DataProc can be used as an extention of BigQuery that does not overlap functionality would be nice. How to covert RDDs to Pandas DF, and vice versa.

創建者 Gopinath K

Jun 26, 2019

Good material, could be more technical about the cases rather than just promoting the product

創建者 JOSE A F

Jun 26, 2019

great way to start with ml and big data!!!!

創建者 Henio T

Jun 20, 2019


創建者 Divyangana P

Jun 20, 2019

Exceptionally well designed course for beginners.

創建者 Anand S

Jun 18, 2019

It was a good course providing synthesized learning in an easy manner

創建者 Akash A

Jun 16, 2019

Great Course to learn fundamentals of Dataproc and PySpark for ML

創建者 Gustavo N R

Jun 14, 2019


創建者 tinku

Jun 13, 2019


創建者 Shail S

Jun 10, 2019

Would be nice if there were more examples of applications

創建者 Satoru I

Jun 09, 2019

Take time.

創建者 Felipe O C A

Jun 06, 2019

es aun mas genial

創建者 Harry C

Jun 06, 2019

very good course

創建者 Hiran W

Jun 06, 2019


創建者 Maud B

Jun 06, 2019

Overall great! The lessons were easy to understand and had all the information I wanted.

The labs were easy to follow with pretty clear instructions.

But it was anoying to not know if and why I was missing points, since the grade is only updated after a long time, plus I lost the connection to the qwicklab tab at some point which stop the grading.

創建者 Widiarto A

May 30, 2019

Awesome course! It really helps, especially the procedure on how to initiate a Dataproc cluster and submit a job to that cluster

創建者 Deep C

May 30, 2019

It is a very informative course. I learned how to use DataProc, create and run custom clusters to run Hadoop jobs.Thanks to Google for providing such a easy to follow course.

創建者 Simon H

May 28, 2019

I feel confident I could spin up custom clusters and perform ML workloads

創建者 Ashwin S

May 28, 2019


創建者 Leonardo L G

May 27, 2019

More labs and you should create challenges!


May 24, 2019

Very Informative

創建者 Dhiraj P

May 23, 2019

Great way to start with Big data.

創建者 Monish K

May 21, 2019

The overall learning experience was good