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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Skills you will gain
- 5 stars73.07%
- 4 stars21.11%
- 3 stars4.39%
- 2 stars0.66%
- 1 star0.74%
TOP REVIEWS FROM BIG DATA ANALYSIS WITH SCALA AND SPARK
It was really useful material. It would be really nice if there are more assignments to polish the materials we learn, but I am really satisfied with the course.
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.
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.
Dear Heather, your course on big data with scala is the very first online course I participate in.\n\nI enjoy the way you explain the material and receive a real aesthetic pleasure.
About the Functional Programming in Scala Specialization
Discover how to write elegant code that works the first time it is run.
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