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學生對 洛桑联邦理工学院 提供的 Big Data Analysis with Scala and Spark 的評價和反饋

2,149 個評分
430 條評論


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:



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.


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!


276 - Big Data Analysis with Scala and Spark 的 300 個評論(共 413 個)

創建者 jose c a

May 05, 2018

Muy Bueno!!!!


Feb 02, 2018

Great course!

創建者 Jose E T

Jun 02, 2017

Great Course!

創建者 Emiliyan T

Apr 09, 2017

Magnificent !

創建者 Light0617

Apr 14, 2019


創建者 Saiteja t

Aug 01, 2018

Nice session

創建者 Hengyu

Apr 06, 2018

very helpful

創建者 Rafael M

Oct 18, 2017

Great Course

創建者 Mihir S

Sep 27, 2017

Good Course.

創建者 Angel V

Aug 21, 2017

very usefull

創建者 Aleksey I

Jun 02, 2017

Good course.

創建者 Kirill K

Oct 10, 2017

A good one.

創建者 William H

Sep 06, 2017


創建者 Sanjeev R

Aug 26, 2019


創建者 Ngoc-Bien N

Apr 04, 2019

bon cours


Jan 17, 2018


創建者 Mohamed K

Oct 30, 2017

Perfect !

創建者 Pengcheng L

Jun 05, 2017

Thanks :)

創建者 Huajian M

Apr 05, 2017

So great!

創建者 李帅鹏

May 02, 2019



Aug 07, 2017

Thanks !

創建者 Estera K

Mar 20, 2017


創建者 Satendra k

Apr 09, 2017


創建者 Kiệt Đ

Jul 01, 2017


創建者 Bianca T

Apr 22, 2017

Taking into consideration that this was the first edition of the course, I can say that it has been a nice journey. I am glad about the fact that Heather managed to expose a bit of the Spark internals and not only talk about querying data and how easily this can be made by using Spark (as most of the Spark oriented courses consist of).

In addition to this, I could listen to Heather all day long - she's a great presenter and has wonderful teaching skills.

However, the homework has outlined some neglected aspects of the course:

- vague description or requirements

- not strongly related to the presented content (the lectures outlined partitioning mechanism, but the homework 2 did not require it...)

- not so meaningful feedback, except for some tests failing/passing - I would have expected something like you did ok, but your job took longer than expected; check out this and that

Overall, it's been a highly expected course and it was nice to get a broader outlook on Spark. I hope that there will be more courses (and more detailed) related to Spark ecosystem in the near future.