About this course: 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. Learning Outcomes. By the end of this course you will be able to: - read data from persistent storage and load it into Apache Spark, - manipulate data with Spark and Scala, - express algorithms for data analysis in a functional style, - recognize how to avoid shuffles and recomputation in Spark, Recommended background: You should have at least one year programming experience. Proficiency with Java or C# is ideal, but experience with other languages such as C/C++, Python, Javascript or Ruby is also sufficient. You should have some familiarity using the command line. This course is intended to be taken after Parallel Programming: https://www.coursera.org/learn/parprog1.
Taught by: Dr. Heather Miller, Research Scientist
EPFL
| Basic Info |
Course 4 of 5 in the
Functional Programming in Scala Specialization.
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| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
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This course gives basics of distributed computing by practical examples, several alternative approaches to the same problem are considered, this gives more insight and flexibility. Very interesting one!
I loved this course
Content was useful, overall a good course. Lectures were kind of dull.
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SL
I thought this was a decent course. I enjoyed the exercises and thought it gave a good introduction to Spark. Some of the lectures in Week4 were a bit long and the material needed to complete the Week4 exercise wasn't in the lectures. It would have been nice to have a 'conclusion' lecture wrapping everything up instead of just ending the course on a DataSet's lecture.