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

4.7
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Оценки: 2,443
Рецензии: 505

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: https://www.coursera.org/learn/parprog1....

Лучшие рецензии

CC
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!

BP
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.

Фильтр по:

376–400 из 488 отзывов о курсе Big Data Analysis with Scala and Spark

автор: Alisdair W

20 апр. 2017 г.

Great course, I learned a lot through the course. However, some of the lectures are quite long and could do with being broken down in to more smaller segments.

автор: antonin p

25 февр. 2018 г.

Great Sparks introduction. Still sometime unsure about the distributed vs local : should I compute this or that locally ? Or in a distributed manner ...

автор: Eduardo

16 июля 2017 г.

Quite insightful as a first or second approach to Spark. After being introduced to Spark dataframes, what's the value of Scala API over the Python one?

автор: Du L

2 июня 2018 г.

Very good introduction to spark. The assignment would be better if they were more targeted at spark, the underlying working of spark, efficiency etc.

автор: Yilong W

11 мая 2018 г.

Very practical course. You can quite freely apply the course material to the programming assignments. I feel like I really learnt Spark in details.

автор: Vikash S

22 июня 2020 г.

The spark internal details was quite descriptive for few topics. Need to add more topics mostly related to transformation and spark submit flow

автор: MAHESH S

18 июля 2017 г.

Introduction to kmeans or asking to read about kmeans would have helped. I found programming exercises more difficult then some other courses.

автор: Tyler F

6 окт. 2018 г.

Somewhat specific, hard to reuse knowledge but do recommend if you're someone who works with Spark or even just work with someone who does.

автор: Pravina

8 сент. 2018 г.

It would be great if there are 2 assignments covering dataframes and datasets spanning week3 & week4 instead of week 3 with no assignment

автор: P.K

15 июля 2017 г.

Way Much Better Presentation than the previous 2 courses in this Specialization!!!

Dr Heather and M. Odersky are really good professors!!!

автор: Frédéric D

18 июня 2017 г.

With this course, I surely improved my knowledge about Spark... But I am still thinking that Spark is an overly intricate framework.

автор: Valter F

29 мая 2019 г.

I love the indepth aproach at the RDDs. I'd say DataFrames and DataSets required a bit more examples and testing material though.

автор: Björn W

10 апр. 2017 г.

Quizzes in the lecture videos would be nice. Also more, but shorter videos would be enjoyable. Programming assignments very nice!

автор: Evgheni E

24 мар. 2017 г.

The video speed is way to fast, this woman is speaking really fast, first as i slowed the video down at 75% was its ok.

автор: Rudolf Z

29 окт. 2017 г.

Good course. Lectures intoduce main concepts of spark very well. Also good explained how spark works under the hood.

автор: Jose R

8 февр. 2018 г.

Creo que es un curso muy educativo y muy práctico con retos que permiten conocer las herramientas en profundidad

автор: Andrejs A

8 янв. 2020 г.

Specially the last lectures where useful. but there is quite some gap between the lectures and practical task.

автор: Léo Z

7 мая 2017 г.

Good course. Could be more comprehensive about analysis. Content is all video and Lecture notes would be nice.

автор: ANKIT S

25 окт. 2018 г.

Very good and knowledgeable course. It basically explains about spark core, dataframe, dataset and spark sql.

автор: Łukasz G

25 апр. 2017 г.

Good introduction to Spark. One thing that I didn't like was use of simple Ints and Strings instead of types.

автор: CarloNicolini

7 апр. 2020 г.

Learned a lot of new things, but I would have focused on more but shorter and more variegate assignments.

автор: Ting T W

9 мая 2020 г.

Nice course, get a fundamental knowledge of spark, scala. Homework is good with a decent level.

автор: Patrik

13 янв. 2020 г.

The videos had focus issues every now and again. It was still possible to see everything shown

автор: Alberto A

29 июня 2017 г.

Good stuff, well presented, but the instructor takes no risks to explain more complex details.

автор: aknin k

25 дек. 2019 г.

Bien mais j'aurais aimé avoir plus d'exercices pour s'entrainer sur les dataframes / datasets