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

4.7
2,077 notes
418 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: https://www.coursera.org/learn/parprog1....

Meilleurs avis

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!

CR

Apr 10, 2017

Great introduction to spark. Fun assignments. Since it was the first ever session, there were quite a few kinks with the assignments. But the discussion forums rescued me any time I was stuck.

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226 - 250 sur 402 Examens pour Big Data Analysis with Scala and Spark

par Eric L

Apr 09, 2017

Demystified the subject for me. I felt like the lecturer covered a considerable amount of material in relatively short time. The assignments helped to cement the knowledge acquired over the duration of the course.

par Ravishankar N N

Sep 15, 2017

Superb course..very detailed and useful.. highly recommend

par Liqun Y

Jun 30, 2017

Very useful. Dr. Miller apparently did a very good job. I strongly suggest beginners to read the "Learning Spark" book and then take this class.

par Roman Z

Apr 14, 2017

I like the course tempo very much. It kept me away from doing anything else while listening to the lectures.

par ciri

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!

par Adrien C

Jun 29, 2017

Interesting course, the last week feels most useful

par Jay

Sep 21, 2017

cool teacher and cool course!

par Gregory E

Mar 10, 2018

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

par Georgi Y

Jul 07, 2017

Excellent course!

par Andrii P

Apr 09, 2017

Just awesome :)

par Florian W

Apr 01, 2017

It's a great course with lots of insight into spark. At first it's confusing - first rdds; then dataframes and later datasets. But this is by no means the instructors' fault. Heather greatly describes in detail what goes on under the hood and why dataframes are way faster than simple rdds and why we have to give up some of our beloved typesafety of scala-land to let catalyst do its magic. Datasets try to bring some of it back.

I really enjoyed coding the assignments. Took me ~2hrs each. The tasks were fun and like those you would find in the wild.

I already applied some of the skills I learned here at work and successfully implemented a simple recommendation engine that will go to production next week.

Highly recommend this course!

par Pengcheng L

Jun 05, 2017

Thanks :)

par Aleksey I

Jun 02, 2017

Good course.

par JULIAN A G V

Feb 02, 2018

Great course!

par Fernando R

Oct 28, 2017

it was a super interesting course

par David M

Sep 18, 2017

Concepts are very well explained..

par Ananda P V

Aug 31, 2017

excellent introduction to Spark. I was always looking for some course which touches the underlying functional aspect of Spark then just showing syntactic values. Moreover this course also teaches how parallelism and distributed process works with Spark. You also get an idea why Spark is written in Scala.

par Manuel M C

Mar 23, 2017

Great course, keep up the good work.

par Aldrin

Jul 10, 2017

The information was explained really well for someone coming in with no spark and minimal scala knowledge. Due to this course I understood basic spark concepts well enough to begin understanding pipelines built on top of spark such as ADAM.

I would highly recommend this course to everyone.

par Andreas K

Jul 26, 2017

It was a pleasure to follow the video lectures and solve the assignments.

par Jayaprakash

Apr 09, 2017

Great Material and lecture videos. It covered important concepts we need to know about Spark and helped me to learn further. Assignments were on real datasets and helped me to explore different APIs if Spark and Scala.

par Rajesh G

Dec 02, 2017

Excellent course!

par Jon Z

Jul 05, 2017

Great course, I learned a lot.

par Patrick

Jan 12, 2018

Been working with Spark since 0.9 and this was still worthwhile. Excellent course.

par Gao Y

Apr 04, 2017

Nice course. Big jump at the end. The assignment is well hinted.