Map-Reduce is on its way out. But we shouldn’t measure its importance in the number of bytes it crunches, but the fundamental shift in data processing architectures it helped popularise.
Almost everyone has heard of Google's MapReduce framework, but very few have ever hacked around with the idea of map and reduce. These two idioms are borrowed from functional programming, and form the basis of Google's framework. Although Python is not a functional programming language, it has built-in support for both of these concepts. A…
Peregrine is a map reduce framework designed for running iterative jobs across partitions of data. Peregrine is designed to be FAST for executing map reduce jobs by supporting a number of optimizations and features not present in other map reduce frameworks.
MRQL (the Map-Reduce Query Language) is an SQL-like query language for map-reduce computations. It is implemented on top of Apache's Hadoop. MRQL is powerful enough to express most common data analysis tasks over many different kinds of raw data, including hierarchical data and nested collections, such as XML data. It is more powerful than other current languages, such as Hive and Pig Latin, since it can operate on more complex data and supports more powerful query constructs, thus eliminating the need for using explicit map-reduce code.
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