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.
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Meilleurs avis pour BIG DATA ANALYSIS WITH SCALA AND SPARK
Good overview of the subject, covering all important aspects. Assignments were well prepared, with a couple of unclear points that were quickly discovered and explained on the forums.
Great introduction to Spark and it's data structures. The course is easy to follow, and lecturer is entertaining and really engaged.
Thanks, I really had fun !
Excellent material. Very good flow. Heather has an amazing way of walking through the flow and simplifying the concepts. Great assignments -- takes a bit longer than 3 hours.
Great course to get going with Apache Spark. Would recommend to someone who has java or scala experience already and wants to learn about distributed processing.
À propos du Spécialisation Functional Programming in Scala
Discover how to write elegant code that works the first time it is run.
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