Chevron Left
Retour à Big Data Analysis with Scala and Spark

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

4.7
2,072 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.

Filtrer par :

26 - 50 sur 402 Examens pour Big Data Analysis with Scala and Spark

par Bora K

Sep 01, 2018

Teacher has excellent teaching skills. She takes enough time to go through to concepts. Before introducing a new technique she teaches why the new technique is needed and explains how it solves existing problems. Absolutely great course.

par Marc K

Sep 09, 2018

Great course explained with great detail

par Rajesh K S

Oct 09, 2018

Excellent Cou

par Karim M

Oct 15, 2018

Amazing course, i learned a lot even if i'm working with scala and spark

par Arthur-Lance

Sep 15, 2018

really great course, it help me fast get into the new area. thanks a lot!!!

par Natalija I

Sep 26, 2018

Great course, I really enjoyed learning! Thank you Dr. Miller

par savitri v v

Jul 27, 2018

Very good learning portal

par Abhay D

Nov 04, 2018

Wonderful course. Helped me a lot.

par Kyle L

Jun 11, 2017

very good course, really enjoyed

par Angel A

May 07, 2017

love this topic!!!! New and frontier technology in NLP. More and more concentrations and analysis on this big data research.

I wish more courses about Parallel clustering using Spark available to the many.

par Vlad N

Apr 03, 2017

Nice topics regarding using partitions for Spark and encoders!! Really interesting course

par Stephen E R

Mar 27, 2017

A superb course taught by a superb lecturer. From a lecture stanpoint, my only recommendation is that additional lectures be provided to flesh out the coverage of Datasets. I found the assignments challenging and a good reflection on my understanding of the course material. I would recommend only the following tweaks:

Week 1 assignment: Either tightly specify the function that determines if a particular String is present in a large textstring OR provide the function in the assignment skeleton. There are too many ways to approach this function that are correct, but different than the one the grader used. This function is only incidental to the goal of applying one's knowledge of Spark to the assignment.

Week 4: Allocate additional lectures to flesh out the coverage of Datasets. The subject was not covered to the same level of detail that the other subjects in the course were given.

These are minor points.

Well done, Dr Heather Miller!

par Álvaro L L

Jun 11, 2017

A great introduction to Spark !!! An esential course to anyone interested in the field! Thank you very much Heather and the EPFL team !!

par Fang Z

Apr 06, 2017

Very good course.

par Manoj K

Aug 27, 2017

This course make me to fall in love with SPARK framework :)

par Xiongchu W

Aug 05, 2017

This course is indeed introductive to learn all the necessary stuffs about Spark. It is pretty good to tell much about shuffling. Because we should not only be familiar with how to operate on Spark, but we should really have a good understanding of what's going on underneath the hood. Thanks!

par Benzakoun S

May 08, 2017

excellent quality of content

par Parker G

Apr 10, 2017

Great course! The powerpoint/slides/pdfs are a GREAT resource

par CAI X

Jul 16, 2017

Well explained and demoed . Good introduction to spark, the most useful big data framework!

par Walter E Z

Apr 02, 2017

Great introduction to Spark and it's data structures. The course is easy to follow, and lecturer is entertaining and really engaged.

Thanks, I really had fun !

par jose r

Nov 24, 2017

Great Course, thanks

par Prashant P

May 12, 2017

Awesome course !

par Imran K

Apr 08, 2017

As always, Coursera delivered another top quality courses on Spark with Scala. I have learned a lot of details, understood the underlying working principles of Spark in the last few days. Thanks to Dr. Miller for such a great course. I hope in the future versions of this course the overall presentations will be more smooth and typo-free.

par Shae S

Mar 23, 2017

I learned so much from this course! It was amazing how Dr. Miller used concepts that were meticulously built up in the earlier courses, such as evaluation strategy, functional collections, reactive programming, and associativity, to describe the core of Spark in only four units. As someone coming from more of a statistics background, I started this specialization only to learn Spark, so I wasn't always sure how relevant learning the more theoretical underpinnings of Scala would be. It turns out that it was pretty essential, while also just making me a better programmer. Looking forward to the capstone!

par Jorge B C

May 02, 2017

Very interesting course!!