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學生對 洛桑联邦理工学院 提供的 Big Data Analysis with Scala and Spark 的評價和反饋

2,569 個評分


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:




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!



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.


201 - Big Data Analysis with Scala and Spark 的 225 個評論(共 508 個)

創建者 rohith a k


It is indeed a very good course, but 2nd assignment was tough though.

創建者 Andrew P M


Excellent course with great content. Really helped me in my day job!

創建者 Kyle A


Very real examples for the exercises and a great way to learn Spark!

創建者 Khaled J A


this is an outstanding course to learn BIg data with scala and spark

創建者 Carlos S


Very good !! ... Thanks Heather Miller for such cool class !! (Y) <3

創建者 Martin P


From easy to hard, not very long, but very practical!

Thanks Heather!

創建者 Martin M


A very nicely paced introduction to spark and all of its major APIs

創建者 Vishwanath G


Learnt a lot with this course. Excellent explinations of concepts..

創建者 Milorad T


The best in the specialization. Teacher is such easy to listen to.

創建者 Johan R


Very good introduction which gets you to be productive on your own

創建者 Eike H


Great course, helped me quickly gain important insights to Spark.

創建者 Gao Y


Nice course. Big jump at the end. The assignment is well hinted.

創建者 Yogen R


Excellent course materials and concepts explanation by Heather!

創建者 Navjinder V


Great Course content with amazing and challenging assignments!

創建者 lu


A good introduction to Scala programming in Spark environment.

創建者 Gary Z


Best Spark Scala course! Clear explanation! Thanks Dr. Heather

創建者 Janis S


The best Apache Spark course I have seen so far. Many thanks.

創建者 Konrad C


A lot things to learn and experiment.

Challenging assignments.

創建者 Natalija I


Great course, I really enjoyed learning! Thank you Dr. Miller

創建者 Deleted A


It's a great course for those who want to learn Apache Spark.

創建者 Parker G


Great course! The powerpoint/slides/pdfs are a GREAT resource

創建者 Carsten I


Excellent class, very well stuctured, clear and informative.

創建者 Murat A


It was very challenging, but also informative. Thanks a lot.

創建者 Manoj K


This course make me to fall in love with SPARK framework :)

創建者 Sivakumar P


Course is very useful to understand Spark and Scala things.