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Bewertung und Feedback des Lernenden für Big Data Analysis with Scala and Spark von École Polytechnique Fédérale de Lausanne

2,548 Bewertungen

Über den Kurs

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:



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.


7. Juni 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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26 - 50 von 506 Bewertungen für Big Data Analysis with Scala and Spark

von Apostolos N P

15. März 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.

von Marcus E

9. Apr. 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!

von Li Z

11. Aug. 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.

von Igor Y

29. Mai 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.

von Imran K

8. Apr. 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.

von Zhaokang P

17. Sep. 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.

von Akash P

12. März 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.

von Ignacio A

17. Apr. 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.

von Yury C

18. Juli 2019

This course was by far the best of 22 courses I've done on Coursera. Prof Miller has this rare ability of presenting material in concise and interesting way and yet going into nitty gritty aspects when needed (in another course on Spark technology, such intricacies weren't covered). Thanks a lot for this course!

von Ananda P V

31. Aug. 2017

excellent introduction to Spark. I was always looking for some course which touches the underlying functional aspect of Spark then just showing syntactic values. Moreover this course also teaches how parallelism and distributed process works with Spark. You also get an idea why Spark is written in Scala.

von Doug F

7. Aug. 2020

I thought it was very good, hands on. I think they need to add a lesson 3 homework to bridge the gap between the lectures. Week 4 lecture is really long. The HW4 is a bit tricky and requires alot of research and lack of any provided unit tests makes it alot of work to figure out what is wrong.

von Xiongchu W

5. Aug. 2017

This course is indeed introductive to learn all the necessary stuffs about Spark. It is pretty good to tell much about shuffling. Because we should not only be familiar with how to operate on Spark, but we should really have a good understanding of what's going on underneath the hood. Thanks!

von Deepika S

19. Juni 2020

It is a great course with excellent material.

There is a lot to try in this course, concepts that one has to try oneself which opens opportunities to learn.

Please go ahead with the course if you are starting to work on Scala and Spark. Iterations run great on a decent 8 GB 64 bit machine.

von Aldrin

10. Juli 2017

The information was explained really well for someone coming in with no spark and minimal scala knowledge. Due to this course I understood basic spark concepts well enough to begin understanding pipelines built on top of spark such as ADAM.

I would highly recommend this course to everyone.

von Edgar D

10. März 2019

Favorite so far out of the Scala Specialization Course. It was executed really well, and taught really well, too. Kinda wish they would add more exercises to help us get more experience with some of the concepts, but that's something you can always just do by yourself either way.

von Emanuel O

22. Okt. 2017

Excelente curso. Já trabalhei com Spark, mas tive a oportunidade de aprender muitas coisas novas sobre ele. Achei muito interessante a abordagem sobre as novas estruturas de dados que chegaram nas novas versões (para mim, pelo menos, pois trabalhei com a versão 1.6). Recomendo.


4. Feb. 2022

Excellent course, the contents are very in details and comprehensive, ​slids are so cool and assignments are very fun and practical, except the last one timeusage which is a little bit hard, however, i really enjoy the learning process. Fabulous course, Fabulous teacher

von Markus B

9. Apr. 2017

Great course overall. The feedback on failed tests and out of memory errors on the assignments can be improved to make it more user-friendly.

Would be great to see a more advanced version of this course that dives deeper into the machine learning features of Spark, etc.

von Jaseer A

23. Dez. 2017

Really enjoyed this course. Unlike previous courses where I had to wrestle with algorithms and only learn the subject as a side effect, the assignments in this course directly addressed the subject. One of the best in this series except for the first course in scala.

von Mugren A

20. Aug. 2020

Perfect. One of the most summarized course, to the point, and straight forward without any trouble. Special thanks to Prof. Heather for focusing on the optimization part and performance and easily explained the lazy transformation and how it could highly cost us.

von Gustavo H L d S

31. Mai 2020

Such a great course! Prof. Miller gives a very good coverage about spark, it's perfect for beginners who never deal with his technology and advanced users who wants o refresh their knowledge about this subject. The didactic of teaching is also excellent!

von Šejla Č

20. März 2019

Brilliant lecturer and slides! The only problem is assignments not being clear on the expected outputs. Sometimes it takes more time to figure out what exactly is asked than to find the solution. A few more test cases or detailed examples would help.

von Kevin L

2. Apr. 2019

I loved this course - it was a great introduction to Spark. At the end, I wasn't (and am still not) clear on type-safe operations on Datasets, and now to write Tests to verify this.

These will be one of the targets of my upcoming research and study.

von AJ C

9. Apr. 2017

This course is a nice guided way to get started with Apache Spark! I wish I took this course earlier when i was using Spark at my previous company. It would've come in handy. Plenty of great content and the course exercises supply good practice.

von Abhishek K

11. Okt. 2020

The course has a great content. The assignments are a bit tough for beginners but if you keep on trying, you will get some great insights of the technology. Also has the best and unique use case scenarios which keep questioning your knowledge.