Google introduced the MapReduce algorithm to perform massively parallel processing of very large data sets using clusters of commodity hardware. MapReduce is a core Google technology and key to ...

It is an open implementation of the MapReduce algorithm and includes HDFS (Hadoop Distributed File System) for high throughput access to distributed data. What has been less visible for some time is ...

After a brief review of how map-reduce works, we shall look at the trade-off that needs to be made when designing map-reduce algorithms for problems that are not embarrassingly parallel. In particular ...

Some algorithms translate poorly to Map-Reduce—the partitioning of data and computation to individual nodes makes some computations (graph processing for instance) difficult. And, the implementation ...

"We knew that we were going to have to take Hadoop beyond MapReduce," Murthy says. "The programming model—the MapReduce algorithm—was limited. It can't support the very wide variety of use-cases we're ...

An Efficient Implementation of Apriori Algorithm Based on Hadoop-Mapreduce Model Finding frequent itemsets is one of the most important fields of data mining.

Cascading is a new processing API for data processing on Hadoop clusters, and supports building complex processing workflows using an expressive, declarative API.

We just follow the MapReduce pattern and Hadoop does the rest. MapReduce with Hadoop Hadoop is mostly a Java framework, but the magically awesome Streaming utility allows us to use programs written in ...

The MapReduce design pattern to distribute data processing was introduced by Google in 2004, and came first with a C++ implementation. A new Ruby implementation is now available under the name of ...