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.

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 ...

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

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 ...

Hadoop is the most significant concrete technology behind the so called 'Big Data' revolution. Hadoop combines an economical model for storing massive quantities of data - the Hadoop Distributed File ...

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 ...

Google today pledged that it will not sue any users, distributors or developers who have implemented open-source versions of its MapReduce programming model for processing large data sets, even ...

Technical Terms MapReduce: A programming model that simplifies distributed data processing by dividing tasks into map and reduce functions operating in a parallel, fault-tolerant manner.

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.