About MapReduce MapReduce is a programming model specifically implemented for processing large data sets. The model was developed by Jeffrey Dean and Sanjay Ghemawat at Google (see “ MapReduce ...

Lack of multiple data source support – Current implementations of the Hadoop MapReduce programming model only support a single distributed file system; the most common being HDFS.

To many, Big Data goes hand-in-hand with Hadoop + MapReduce. But MPP (Massively Parallel Processing) and data warehouse appliances are Big Data technologies too. The MapReduce and MPP worlds have ...

The core components of Apache Hadoop are the Hadoop Distributed File System (HDFS) and the MapReduce programming model.

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

Companies who have experimented with Hadoop and have had early success but are weary of the bottleneck that MapReduce programming presents to exploit data.

Platform Computing, a provider of cluster, grid and cloud management software, has announced support for the Apache Hadoop MapReduce programming model to bring enterprise-class distributed computing ...

But there are downsides. The MapReduce programming model that accesses and analyses data in HDFS can be difficult to learn and is designed for batch processing.

This is a comprehensive Apache Hadoop and Spark comparison, covering their differences, features, benefits, and use cases.

Lack of multiple data source support – Current implementations of the Hadoop MapReduce programming model only support a single distributed file system; the most common being HDFS.