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Avis et commentaires pour l'étudiant pour Leveraging Unstructured Data with Cloud Dataproc on Google Cloud Platform par Google Cloud

4.5
2,977 notes
360 avis

À propos du cours

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...

Meilleurs avis

RW

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.

CP

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.

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1 - 25 sur 367 Examens pour Leveraging Unstructured Data with Cloud Dataproc on Google Cloud Platform

par Joe S

May 12, 2019

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

par 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.

par Akash A

Jun 16, 2019

Great Course to learn fundamentals of Dataproc and PySpark for ML

par Gustavo N R

Jun 14, 2019

ggod

par tinku

Jun 13, 2019

Great!

par Shail S

Jun 10, 2019

Would be nice if there were more examples of applications

par Satoru I

Jun 09, 2019

Take time.

par Felipe O C A

Jun 06, 2019

es aun mas genial

par Harry C

Jun 06, 2019

very good course

par Hiran W

Jun 06, 2019

grate

par 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.

par 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

par 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.

par Simon H

May 28, 2019

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

par Ashwin S

May 28, 2019

good

par Leonardo L G

May 27, 2019

More labs and you should create challenges!

par RAJASHEKHAR G

May 24, 2019

Very Informative

par Dhiraj P

May 23, 2019

Great way to start with Big data.

par Monish K

May 21, 2019

The overall learning experience was good

par Nithin K

May 21, 2019

Nice

par SATEESH K C

May 19, 2019

I am confident that I can build a ML model and customize DataProc

par Eugenio E C S

May 19, 2019

This course is really good if you want to have a brief understood about what's possible to do with Dataproc, how to submit jobs using hive, pig and spark.

par Shalini P

May 19, 2019

It was a very informative course.

par Alberto O

May 19, 2019

Nice introduction to cluster usage and all the power that comes with it.

par Venkata K N

May 19, 2019

Would like to have some more use cases for NLP