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学生对 École Polytechnique Fédérale de Lausanne 提供的 Big Data Analysis with Scala and Spark 的评价和反馈

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2,594 个评分

课程概述

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://hua.dididi.sbs/learn/parprog1....

热门审阅

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!

CR

Apr 9, 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.

筛选依据：

426 - Big Data Analysis with Scala and Spark 的 450 个评论（共 509 个）

创建者 Deepak D

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Nov 23, 2018

Not specify & given instruction regarding how to submit the solution

创建者 Pedro M

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Apr 29, 2017

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

创建者 Vahid S

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Jun 27, 2020

I think programming assignments could be better but still OK.

创建者 Deleted A

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Apr 16, 2017

I really liked the content regarding Dataframes and Datasets.

创建者 Emmanouil G

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Apr 1, 2017

Assignment Instructions need improvement in terms of clarity.

创建者 Gongqi L

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Apr 9, 2017

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

创建者 kaushik s

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Apr 9, 2017

Good course ! But does need more programming assignments

创建者 Mohd T

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Aug 24, 2019

such a beautiful course design for a bigData devlopers

创建者 Kota M

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Apr 5, 2018

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

创建者 Anuj A

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Oct 22, 2020

Needs more detailing for datasets and dataframe apis

创建者 Wolfgang G

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Aug 29, 2017

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

创建者 Manuel W

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Apr 18, 2017

Would be better to have more and shorter exercises.

创建者 Ruslan A

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Aug 23, 2017

lectures don't correlate to practical assigment :(

创建者 David G

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Aug 25, 2017

Great course, but can be great idea have the ppts

创建者 Yuan R

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Jan 20, 2018

Great course that is very practical for the job.

创建者 Guillermo G H

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Jun 30, 2017

Great approach to learn about Spark in practice

创建者 Michaël M P

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Feb 5, 2019

Talk about how to set Scala version in Eclipse

创建者 林鼎棋

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May 29, 2017

Great! But I want to know more about dataset!

创建者 VeeraVenkataSatyanarayana M

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Jun 4, 2017

Basics are covered in an effective way.

创建者 Pavel O

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Aug 12, 2017

Good final course for Scala learners.

创建者 Lucas F

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May 15, 2017

Great lectures and great content!

创建者 Роман В

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Jun 24, 2018

I would like to learn some more.

创建者 Park H

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Apr 18, 2017

Learned Spark APIs, internals.

创建者 Alberto P d P

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May 12, 2017

Very good and concise course.