MapReduce was invented by Google in 2004, made into the Hadoop open source project by Yahoo! in 2007, and now is being used increasingly as a massively parallel data processing engine for Big Data.

The MapReduce paradigm consists of two main phases: map and reduce. In the map phase, each input data element is processed by a user-defined function that emits one or more key-value pairs. In the ...

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 ...

Implemented Map Reduce algorithms to: compute the word count, produce modified tri-grams around keywords, generate inverted indices for the given dataset and perform relational join on two datasets to ...

Big data can feel abstract until we see how the work actually moves. When we say that large-scale systems process massive datasets, it is easy to imagine one powerful computer doing everything. But ...

When the Big Data moniker is applied to a discussion, it’s often assumed that Hadoop is, or should be, involved. But perhaps that’s just doctrinaire. Hadoop, at its core, consists of HDFS (the Hadoop ...

Google and its MapReduce framework may rule the roost when it comes to massive-scale data processing, but there’s still plenty of that goodness to go around. This article gets you started with Hadoop, ...

Finding frequent itemsets is one of the most important fields of data mining. Apriori algorithm is the most established algorithm for finding frequent itemsets from a transactional dataset; however, ...

Distributed K-means clustering using MapReduce paradigm Configurable number of mappers and reducers (1-5 each) Fault tolerance with failure simulation and recovery gRPC-based communication between ...

In my last post, I explained MapReduce in terms of a hypothetical exercise: counting up all the smartphones in the Empire State Building. My idea was to have the fire wardens count up the number of ...