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.
Acerca de este Curso
Habilidades que obtendrás
- 5 stars73,02 %
- 4 stars21,15 %
- 3 stars4,37 %
- 2 stars0,66 %
- 1 star0,78 %
Principales reseñas sobre BIG DATA ANALYSIS WITH SCALA AND SPARK
the theory is very clear and well explained.
the practical assignments are a little bit ambiguous but they are overall very good and challenging.
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.
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.
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.
Acerca de Programa especializado: Functional Programming in Scala
Discover how to write elegant code that works the first time it is run.
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