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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
2,368 évaluations
488 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

Nov 29, 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

Jun 08, 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!

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351 - 375 sur 471 Avis pour Big Data Analysis with Scala and Spark

par William S

Jun 29, 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

Feb 04, 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

Mar 18, 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

Dec 25, 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

Nov 18, 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

Jan 24, 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

Apr 30, 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

Nov 21, 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

Apr 15, 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

Apr 03, 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

Oct 24, 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

Apr 03, 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

Nov 17, 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

Apr 20, 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

Feb 25, 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

Jul 16, 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

Jun 02, 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

May 11, 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

Jun 22, 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

Jul 18, 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

Oct 06, 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 S M

Sep 08, 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

Jul 15, 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

Jun 18, 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

May 29, 2019

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