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Отзывы учащихся о курсе Big Data Analysis with Scala and Spark от партнера Федеральная политехническая школа Лозанны

Оценки: 2,540
Рецензии: 520

О курсе

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:

Лучшие рецензии


28 нояб. 2019 г.

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.


7 июня 2017 г.

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!

Фильтр по:

426–450 из 505 отзывов о курсе Big Data Analysis with Scala and Spark

автор: Francis T

16 апр. 2017 г.

I really liked the content regarding Dataframes and Datasets.

автор: Emmanouil G

1 апр. 2017 г.

Assignment Instructions need improvement in terms of clarity.

автор: Gongqi L

9 апр. 2017 г.

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

автор: kaushik

9 апр. 2017 г.

Good course ! But does need more programming assignments

автор: Mohammad T

24 авг. 2019 г.

such a beautiful course design for a bigData devlopers

автор: Kota M

5 апр. 2018 г.

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

автор: Anuj A

22 окт. 2020 г.

Needs more detailing for datasets and dataframe apis

автор: Wolfgang G

30 авг. 2017 г.

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

автор: Manuel W

18 апр. 2017 г.

Would be better to have more and shorter exercises.

автор: Ruslan A

23 авг. 2017 г.

lectures don't correlate to practical assigment :(

автор: David G

25 авг. 2017 г.

Great course, but can be great idea have the ppts

автор: Yuan R

20 янв. 2018 г.

Great course that is very practical for the job.

автор: Guillermo G H

30 июня 2017 г.

Great approach to learn about Spark in practice

автор: Michaël M P

5 февр. 2019 г.

Talk about how to set Scala version in Eclipse

автор: 林鼎棋

29 мая 2017 г.

Great! But I want to know more about dataset!

автор: VeeraVenkataSatyanarayana M

4 июня 2017 г.

Basics are covered in an effective way.

автор: Pavel O

12 авг. 2017 г.

Good final course for Scala learners.

автор: Lucas F

15 мая 2017 г.

Great lectures and great content!

автор: Роман В

24 июня 2018 г.

I would like to learn some more.

автор: Hoon P

18 апр. 2017 г.

Learned Spark APIs, internals.

автор: Alberto P d P

12 мая 2017 г.

Very good and concise course.

автор: Javier L B

7 дек. 2021 г.

Good course.

автор: Stéphane L

13 окт. 2017 г.

Very useful

автор: Srinivasu N

15 мая 2020 г.