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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
2,361 évaluations
486 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.

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1 - 25 sur 469 Avis pour Big Data Analysis with Scala and Spark

par Rodion G

Apr 15, 2019

Course is good to have some practice in spark and scala. However it seems to be long forsaken by staff and some issues with assignments require doing archaeology in the forums. Also it is quite unpleasant to see that while specialization emphasizes functional programming, some auxiliary code in assignments is written in the worst manner of imperative programming... Why use scala then?

par Luiz C

Jan 27, 2019

If you know the subject, may be easy. If you don't know the subject, too dense and abstract with not enough practical examples/exercises before the assignments (specially for week4). Would have been better to break the videos into smaller ones, include more practical concrete examples/exercises during the videos (as very well done by Pr. Obersky during the 1st Course of the Specialization). There could have been room to break week4 into material for 2 weeks. Still, inresting.

par Kuntal G

Apr 01, 2017

Very Nice and effective course. One of the best course i have done on Spark online. Many Thanks to the course instructor Heather Miller for creating a very detail and updated course on Spark.

par Stephen E R

Mar 27, 2017

A superb course taught by a superb lecturer. From a lecture stanpoint, my only recommendation is that additional lectures be provided to flesh out the coverage of Datasets. I found the assignments challenging and a good reflection on my understanding of the course material. I would recommend only the following tweaks:

Week 1 assignment: Either tightly specify the function that determines if a particular String is present in a large textstring OR provide the function in the assignment skeleton. There are too many ways to approach this function that are correct, but different than the one the grader used. This function is only incidental to the goal of applying one's knowledge of Spark to the assignment.

Week 4: Allocate additional lectures to flesh out the coverage of Datasets. The subject was not covered to the same level of detail that the other subjects in the course were given.

These are minor points.

Well done, Dr Heather Miller!

par Choy R

Apr 10, 2017

Great introduction to spark. Fun assignments. Since it was the first ever session, there were quite a few kinks with the assignments. But the discussion forums rescued me any time I was stuck.

par Pavel T

Apr 06, 2017

Very interesting course. Heather presents at just right level of abstraction to my taste and her presentation is very lively so it is easy to stay focused.

Formally, this is a course on a particular set of software tools and many such courses are not very useful unless one is to start applying learned skills immdeiately because the material starts getting obsolete the day the course is passed.

I believe that this course, however, teaches important priniples that outlive the particular toolkit, in particular the art of reasoning about algorithms on distributed collections, in particular, their performance. One would expect this to be a complex subject but somehow this course makes it feel simple -- which is a good indicator of high quality.

par Florian W

Apr 01, 2017

It's a great course with lots of insight into spark. At first it's confusing - first rdds; then dataframes and later datasets. But this is by no means the instructors' fault. Heather greatly describes in detail what goes on under the hood and why dataframes are way faster than simple rdds and why we have to give up some of our beloved typesafety of scala-land to let catalyst do its magic. Datasets try to bring some of it back.

I really enjoyed coding the assignments. Took me ~2hrs each. The tasks were fun and like those you would find in the wild.

I already applied some of the skills I learned here at work and successfully implemented a simple recommendation engine that will go to production next week.

Highly recommend this course!

par Adel F

Jan 08, 2018

Course is solid, useful concepts are thought. Assignments are interesting.

Points for improvement:

I wish this course was 2-3 courses focusing on topic with more assignments. If a concept is reviewed by the student is not challenged with tough questions the concept is not learned. People taking this course are already advanced enough to tackle difficult challenges, sometimes it appears that instructor assumes that students are entry level.

A note for online instructors: avoid jokes. They do not work online.

I think a specialization on applications of spark with scala covering AI, graph and text processing would be interesting. Overall thanks for the effort; pretty good.

par Shae S

Mar 23, 2017

I learned so much from this course! It was amazing how Dr. Miller used concepts that were meticulously built up in the earlier courses, such as evaluation strategy, functional collections, reactive programming, and associativity, to describe the core of Spark in only four units. As someone coming from more of a statistics background, I started this specialization only to learn Spark, so I wasn't always sure how relevant learning the more theoretical underpinnings of Scala would be. It turns out that it was pretty essential, while also just making me a better programmer. Looking forward to the capstone!

par Massimiliano D

Nov 14, 2018

Great course over Spark. It shows the syntax, but more than this, it shows the problems, the caveats, the optimization and the architecture from a wide point of view.

The assignment were designed to focus right to the point: they managed all the configuration and initialization code of the project, leaving the student to fill only the most important part, with the resulto of having, at the end of the course, working projects that could be used as "cheat sheet" to remember all the material of the course.

Massimiliano.

par Yaroslav G

Apr 08, 2020

I like the course. Very interesting and stimulating. Week 4 is a bit cumbersome, but it is due to a big amount of topics covered in it. I think that it could be better to have a DataSets and DataFrames brief overview in this course and study these topics in detail in separate advanced courses.

Assignments are designed to make us spend some time reading the docs. I think that it is good, as by doing so I have learned a bit more.

Many thanks to Prof. Heather Miller!

par Kushagra V

Jun 14, 2017

Very nicely taught. Liked these "long" lectures of 15-20 min where the instructor gradually builds the material around the topic. Most other courses online, especially Udacity has frustratingly short videos where more than half the time the student has to keep clicking to the next video. Material coverage is sufficiently wide as well and the curriculum is freshly designed which is very important in this field.

par Joël V

May 17, 2019

The course presented the concepts in a clear and exemplified way. Tough, it was a bit too verbose.

The exercises were not too hard and thorough enough to cover the concepts presented in class.

I wished there were more exercises as Spark's API is huge and sometimes a bit tedious to use thus being hard to quickly get confident. I hope that the capstone project will help for that.

par Anna B

Mar 20, 2017

The course covers the important concepts and explains them in detail.

Homework tasks really make you think, revisit the lectures and read documentation, until you get it right, and that all deepens understanding of the material.

I also obtained some clues for the future: the lectures provided me with context, which helps formulate questions when searching for answers elsewhere.

par Hristo I

Apr 09, 2017

This is a great course on the intricacies of Apache Spark. It is not a general Big Data course, neither is it an easy one. Doing the programming assignments properly requires reading a lot trough the Spark documentation, which I personally liked as part of the challenge, but beware if you are not that type of person or aim at finishing the course as quickly as possible.

par Hessam S M

Mar 25, 2018

Excellent course. I had some knowledge about Spark before but this course helped me to leverage my skills from pure theory to something practical. Now I have the confidence I can use spark to solve real life problems. This specialization as a whole is totally changing my career prospect. Thank you EPFL, Dr. Miller and Coursera for providing such opportunity for me.

par Varun R

Sep 22, 2017

Greate course to get an overview of Spark. Heather's teaching style is awesome. Her style really helps to connect the dots. Explains a lot on 'why' along with 'how'. To get full benefit, do as a continuation of other scala courses in the specialization. It would have been nice if there was a gentle intro to other areas like ML and Streaming. Great course.

par Apostolos N P

Mar 15, 2017

I really enjoyed this course! First of all I would like to congratulate the people behind this effort. The videos are clear, to the point and they contain very useful information and tips that are very difficult to get from a book. I hope that you will continue with a second course on Spark and Scala with more advanced topics. Thank you very much.

par Marcus E

Apr 09, 2017

The course gave me insight into the world och big data batch processing and how Spark solves it. Heather does a great job with presenting the material in a thorough way with relevant theory and illustrative examples. The assignments are well balanced and forces you to apply all your new knowledge when solving them. I highly recommend this course!

par Zhu L

Aug 11, 2017

An introductory course to spark programming, lectures are well-balanced between theory and boier-plate codes, but programming assignments are mainly about teaching you the APIs. The problem-solving part is basically trivial, whereas most of the time were spent on searching for API documents and correcting compiler errors and runtime exceptions.

par Igor Y

May 29, 2017

This is my first cource review on English, but I want to do accent on Heather's high professionalism, great explanations abilities and great organisation of material. I need to say that I have education of computer science teacher and I can say that it's wonderful cource. It was difficult for me and I've really increased my skill. Thanx.

par Imran K

Apr 08, 2017

As always, Coursera delivered another top quality courses on Spark with Scala. I have learned a lot of details, understood the underlying working principles of Spark in the last few days. Thanks to Dr. Miller for such a great course. I hope in the future versions of this course the overall presentations will be more smooth and typo-free.

par Zhaokang P

Sep 18, 2017

this course help me form a basic understanding of Spark and how to use it to analyze large scale dataset. Besides fundamental knowledge of how to use, the lecturer also provide students with some deeper concept of how to optimize the performance of spark programming, which can be very useful in running code on large dataset.

par Akash P

Mar 12, 2018

Thank you Dr. Heather Miller and the EPFL team along with coursera team for this course. I found it interesting. It gave me complete insight of spark. I had a great start with spark.

The internal working of spark API, the shuffle operations, query optimization and many more tips are really useful. Thank you once again.

par Ignacio A

Apr 17, 2017

This course is a great introduction to Spark. The only thing I think could be improved is that each programming assignments has unit tests that drive students towards the final solution. I saw lots of people complaining in the forums about this slow and tedious back and forth of having the grader testing their code.