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Learner Reviews & Feedback for Big Data Analysis with Scala and Spark by École Polytechnique Fédérale de Lausanne

4.7
stars
2,383 ratings
494 reviews

About the Course

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....

Top reviews

CC
Jun 7, 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 28, 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.

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326 - 350 of 477 Reviews for Big Data Analysis with Scala and Spark

By Roman I

Apr 5, 2020

good cource

By Kirill K

Oct 10, 2017

A good one.

By William H

Sep 6, 2017

Outstanding

By jose a m l

Jun 13, 2020

Excelente

By Sanjeev R

Aug 26, 2019

Excellent

By Ngoc-Bien N

Apr 4, 2019

bon cours

By SAIDULU D

Jan 17, 2018

Excellent

By Mohamed K

Oct 30, 2017

Perfect !

By Pengcheng L

Jun 5, 2017

Thanks :)

By Huajian M

Apr 4, 2017

So great!

By 李帅鹏

May 1, 2019

Perfect!

By IURII B

Aug 7, 2017

Thanks !

By Estera K

Mar 20, 2017

AWESOME!

By Satendra k

Apr 9, 2017

Thanks

By Вьюн С А

Feb 27, 2020

Nice!

By Kiệt Đ

Jul 1, 2017

Best

By 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.

By Anton M

Jun 19, 2020

Really enjoyed most part of the course, it was a fun ride with Spark !

Explanations of lector was crystal clear and I liked all assignments (except last one)

There are some cons though:

-> Week 3 contains no assignment, I would prefer to have one really dedicated to "Partition and Shuffling" subject

-> Spark SQL explanations about untyped were too much shady. It somehow feels like this API goes totally orthogonal to everything functional we have had so far. It's like running in Java but using C with JNI... Well, after all, it's a drawback of API, not course itself, but still having bit of aftertaste of fighting with Scala type system trying to glue SQL... meh

-> there are many missed opportunities to have proper Coursera quiz during lectures

By Robin B

Jul 4, 2019

Very good introduction to RDDs and DataFrames/Dataset along with valuable insight into performance considerations.

I'd done some prior work with Hadoop/Pig in the past and more recently with Spark (mainly DataFrames/GraphFrames) - this was really useful to round out my understanding of RDDs and optimisation.

The assignment guidance in the code comments could be more complete to save having to refer back to the site (and maybe reference specific video lectures with the hints). Though it's good that the assignment exercises aren't tutorial-grade, as that makes the experience more transferable to real projects.

By Saurabh M

Apr 8, 2017

Dr Heather has done an outstanding job to create this unique material with a fine combination of theoretical and practical aspect of Spark. She has covered almost everything from basic to complex, but there is some area which demands more time from the creator for its explanations. Since this is first time launched course and definitely going to improve itself in upcoming days. I enjoyed this course thoroughly. It helped me, cementing my basic concept of Spark.

By Ellen K

Apr 2, 2017

The structure, focus content of the videos of this course are great. The assignments are so-so. They do practice writing reasonably realistic Spark jobs in Scala, but it is hard to draw the connection between the more theoretical videos and the very practical assignments. Also, the assignments are hard to solve due to being poorly specified and there being hardly any helpful output from the auto-graders used to evaluate assignment submissions.

By Mykyta P

Aug 18, 2019

The video lectures are good but code assignments are worse, seems like they were written by students instead of professor or something. Sometimes code doesn't follow Scala and FP conventions. And the output of the grader doesn't really provide any helpful information besides the name of the faulty function. But overall it's a good course and I think the newcomers without any previous experience with Spark will learn a lot.

By Chet W

Jan 29, 2018

Great lectures but the exercises felt contrived sometimes. Especially the exercise on PCA didn't really seem to provide that much insight into the data or illustrate the usefulness of the algorithm (especially when compared to the parallel programming exercise which had a great use for PCA). However the last exercise was good and forced the student to really explore the spark API. Learned so much from this!

By Sergio

Jun 25, 2017

I thought this was a decent course. I enjoyed the exercises and thought it gave a good introduction to Spark. Some of the lectures in Week4 were a bit long and the material needed to complete the Week4 exercise wasn't in the lectures. It would have been nice to have a 'conclusion' lecture wrapping everything up instead of just ending the course on a DataSet's lecture.

By Philip R K

Apr 11, 2017

Generally a really good introduction to Spark. What I found disturbing though were the very imbalanced difficulties of the excercises and the rather uninformative test messages that did not help for the implementation. There was no course where I had to search for other peoples suggestions in the forums.

Still the course was good -- I would do it again!