Chevron Left
Retour à Big Data Analysis with Scala and Spark

Avis et commentaires pour d'étudiants pour Big Data Analysis with Scala and Spark par École polytechnique fédérale de Lausanne

4.7
étoiles
2,539 évaluations
519 avis

À propos du cours

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....

Meilleurs avis

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 juin 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!

Filtrer par :

376 - 400 sur 505 Avis pour Big Data Analysis with Scala and Spark

par Jose F O

25 déc. 2019

It is a great course however the exercises make you waste time trying to figure out how the grader works. You really need to read the instructions word by word then go to the discussions to figure out from others questions the pitfalls.

par Tony H

18 nov. 2017

It felt short at 4 weeks! I wish it was longer and presented an assignment with each new concept-cluster.

Great information and I appreciated Dr. Miller's efforts to simplify the newly taught concepts and present with concrete examples.

par Greg J

24 janv. 2019

Assignments are challenging but reasonable and can be completed in the estimated time. The assignments seem a little out of sync with the course, though. Material taught in week 2 was recommended to be used on the week 1 assignment.

par Tri N

29 avr. 2018

Excellent course, RDD, DataFrame, Dataset are better discussed in this course than most of Spark books. SparkSQL is light however. The missing star is because some code suggested by the course is more imperative than functional.

par Mark M

20 nov. 2017

Dr. Miller's lectures are clear and concise. An excellent intro to Spark! This would have gotten a 5 star rating from me, if not for the unfortunate inclusion of the awful kmeans problem from the Parallel Programming class.

par Adam R

25 août 2021

Enjoyed learning about apache spark and optimizations in distributed data processing. I still feel like I've only been introduced to spark. Maybe if there was a Spark 2 course? I would like more familiarity with this tool.

par Prateek G

15 avr. 2017

Informative. Although, it a week course on architecture of Spark (especially YARN mode), explaining Spark Jobs, Stages & Tasks would be nice addition. Thank you for sharing knowledge and a wonderful learning experience!

par Miguel D

3 avr. 2017

I learned a lot and I really enjoyed the course. What I would improve - reference material from upcoming weeks should be organized (or at least added as recommended reading) if it helps the current week assignment.

par Srinivas S

24 oct. 2018

The exercises were below the standard of previous courses. Also the instructions on exercises could have been better. Lost a lot of time figuring out as a new bee in Spark.

par Benjamin L T L

3 avr. 2020

some of the questions are unnecessarily specific (i.e. needs to be rounded to 1 decimal and sorted exactly for it to work)

but otherwise, great lecturer and great content

par Changli H

17 nov. 2017

although spark part is taught nicely, it also takes a lot of time to understand the sql part and remember a lot of sql operations as a zero background man in sql

par Alisdair W

20 avr. 2017

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.

par antonin p

25 févr. 2018

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

par Eduardo

16 juil. 2017

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?

par Du L

2 juin 2018

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

par Yilong W

11 mai 2018

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

par Vikash S

22 juin 2020

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

par MAHESH S

18 juil. 2017

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

par Tyler F

6 oct. 2018

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.

par Pravina

8 sept. 2018

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

par P.K

15 juil. 2017

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

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

par Frédéric D

18 juin 2017

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

par Valter F

29 mai 2019

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

par Björn W

10 avr. 2017

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

par Evgheni E

24 mars 2017

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.