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

4.7
étoiles
2,383 évaluations
494 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

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!

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.

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351 - 375 sur 477 Avis pour Big Data Analysis with Scala and Spark

par Simon M

19 avr. 2017

Gives a really solid overview of the foundations of the Spark programming model, where it came from, and how latency affects this model for a distributed cluster. Explains well the key differenced between RDDs, Datasets, and Dataframes. Thought the videos were unnecessarily long and could do with "sharpening" them up a bit.

par Isaac A

26 août 2020

This course helped me understand Scala and Spark main operations as well as their use cases. Moreover, exercise directions for Week 1, 2 and 4 should be more clear. To all students, you don't need to install Hadoop to complete this course. I recommend you to use IntelliJ over Eclipse.

par Viacheslav I

19 juin 2017

Very good course! Practical and industry-useful. Would be great only if there were a bit more programming assignments, with more fine grained structure, so that one could practice more in simple things, not only trying to fill out ??? marks left alone. Overall happy that participated!

par Nag K

28 févr. 2019

Course Assignments consumed more time than anticipated, as they required the knowledge from upcoming week's video lectures. Had it not been for someone mentioning about this in discussion forums, it would've consumed more time for me to complete the assignments

par Ashish M

6 juil. 2017

A nice course to start with learning basics of Spark with Scala, however it has missing things like broadcast variables, what are tasks/executors in Spark etc. The course is mainly around how to do distributed execution using scala via Spark, not the vice-versa.

par William S

29 juin 2017

On a scale of 1 to 10 with 10 being the most familiar with Scala that you can be, it is very helpful to be at least a 6 or 7 for this course to code everything efficiently. Concepts covered here are very helpful though and it is a useful introduction to Spark.

par Michael R

4 févr. 2019

Great course, but week 4 leaves out some key Spark SQL concepts that you need to finish the last project, such as the use of when(). Also, the part about DataSets is gone through rather quickly and without nearly as much detail as RDD and DataFrames.

par RonBarabash

18 mars 2017

Excellent in depth explanation of RDD and the API.

Heather is super informative and the material is being passed in a practical and explanatory way.

Hopefully there will be more courses like this one about Spark Streaming and Machine Learning.

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 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.