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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

4.7
2,454 個評分
507 條評論

課程概述

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....

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CC
2017年6月7日

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!

BP
2019年11月28日

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.

篩選依據:

226 - Big Data Analysis with Scala and Spark 的 250 個評論(共 490 個)

創建者 Ismail A I

2017年4月9日

A very interesting and useful course.

Highly Recommended!

創建者 sheng w

2019年1月10日

Very useful course and great materials and assignments.

創建者 Pushkin G

2018年5月6日

Very useful lessons. Great way to get started on Spark.

創建者 John D

2017年7月13日

There is some little mistakes in course(like on slides)

創建者 Fabian D A

2020年4月28日

Excellent material and challenging exercises, love it!

創建者 Mohamed R A

2020年6月30日

Amazing course, thanks for the professor and Coursera

創建者 Anand S

2018年8月14日

good implementation of programming knowledge recieved

創建者 Oleg O

2017年7月29日

Good course, but sometime there are too much details.

創建者 Ravikumar

2017年4月9日

It is very good course material for Spark with scala.

創建者 Dmitriy K

2017年3月20日

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

創建者 Robson R S P

2020年4月12日

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

創建者 Alexey A

2019年7月13日

The most interesting course in whole specialization.

創建者 李东恒

2020年1月24日

Very good course for learning and practicing Spark.

創建者 Yuri R

2019年10月24日

Liked the course. Wasn't familiar with spark before

創建者 Cliff R

2018年7月22日

Gives a good grounding in the fundamentals of Spark

創建者 Kovalenko S

2017年7月17日

Курс очень понравился, спасибо большое за ваш труд!

創建者 Adrien C

2017年6月29日

Interesting course, the last week feels most useful

創建者 Juan L R A

2017年6月19日

Very good course and good materials for learning

創建者 Florian B

2017年11月18日

Super cours, merci beaucoup! EPFL always rocks.

創建者 Devaki B

2017年4月15日

It was good. Got indepth knowledge of Spark API

創建者 Harshad H

2019年10月30日

Best Course for Big Data Learning in the World

創建者 David F S

2019年1月14日

Very informative. Well-organized presentation.

創建者 Husain K

2017年5月7日

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

創建者 samy k

2017年3月21日

Interesting and challenging course! Thank You!

創建者 Robert C M P

2019年2月11日

Excellent videos, explanation, and resources!