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
2,333 évaluations
477 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

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!

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.

Filtrer par :

426 - 450 sur 460 Avis pour Big Data Analysis with Scala and Spark

par Luis V

Oct 01, 2017

Good course but with many outdated concepts (mostly valid for Spark 1.x) and some pitfalls. Need many improvements, actualization and some reshaping in the distribution of the topics and sessions of the course. The topics left for the last week are some of the most important and central in current Spark 2.x and they include at least as many fundamental concepts as the rest of the course.

par Jeff B

Jul 05, 2017

The course content is fine, I did learn about Spark and how to work with it in Scala. The only disappointing thing is the homework assignments all use Spark in local mode, so it runs only on my laptop. I really wanted to see an assignment run in a real cluster, for real. So having some kind of cookbook recipe to follow for putting an assignment into Databricks would caus

par Vesa P

Jul 03, 2017

I learned a lot and the lectures were good.

The feedback from the automatic grader was sometimes absolutely awful. Cache an RDD in the main -> grader does not execute main -> OOM exception with no stack trace. I guess it prepares you for real life since EMR Spark also has absolutely useless log output if you go OOM.

Thank goodness for forums.

par Lance F

Mar 27, 2017

This course took a lot of work to create. I would have like more quizzes during the lectures and the assignments to have walk through the steps more. The best course I have seen online is the Machine learning course by Andrew Ng. https://www.coursera.org/learn/machine-learning/home/welcome.

I did really enjoy the course. Thank you.

par Aaron S

Jun 04, 2017

Very average. Lectures could be a fraction of their current length, too much time spent rephrasing the same point (sometimes 3+ times!). It was driving me nuts, my mind would wonder if I didn't focus. It would be nice to have local tests that incrementally check progress similar to Andrew Ng's Machine Learning coursera.

par Rafael G

Mar 31, 2017

The material in this course is very interesting. However, there were a few important issues:

Lots of typos in the slides

Lots of problems with the assignments

At the end, I feel like a beta-tester (it would be OK if it was clearly stated and if we had a discount).

It could also be nice to add 1 or 2 weeks to this course.

par Korbinian K

Oct 10, 2017

I really liked the lectures and the good and fun explanations by the instructor. However, I found the assignments over complicated with unnecessary machine learning concepts involved. I think a course about Spark should be about core Spark ONLY and applications to machine learning should happen in a separate course.

par Andre H

Aug 05, 2017

The material of the fourth week is quite dense, this could be split over two weeks (including splitting it into two exercises). The exercise of the fourth week is quite a dissatisfying experience, there is too little detail in the error messages about what failed for students to improve their solution.

par Jeni R

Nov 30, 2019

It felt like the course material skipped over a great deal of syntax and how-to. It was useful for concepts; but I found that I had to dig a great deal to be able to complete the assignments and that there is a lot of folklore in stackOverflow that potentially send you in a wrong direction.

par Alexandre V

Nov 25, 2017

Explanations are OK and it's a good investment. However, I'm mixed about the courses: the teacher is speaking really fast with slides full of text. It's sometime hard to keep my concentration (compared to previous courses of the specialization). Still, I would recommend this course.

par Evgeny K

Jul 24, 2020

a very basic and shallow introduction:

-spark dataframe usage scenarios and syntax poorly covered

-homeworks are too basic and do not prepare for real life scenarios

-lots of typos in the slides

-pdf slides are not searchable for terms

-how is this course rated 4.7 (as of 24-07-2020)?

par Daniel Z

Mar 14, 2020

The assignments are not really well prepared - there is tests provided which is really needed for big amount of data - sometimes it really hard to find a bug. If you tell me that this is my problem to right tests - I'll tell you that I've paid money for that.

par Yann L M

Mar 19, 2017

Lectures are great. Explanation are very clear. Assignment was having issue like incorrect and/or vague reporting which made them needlessly painfull. I'm quite sure that the next iteration of this course can get a 5 star rating, but for now, it's only 3.

par Benjamin S

Sep 12, 2017

The lectures are really great with vivid and easy-to-follow explanations on complex topics.

However, the exercises don't seem to match the lectures very well and may confuse you. I would prefer to apply the things I learned in the lectures.

par Gian U L

May 07, 2017

In the assignments, I had the feeling that the goal was more "guess what they want" than "write it correctly using what you have learnt". Stating more clearly the requirements and improving error messages from the grader may help.

par Aaron H

Jan 23, 2018

Instructor was good and knew what she was talking about. The assignments were also good, but the grading was weird. Spent a lot of time try to figure out the unwritten requirements that would make Coursera's tests pass.

par Serg D

Apr 14, 2020

It was a good course, much more useful than the first one in the series. I would say too much focus was on the SQL side and not enough on the big data side, which is what i was hope to get from the course.

par Nikita V

May 11, 2017

I guess it's a goo introduction course into Apache Spark. Meantime I would expect deeper dive into optimizations and algorithms.

par Moiseenko A

May 06, 2020

There is not any course support at all.

Provided drafts of program assignments does not satisfy SOLID and Clean-code principles

par Kyle J

May 22, 2019

Pretty good, but one of the assignments was poorly set up. Some of the provided code was broken and it was very hard to debug.

par Virginija D

Aug 07, 2017

Too entry-level after the first two much more challenging courses by M. Odersky.

par Mortatha K H

Jul 08, 2020

it's hard for beginner understanding this course

par Ioannis A

Sep 25, 2018

course needs to be updated

par José F

Apr 18, 2017

a let down and not up to par with other courses in the series.

Huge amount of time is wasted bsically repeating that the API is close to scala collections'. The huge amout of time is wasted again on very simple dataframe APIs including several slides presenting the show() function. Allow me to repeat : several slides presenting the show() function.

Finally the assignments are unimaginative and mechanical. The desciption is really confuse and most of the time is spend trying to chase small differences away to please the grader.

The sole part of the course that seemed interesting was the shuffling one, that was unfourtunately ignored on the assignments

Not related to the course but to spark: What utter mess are dataframes and datasets filled with boilerplate type conversions and runtime erros. I shy away in disgust form this untyped IDE-unfriendly monstruosity.

par Vladyslav S

May 06, 2017

Relatively decent video lectures, if not that blurry which makes text hard to read. Accompanied with awful practice lessons: - code templates are written with little to no style, even file reading is done in 3 different ways in all 3 lessons; - grader output is very confusing and almost useless; - unit tests, very useful to avoid some common caveats, were present in the first lesson, disappear completely in the last one.

Probably following spark's programming guide is better time investment, even if it misses some "humanity" of video lectures