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

4.7
звезд
Оценки: 2,432
Рецензии: 504

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 487 отзывов о курсе Big Data Analysis with Scala and Spark

автор: Saiteja t

1 авг. 2018 г.

Nice session

автор: Hengyu

6 апр. 2018 г.

very helpful

автор: Rafael M

18 окт. 2017 г.

Great Course

автор: Mihir S

27 сент. 2017 г.

Good Course.

автор: Angel V

21 авг. 2017 г.

very usefull

автор: Aleksey I

2 июня 2017 г.

Good course.

автор: Roman I

5 апр. 2020 г.

good cource

автор: Kirill K

10 окт. 2017 г.

A good one.

автор: William H

6 сент. 2017 г.

Outstanding

автор: jose a m l

13 июня 2020 г.

Excelente

автор: Sanjeev R

26 авг. 2019 г.

Excellent

автор: Ngoc-Bien N

4 апр. 2019 г.

bon cours

автор: SAIDULU D

17 янв. 2018 г.

Excellent

автор: Mohamed K

30 окт. 2017 г.

Perfect !

автор: Pengcheng L

5 июня 2017 г.

Thanks :)

автор: Huajian M

4 апр. 2017 г.

So great!

автор: 李帅

1 мая 2019 г.

Perfect!

автор: IURII B

7 авг. 2017 г.

Thanks !

автор: Estera K

20 мар. 2017 г.

AWESOME!

автор: Satendra k

9 апр. 2017 г.

Thanks

автор: Вьюн С А

27 февр. 2020 г.

Nice!

автор: Kiệt Đ

1 июля 2017 г.

Best

автор: Bianca T

22 апр. 2017 г.

Taking into consideration that this was the first edition of the course, I can say that it has been a nice journey. I am glad about the fact that Heather managed to expose a bit of the Spark internals and not only talk about querying data and how easily this can be made by using Spark (as most of the Spark oriented courses consist of).

In addition to this, I could listen to Heather all day long - she's a great presenter and has wonderful teaching skills.

However, the homework has outlined some neglected aspects of the course:

- vague description or requirements

- not strongly related to the presented content (the lectures outlined partitioning mechanism, but the homework 2 did not require it...)

- not so meaningful feedback, except for some tests failing/passing - I would have expected something like you did ok, but your job took longer than expected; check out this and that

Overall, it's been a highly expected course and it was nice to get a broader outlook on Spark. I hope that there will be more courses (and more detailed) related to Spark ecosystem in the near future.

автор: Anton M

19 июня 2020 г.

Really enjoyed most part of the course, it was a fun ride with Spark !

Explanations of lector was crystal clear and I liked all assignments (except last one)

There are some cons though:

-> Week 3 contains no assignment, I would prefer to have one really dedicated to "Partition and Shuffling" subject

-> Spark SQL explanations about untyped were too much shady. It somehow feels like this API goes totally orthogonal to everything functional we have had so far. It's like running in Java but using C with JNI... Well, after all, it's a drawback of API, not course itself, but still having bit of aftertaste of fighting with Scala type system trying to glue SQL... meh

-> there are many missed opportunities to have proper Coursera quiz during lectures

автор: Robin B

4 июля 2019 г.

Very good introduction to RDDs and DataFrames/Dataset along with valuable insight into performance considerations.

I'd done some prior work with Hadoop/Pig in the past and more recently with Spark (mainly DataFrames/GraphFrames) - this was really useful to round out my understanding of RDDs and optimisation.

The assignment guidance in the code comments could be more complete to save having to refer back to the site (and maybe reference specific video lectures with the hints). Though it's good that the assignment exercises aren't tutorial-grade, as that makes the experience more transferable to real projects.