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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

2,361 évaluations
486 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


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!


Nov 29, 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.

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401 - 425 sur 469 Avis pour Big Data Analysis with Scala and Spark

par Pedro M

Apr 29, 2017

Very interesting course about Spark, it covers a lot of key concepts!

par vahid s

Jun 27, 2020

I think programming assignments could be better but still OK.

par Francis T

Apr 16, 2017

I really liked the content regarding Dataframes and Datasets.

par Emmanouil G

Apr 01, 2017

Assignment Instructions need improvement in terms of clarity.

par Gongqi L

Apr 10, 2017

Very good course, but it needs more details and examples.

par kaushik

Apr 09, 2017

Good course ! But does need more programming assignments

par Mohammad T

Aug 24, 2019

such a beautiful course design for a bigData devlopers

par Kota M

Apr 05, 2018

It is a good course, but the lecturer speaks too fast.

par Wolfgang G

Aug 30, 2017

Very well-lead introductory, a bit lengthy at times.

par Manuel W

Apr 18, 2017

Would be better to have more and shorter exercises.

par Ruslan A

Aug 23, 2017

lectures don't correlate to practical assigment :(

par David G

Aug 25, 2017

Great course, but can be great idea have the ppts

par Yuan R

Jan 20, 2018

Great course that is very practical for the job.

par Guillermo G H

Jun 30, 2017

Great approach to learn about Spark in practice

par Michaël M P

Feb 05, 2019

Talk about how to set Scala version in Eclipse

par 林鼎棋

May 30, 2017

Great! But I want to know more about dataset!

par VeeraVenkataSatyanarayana M

Jun 04, 2017

Basics are covered in an effective way.

par Pavel O

Aug 12, 2017

Good final course for Scala learners.

par Lucas F

May 15, 2017

Great lectures and great content!

par Роман В

Jun 24, 2018

I would like to learn some more.

par Hoon P

Apr 19, 2017

Learned Spark APIs, internals.

par Alberto P d P

May 12, 2017

Very good and concise course.

par Stéphane L

Oct 13, 2017

Very useful

par Srinivasu N

May 15, 2020