The past few years have seen a major change in computing systems, as growing data
volumes and stalling processor speeds require more and more applications to scale out to …

MapReduce and its variants have been highly successful in implementing large-scale data-
intensive applications on commodity clusters. However, most of these systems are built …

MapReduce and Spark are two very popular open source cluster computing frameworks for
large scale data analytics. These frameworks hide the complexity of task parallelism and …

Apache Spark is an open-source cluster computing framework for big data processing. It has
emerged as the next generation big data processing engine, overtaking Hadoop …

Companies providing cloud-scale data services have increasing needs to store and analyze
massive data sets, such as search logs, click streams, and web graph data. For cost and …

MapReduce is a programming model and an associated implementation for processing and
generating large datasets that is amenable to a broad variety of real-world tasks. Users …

We describe the design and implementation of Disco, a distributed computing platform for
MapReduce style computations on large-scale data. Disco is designed for operation in …

MapReduce is a framework for processing and managing large-scale datasets in a
distributed cluster, which has been used for applications such as generating search indexes …

MapReduce has emerged as a popular method to process big data. In the past few years,
however, not just big data, but fast data has also exploded in volume and availability …

MapReduce has emerged as a promising architecture for large scale data analytics on
commodity clusters. The rapid adoption of Hive, a SQL-like data processing language on …