Chevron Left
Retour à Big Data Analysis with Scala and Spark

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

2,383 évaluations
494 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:

Meilleurs avis

7 juin 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!

28 nov. 2019

Excellent overview of Spark, including exercises that solidify what you learn during the lectures. The development environment setup tutorials were also very helpful, as I had not yet worked with sbt.

Filtrer par :

226 - 250 sur 477 Avis pour Big Data Analysis with Scala and Spark

par Dmitriy K

20 mars 2017

Thanks Heather! You did a great job with this course.

par Robson R S P

12 avr. 2020

If you are a spark beginner, this is a great course.

par Alexey A

13 juil. 2019

The most interesting course in whole specialization.

par 李东恒

24 janv. 2020

Very good course for learning and practicing Spark.

par Yuri R

24 oct. 2019

Liked the course. Wasn't familiar with spark before

par Cliff R

22 juil. 2018

Gives a good grounding in the fundamentals of Spark

par Kovalenko S

17 juil. 2017

Курс очень понравился, спасибо большое за ваш труд!

par Adrien C

29 juin 2017

Interesting course, the last week feels most useful

par Juan L R A

19 juin 2017

Very good course and good materials for learning

par Florian B

18 nov. 2017

Super cours, merci beaucoup! EPFL always rocks.

par Devaki B

15 avr. 2017

It was good. Got indepth knowledge of Spark API

par Harshad H

30 oct. 2019

Best Course for Big Data Learning in the World

par David F S

14 janv. 2019

Very informative. Well-organized presentation.

par Husain K

7 mai 2017

Great course, learnt a lot from it. Thank you.

par samy k

21 mars 2017

Interesting and challenging course! Thank You!

par Robert C M P

11 févr. 2019

Excellent videos, explanation, and resources!

par shubham m

10 juil. 2018

good but give more practical of small program

par abdhesh

31 déc. 2017

It was an awesome and well explained course.

par Jeroen M

9 avr. 2017

Great course, well explained, instant value!

par Hong C

14 avr. 2020

A perfect resource to get start with Spark.

par Denys L

5 déc. 2018

Very nice, but a little bit outdated course

par Zhenhua w

30 oct. 2019

The lecture is well-organized

and excellent

par Muhammad B

10 juin 2020

Very brilliant instructor, learned a lot.

par Arnaud J

2 juin 2017

Great course. Would definitely recommend.

par Daniel D

20 avr. 2017

Great course - well prepared by the team.