Dean Wampler supports using Functional Programming and its core operations to process large amounts of data, explaining why Java’s dominance in Hadoop is harming Big Data’s progress.
Dean Wampler is a contributer to several open-source projects and the founder of the Chicago-Area Scala Enthusiasts. He is the author of Functional Programming for Java Developers, the co-author of [Programming Scala](http://programmingscala.com/), and the co-author of Programming Hive, all from O'Reilly. He pontificates on twitter,@deanwampler, and at polyglotprogramming.com.
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I should have mentioned LINQ
Re: I should have mentioned LINQ
LINQ came to mind a lot for me during the talk, most prominently when you mentioned joins as it handles that quite well. There is no reason why a LINQ facade cannot be put on something like Hadoop.
An idea I find interesting is Nested Data-Parallelism (developed by Guy Blelloch) which can be [grossly] abstracted as keeping separate views over your data: one view supports the solution structure (allows programmer to divide and conquer; reason about the problem/solution) while another view (the physical/memory layout) is laid out for efficiency. While a lot of the current work seems [to my knowledge] to focus on multicore and GPGPU, I see no reason why it cannot expand to distributed systems for computations where the commutative property holds (for a given nesting level).