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Large-scale data processing frameworks, such as Hadoop MapReduce, are widely used to analyze enormous amounts of data. However, processing is often time-consuming, preventing interactive analysis. One way to decrease response time is partial job execution, where an approximate, early result becomes available to the user, prior to job completion. The Hadoop Online Prototype (HOP) uses online aggregation...
High-level parallel dataflow systems, such as Pig and Hive, have lately gained great popularity in the area of big data processing. These systems often consist of a declarative query language and a set of compilers, which transform queries into execution plans and submit them to a distributed engine for execution. Apart from the useful abstraction and support for common analysis operations, high-level...
MapReduce has recently gained great popularity as a programming model for processing and analyzing massive data sets and is extensively used by academia and industry. Several implementations of the MapReduce model have emerged, the Apache Hadoop framework being the most widely adopted. Hadoop offers various utilities, such as a distributed file system, job scheduling and resource management capabilities...
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