Chevron Left
Zurück zu Big Data Analysis with Scala and Spark

Bewertung und Feedback des Lernenden für Big Data Analysis with Scala and Spark von École Polytechnique Fédérale de Lausanne

4.7
Sterne
2,548 Bewertungen

Über den Kurs

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....

Top-Bewertungen

BP

28. Nov. 2019

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.

CC

7. Juni 2017

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!

Filtern nach:

251 - 275 von 506 Bewertungen für Big Data Analysis with Scala and Spark

von Devaki B

15. Apr. 2017

It was good. Got indepth knowledge of Spark API

von Harshad H

30. Okt. 2019

Best Course for Big Data Learning in the World

von David F S

14. Jan. 2019

Very informative. Well-organized presentation.

von Husain K

7. Mai 2017

Great course, learnt a lot from it. Thank you.

von samy k

21. März 2017

Interesting and challenging course! Thank You!

von Robert M

11. Feb. 2019

Excellent videos, explanation, and resources!

von shubham m

10. Juli 2018

good but give more practical of small program

von abdhesh

31. Dez. 2017

It was an awesome and well explained course.

von Jeroen M

9. Apr. 2017

Great course, well explained, instant value!

von Hong C

14. Apr. 2020

A perfect resource to get start with Spark.

von Denis L

5. Dez. 2018

Very nice, but a little bit outdated course

von Wang Z

30. Okt. 2019

The lecture is well-organized

and excellent

von Muhammad B

10. Juni 2020

Very brilliant instructor, learned a lot.

von Arnaud J

2. Juni 2017

Great course. Would definitely recommend.

von Daniel D

20. Apr. 2017

Great course - well prepared by the team.

von Olivier L

29. Nov. 2019

Very well explained, a very well teacher

von Marc K

8. Sep. 2018

Great course explained with great detail

von Joaquin D R

25. Sep. 2019

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

von jiajie

8. Juli 2017

Learn a lot things about spark. Thanks!

von César A

29. März 2017

Excellent course. Fun and entertaining.

von Hari K N

22. Juli 2020

It's an overall great learning session

von Varlamova E

10. März 2019

It was amazing!!! Very useful course!

von Msellek A

26. Jan. 2019

Great course ! Thanks for the effort

von Jose M N

28. Mai 2018

Great course. Thanks for everything.

von Srinivasa R M

13. Sep. 2017

Very nice explanation with examples.