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

4.6
stars
2,594 ratings

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

MP

Apr 8, 2017

Excellent material. Very good flow. Heather has an amazing way of walking through the flow and simplifying the concepts. Great assignments -- takes a bit longer than 3 hours.

NG

Mar 27, 2017

goot as introduction about spark and big data. Small notice: it is incorrect to compare performance hadoop and spark. As I understand, spark was expected to be compacred with MapReduce.

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426 - 450 of 509 Reviews for Big Data Analysis with Scala and Spark

By Deepak D

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Nov 23, 2018

Not specify & given instruction regarding how to submit the solution

By Pedro M

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Apr 29, 2017

Very interesting course about Spark, it covers a lot of key concepts!

By Vahid S

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Jun 27, 2020

I think programming assignments could be better but still OK.

By Deleted A

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Apr 16, 2017

I really liked the content regarding Dataframes and Datasets.

By Emmanouil G

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Apr 1, 2017

Assignment Instructions need improvement in terms of clarity.

By Gongqi L

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Apr 9, 2017

Very good course, but it needs more details and examples.

By kaushik s

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Apr 9, 2017

Good course ! But does need more programming assignments

By Mohd T

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Aug 24, 2019

such a beautiful course design for a bigData devlopers

By Kota M

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Apr 5, 2018

It is a good course, but the lecturer speaks too fast.

By Anuj A

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Oct 22, 2020

Needs more detailing for datasets and dataframe apis

By Wolfgang G

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Aug 30, 2017

Very well-lead introductory, a bit lengthy at times.

By Manuel W

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Apr 18, 2017

Would be better to have more and shorter exercises.

By Ruslan A

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Aug 23, 2017

lectures don't correlate to practical assigment :(

By David G

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Aug 25, 2017

Great course, but can be great idea have the ppts

By Yuan R

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Jan 20, 2018

Great course that is very practical for the job.

By Guillermo G H

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Jun 30, 2017

Great approach to learn about Spark in practice

By Michaël M P

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Feb 5, 2019

Talk about how to set Scala version in Eclipse

By 林鼎棋

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May 29, 2017

Great! But I want to know more about dataset!

By VeeraVenkataSatyanarayana M

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Jun 4, 2017

Basics are covered in an effective way.

By Pavel O

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Aug 12, 2017

Good final course for Scala learners.

By Lucas F

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May 15, 2017

Great lectures and great content!

By Роман В

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Jun 24, 2018

I would like to learn some more.

By Park H

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Apr 18, 2017

Learned Spark APIs, internals.

By Alberto P d P

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May 12, 2017

Very good and concise course.