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.
About this Course
Skills you will gain
- 5 stars73.02%
- 4 stars21.13%
- 3 stars4.36%
- 2 stars0.66%
- 1 star0.81%
TOP REVIEWS FROM BIG DATA ANALYSIS WITH SCALA AND SPARK
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!
the theory is very clear and well explained.
the practical assignments are a little bit ambiguous but they are overall very good and challenging.
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.
goot as introduction about spark and big data.
Small notice: it is incorrect to compare performance hadoop and spark. As I understand, spark was expected to be compacred with MapReduce.
About the Functional Programming in Scala Specialization
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