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Отзывы учащихся о курсе Big Data Analysis with Scala and Spark от партнера Федеральная политехническая школа Лозанны

4.7
звезд
Оценки: 2,443
Рецензии: 505

О курсе

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....

Лучшие рецензии

CC
7 июня 2017 г.

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 нояб. 2019 г.

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.

Фильтр по:

351–375 из 488 отзывов о курсе Big Data Analysis with Scala and Spark

автор: Robin B

4 июля 2019 г.

Very good introduction to RDDs and DataFrames/Dataset along with valuable insight into performance considerations.

I'd done some prior work with Hadoop/Pig in the past and more recently with Spark (mainly DataFrames/GraphFrames) - this was really useful to round out my understanding of RDDs and optimisation.

The assignment guidance in the code comments could be more complete to save having to refer back to the site (and maybe reference specific video lectures with the hints). Though it's good that the assignment exercises aren't tutorial-grade, as that makes the experience more transferable to real projects.

автор: Saurabh M

8 апр. 2017 г.

Dr Heather has done an outstanding job to create this unique material with a fine combination of theoretical and practical aspect of Spark. She has covered almost everything from basic to complex, but there is some area which demands more time from the creator for its explanations. Since this is first time launched course and definitely going to improve itself in upcoming days. I enjoyed this course thoroughly. It helped me, cementing my basic concept of Spark.

автор: Ellen K

2 апр. 2017 г.

The structure, focus content of the videos of this course are great. The assignments are so-so. They do practice writing reasonably realistic Spark jobs in Scala, but it is hard to draw the connection between the more theoretical videos and the very practical assignments. Also, the assignments are hard to solve due to being poorly specified and there being hardly any helpful output from the auto-graders used to evaluate assignment submissions.

автор: Mykyta P

18 авг. 2019 г.

The video lectures are good but code assignments are worse, seems like they were written by students instead of professor or something. Sometimes code doesn't follow Scala and FP conventions. And the output of the grader doesn't really provide any helpful information besides the name of the faulty function. But overall it's a good course and I think the newcomers without any previous experience with Spark will learn a lot.

автор: Chet W

29 янв. 2018 г.

Great lectures but the exercises felt contrived sometimes. Especially the exercise on PCA didn't really seem to provide that much insight into the data or illustrate the usefulness of the algorithm (especially when compared to the parallel programming exercise which had a great use for PCA). However the last exercise was good and forced the student to really explore the spark API. Learned so much from this!

автор: Sergio

25 июня 2017 г.

I thought this was a decent course. I enjoyed the exercises and thought it gave a good introduction to Spark. Some of the lectures in Week4 were a bit long and the material needed to complete the Week4 exercise wasn't in the lectures. It would have been nice to have a 'conclusion' lecture wrapping everything up instead of just ending the course on a DataSet's lecture.

автор: Philip R K

11 апр. 2017 г.

Generally a really good introduction to Spark. What I found disturbing though were the very imbalanced difficulties of the excercises and the rather uninformative test messages that did not help for the implementation. There was no course where I had to search for other peoples suggestions in the forums.

Still the course was good -- I would do it again!

автор: Simon M

19 апр. 2017 г.

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.

автор: Isaac A

26 авг. 2020 г.

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.

автор: Viacheslav I

19 июня 2017 г.

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!

автор: Nag K

28 февр. 2019 г.

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

автор: Ashish M

6 июля 2017 г.

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.

автор: William S

29 июня 2017 г.

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.

автор: Michael R

4 февр. 2019 г.

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.

автор: RonBarabash

18 мар. 2017 г.

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.

автор: Jose F O

25 дек. 2019 г.

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.

автор: Tony H

18 нояб. 2017 г.

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.

автор: Greg J

24 янв. 2019 г.

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.

автор: Tri N

29 апр. 2018 г.

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.

автор: Mark M

20 нояб. 2017 г.

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.

автор: Prateek G

15 апр. 2017 г.

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!

автор: Miguel D

3 апр. 2017 г.

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.

автор: Srinivas S

24 окт. 2018 г.

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.

автор: Benjamin L T L

3 апр. 2020 г.

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

автор: Changli H

17 нояб. 2017 г.

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