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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: 2,383
Рецензии: 494

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:

251–275 из 477 отзывов о курсе Big Data Analysis with Scala and Spark

автор: Olivier L

29 нояб. 2019 г.

Very well explained, a very well teacher

автор: Marc K

8 сент. 2018 г.

Great course explained with great detail

автор: Joaquin D R

25 сент. 2019 г.

Incredible tutorial!!!!!!!!!! I love it

автор: jiajie

8 июля 2017 г.

Learn a lot things about spark. Thanks!

автор: César A

29 мар. 2017 г.

Excellent course. Fun and entertaining.

автор: Hari K N

22 июля 2020 г.

It's an overall great learning session

автор: Varlamova E

10 мар. 2019 г.

It was amazing!!! Very useful course!

автор: Msellek A

26 янв. 2019 г.

Great course ! Thanks for the effort

автор: Jose M N F

28 мая 2018 г.

Great course. Thanks for everything.

автор: Srinivasa R M

13 сент. 2017 г.

Very nice explanation with examples.

автор: Martin A

3 мая 2017 г.

Great course, good intro into Spark.

автор: Manuel M C

23 мар. 2017 г.

Great course, keep up the good work.

автор: Neeraj V D

27 февр. 2018 г.

limited content with dewp knowledge

автор: Abhay D

4 нояб. 2018 г.

Wonderful course. Helped me a lot.

автор: David M

18 сент. 2017 г.

Concepts are very well explained..

автор: Liu D

26 июля 2017 г.

Great speeches with great exercise

автор: Fernando R

28 окт. 2017 г.

it was a super interesting course

автор: Alejandro R C

13 авг. 2017 г.

Everything was easy to understand

автор: Jinfu X

12 мар. 2017 г.

Thanks! It's an excellent course.

автор: Fedor C

31 авг. 2017 г.

Very interesting course! Thanks!

автор: Vasyl Y

26 июня 2017 г.

Cool course! Thanks for your job

автор: Kyle L

10 июня 2017 г.

very good course, really enjoyed

автор: Alex S

5 мая 2018 г.

Super course, well done Heather

автор: Jong H S

18 авг. 2017 г.

A wonderful and timely course.

автор: Jon Z

5 июля 2017 г.

Great course, I learned a lot.