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

2,413 ratings
498 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:

Top reviews

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!

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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476 - 481 of 481 Reviews for Big Data Analysis with Scala and Spark

By Dan O

Mar 25, 2017

Slow videos repeating several times the same thing (not a pedagogical / "good to fix an idea" kind of repetition), which makes them hard to follow.

However the worst are the exercises: the first time after 3-4 other Coursera Scala related courses where I have to actively check the forums for minute details about what is expected / implied for the solutions to pass the grading.

Things like what to do when updating the kmeans and you have duplicates, subtle differences between average and mean, etc. ...

In all other courses the expectation of the exercises were sufficiently clear and straight forward that I never had to check the forums to solve them.

Also, the code style of the exercises is literally an anti-pattern in idiomatic Scala, against everything learnt in the previous Scala courses: "var" all over the place, low level loops like in C or Java, etc. ...

By Марко И

Apr 10, 2017

I don't know what happened but it seems they had technical or some other problem while preparing this course. Some assignments were more oriented to solving marginal problems then using Spark and distributed and parallel computing. And that is really annoying. Previous 3 courses were great, maybe this one will improve.

By Deleted A

Jul 9, 2020

good lectures another case of lectures not matching the assignment in terms of what you should pick up. inadequate resources and not enough depth on actual transformations and methods.

By Sergio R P

Sep 30, 2019

The assignments are very confusing and unexplained. They do not take long to reply to the forum.

By Krzysztof S

Mar 3, 2020

Assignments don't work properly

By Bulat S

May 19, 2018

The course is too basic.