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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- 5 stars73,02 %
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- 3 stars4,36 %
- 2 stars0,66 %
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Meilleurs avis pour BIG DATA ANALYSIS WITH SCALA AND SPARK
Great course with nice explanations of some Spark concepts. The third week was particularly useful for my understanding of Spark shuffling and partitioning. Thanks a lot!
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.
your course on big data with scala is the very first online course I participate in.
I enjoy the way you explain the material and receive a real aesthetic pleasure.
Awesome course and awesome teacher! Nevertheless, to grasp the most of this course, you should do the previous 3 courses of the "Functional Programming in Scala" specialization.
À propos du Spécialisation Functional Programming in Scala
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