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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
2,103 個評分
421 個審閱

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....

熱門審閱

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!

BP

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.

篩選依據:

201 - Big Data Analysis with Scala and Spark 的 225 個評論(共 406 個)

創建者 RAMESH K M

Apr 23, 2017

Instructor has been explaining the concepts with more practical direction

創建者 Alvin H

Apr 04, 2017

Awesome Course . Detail and Depth of RDD vs Dataframe vs Dataset.

Latency vs Network/IO vs Shuffling.

Learnt a lot .

Thank you Heather.

創建者 Nikola M

Apr 03, 2017

Good overview of the subject, covering all important aspects. Assignments were well prepared, with a couple of unclear points that were quickly discovered and explained on the forums.

創建者 Rajesh G

Dec 02, 2017

Excellent course!

創建者 Pavel T

Apr 06, 2017

Very interesting course. Heather presents at just right level of abstraction to my taste and her presentation is very lively so it is easy to stay focused.

Formally, this is a course on a particular set of software tools and many such courses are not very useful unless one is to start applying learned skills immdeiately because the material starts getting obsolete the day the course is passed.

I believe that this course, however, teaches important priniples that outlive the particular toolkit, in particular the art of reasoning about algorithms on distributed collections, in particular, their performance. One would expect this to be a complex subject but somehow this course makes it feel simple -- which is a good indicator of high quality.

創建者 Andrzej J

Mar 20, 2017

Great course, presentations prepared with passion, exercise of right level....

創建者 Atsuya K

Oct 30, 2017

A good quick intro to Spark.

創建者 Markus B

Apr 10, 2017

Great course overall. The feedback on failed tests and out of memory errors on the assignments can be improved to make it more user-friendly.

Would be great to see a more advanced version of this course that dives deeper into the machine learning features of Spark, etc.

創建者 Heyang W

Aug 18, 2017

A walk through from the oldest RDD to newest Dataset API of spark, together with brief introduction on how spark work. Home work set up several scenario to use the different kinds of spark API to do basic data analysis.

創建者 Jon Z

Jul 05, 2017

Great course, I learned a lot.

創建者 Wei-Ting C

Sep 13, 2017

This is my first completed course on Coursera! It's good for understanding Apache Spark's RDD and its usage.

創建者 Adrien C

Jun 29, 2017

Interesting course, the last week feels most useful

創建者 Gregory E

Mar 10, 2018

Good course, shows a lot of useful and unobvious things about Spark. But not always has well described assignments

創建者 Dinesh A G

Apr 02, 2017

good course on spark.

創建者 Piotr A

Mar 16, 2017

Great course. Very informative. I have learned a lot. Lessons are very well prepared and structured. Implementation tasks are interesting. Thanks to all EPFL team. Great job!

創建者 Denis A Z Q

Aug 27, 2017

Course with excellent content, methodology and teacher. It was an extraordinary learning experience

創建者 Angel V

Aug 21, 2017

very usefull

創建者 Apostolos N P

Mar 15, 2017

I really enjoyed this course! First of all I would like to congratulate the people behind this effort. The videos are clear, to the point and they contain very useful information and tips that are very difficult to get from a book. I hope that you will continue with a second course on Spark and Scala with more advanced topics. Thank you very much.

創建者 Jijo T

Apr 13, 2017

It was well worth the wait! The instructor was good. Assignments were challenging as well as hands on!

創建者 Andronik

Jun 15, 2017

Nice introduction into Spark with details about how Spark works internally. This course also talks about when to use RDD/Dataframe/Dataset and performance pitfalls.

創建者 Prachi C

Sep 14, 2017

An excellent explanation of basic concepts of Spark using Scala. We covered the most important topics and ready to deep dive!

創建者 Huajian M

Apr 05, 2017

So great!

創建者 Canh S L

Mar 25, 2017

really good, informative

創建者 Daniel D

Apr 20, 2017

Great course - well prepared by the team.

創建者 César A

Mar 29, 2017

Excellent course. Fun and entertaining.