Chevron Left
Вернуться к Big Data Analysis with Scala and Spark

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

4.7
звезд
Оценки: 2,551

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:

326–350 из 506 отзывов о курсе Big Data Analysis with Scala and Spark

автор: Andrii P

•

9 апр. 2017 г.

автор: NGUYEN K B A

•

15 нояб. 2020 г.

автор: Henoc M

•

26 мар. 2017 г.

автор: Moncef Z M

•

6 сент. 2020 г.

автор: Rajesh K S

•

9 окт. 2018 г.

автор: Jose C A

•

5 мая 2018 г.

автор: JULIAN A G V

•

1 февр. 2018 г.

автор: Jose E T

•

2 июня 2017 г.

автор: Emiliyan T

•

9 апр. 2017 г.

автор: Light0617

•

14 апр. 2019 г.

автор: Saiteja t

•

1 авг. 2018 г.

автор: Hengyu

•

6 апр. 2018 г.

автор: Rafael M

•

18 окт. 2017 г.

автор: Mihir S

•

27 сент. 2017 г.

автор: Angel V

•

21 авг. 2017 г.

автор: Aleksey I

•

2 июня 2017 г.

автор: Roman I

•

5 апр. 2020 г.

автор: Kirill K

•

10 окт. 2017 г.

автор: William H

•

6 сент. 2017 г.

автор: jose a m l

•

13 июня 2020 г.

автор: Sanjeev R

•

26 авг. 2019 г.

автор: Ngoc-Bien N

•

4 апр. 2019 г.

автор: D S

•

17 янв. 2018 г.

автор: Mohamed K

•

30 окт. 2017 г.

автор: Pengcheng L

•

5 июня 2017 г.

Coursera Footer

Начните карьеру или продвиньтесь по карьерной лестнице

Смотреть популярные темы

Популярные курсы и статьи

Получите диплом или сертификат онлайн

Coursera

Сообщество

Еще

Учитесь, где угодно
Placeholder
Placeholder
Placeholder