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Avis et commentaires pour l'étudiant pour Big Data Analysis with Scala and Spark par École polytechnique fédérale de Lausanne

4.7
2,074 notes
418 avis

À propos du cours

Manipulating big data distributed over a cluster using functional concepts is rampant in industry, and is arguably one of the first widespread industrial uses of functional ideas. This is evidenced by the popularity of MapReduce and Hadoop, and most recently Apache Spark, a fast, in-memory distributed collections framework written in Scala. In this course, we'll see how the data parallel paradigm can be extended to the distributed case, using Spark throughout. We'll cover Spark's programming model in detail, being careful to understand how and when it differs from familiar programming models, like shared-memory parallel collections or sequential Scala collections. Through hands-on examples in Spark and Scala, we'll learn when important issues related to distribution like latency and network communication should be considered and how they can be addressed effectively for improved performance. Learning Outcomes. By the end of this course you will be able to: - read data from persistent storage and load it into Apache Spark, - manipulate data with Spark and Scala, - express algorithms for data analysis in a functional style, - recognize how to avoid shuffles and recomputation in Spark, Recommended background: You should have at least one year programming experience. Proficiency with Java or C# is ideal, but experience with other languages such as C/C++, Python, Javascript or Ruby is also sufficient. You should have some familiarity using the command line. This course is intended to be taken after Parallel Programming: https://www.coursera.org/learn/parprog1....

Meilleurs avis

CC

Jun 08, 2017

The sessions where clearly explained and focused. Some of the exercises contained slightly confusing hints and information, but I'm sure those mistakes will be ironed out in future iterations. Thanks!

CR

Apr 10, 2017

Great introduction to spark. Fun assignments. Since it was the first ever session, there were quite a few kinks with the assignments. But the discussion forums rescued me any time I was stuck.

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1 - 25 sur 402 Examens pour Big Data Analysis with Scala and Spark

par Luiz C

Jan 27, 2019

If you know the subject, may be easy. If you don't know the subject, too dense and abstract with not enough practical examples/exercises before the assignments (specially for week4). Would have been better to break the videos into smaller ones, include more practical concrete examples/exercises during the videos (as very well done by Pr. Obersky during the 1st Course of the Specialization). There could have been room to break week4 into material for 2 weeks. Still, inresting.

par Rodion G

Apr 15, 2019

Course is good to have some practice in spark and scala. However it seems to be long forsaken by staff and some issues with assignments require doing archaeology in the forums. Also it is quite unpleasant to see that while specialization emphasizes functional programming, some auxiliary code in assignments is written in the worst manner of imperative programming... Why use scala then?

par Sivakumar P

Nov 29, 2018

Course is very useful to understand Spark and Scala things.

par Navjinder S V

Dec 18, 2018

Great Course content with amazing and challenging assignments!

par sheng w

Jan 10, 2019

Very useful course and great materials and assignments.

par Andrey M

Jan 10, 2019

Thank you for the great introduction in to the Spark, What it is and What are the most commonly used APIs.

par Johan R

Dec 28, 2018

Very good introduction which gets you to be productive on your own

par David F S

Jan 15, 2019

Very informative. Well-organized presentation.

par Msellek A

Jan 26, 2019

Great course ! Thanks for the effort

par Šejla Č

Mar 20, 2019

Brilliant lecturer and slides! The only problem is assignments not being clear on the expected outputs. Sometimes it takes more time to figure out what exactly is asked than to find the solution. A few more test cases or detailed examples would help.

par Subodh C

Mar 30, 2019

Thanks Prof. Miller !

par Konrad C

Jan 27, 2019

A lot things to learn and experiment.

Challenging assignments.

par Robert C M P

Feb 12, 2019

Excellent videos, explanation, and resources!

par Kevin L

Apr 02, 2019

I loved this course - it was a great introduction to Spark. At the end, I wasn't (and am still not) clear on type-safe operations on Datasets, and now to write Tests to verify this.

These will be one of the targets of my upcoming research and study.

par Ngoc-Bien N

Apr 04, 2019

bon cours

par Light0617

Apr 14, 2019

wonderful!!!

par Denys L

Dec 05, 2018

Very nice, but a little bit outdated course

par Sreeraj R P

Jan 07, 2019

Very good course for a great start in Spark. Require some initial knowledge and coding experience in Scala.

par Yacine G

Dec 23, 2018

FANTASTIC!!! I don't even know which was better: the course material quality, the instructor's approach or the assignments. FANTASTIC!!!

par Murat A

Jan 20, 2019

It was very challenging, but also informative. Thanks a lot.

par Edgar D

Mar 10, 2019

Favorite so far out of the Scala Specialization Course. It was executed really well, and taught really well, too. Kinda wish they would add more exercises to help us get more experience with some of the concepts, but that's something you can always just do by yourself either way.

par Varlamova E

Mar 10, 2019

It was amazing!!! Very useful course!

par Симкин И М

Mar 10, 2019

Perfectly. Very competent teacher and good tasks. Requires knowledge of scala.

par shubham m

Jul 10, 2018

good but give more practical of small program

par Hristo I

Apr 09, 2017

This is a great course on the intricacies of Apache Spark. It is not a general Big Data course, neither is it an easy one. Doing the programming assignments properly requires reading a lot trough the Spark documentation, which I personally liked as part of the challenge, but beware if you are not that type of person or aim at finishing the course as quickly as possible.