This tutorial is motivated by the clear need of many organizations, companies, and researchers to deal with big data volumes efficiently. Examples include web analytics applications, scientific applications, and social networks. A popular data processing engine for big data is Hadoop MapReduce. Early versions of Hadoop MapReduce suffered from severe performance problems. Today, this is becoming...

Data analysis has become a challenge in recent years as the volume of data generated difficult to manage, therefore more hardware and software resources are needed store process this huge amount data. Apache Hadoop is free framework, widely used thanks Distributed Files System (HDFS) its ability relate other processing components such MapReduce for data, Spark - in-memory Processing, Drill SQL ...

Support of high performance queries on large volumes of spatial data becomes increasingly important in many application domains, including geospatial problems in numerous fields, location based services, and emerging scientific applications that are increasingly data- and compute-intensive. The emergence of massive scale spatial data is due to the proliferation of cost effective and ubiquitous ...

MapReduce is an enabling technology in support of Cloud Computing. Hadoop which is a mapReduce implementation has been widely used in developing MapReduce applications. This paper presents Hadoop simulatorHaSim, MapReduce simulator which builds on top of Hadoop. HaSim models large number of parameters that can affect the behaviors of MapReduce nodes, and thus it can be used to tune the performa...

This short white paper describes our efforts to establish techniques and tools to identify optimization opportunities for Hadoop workloads. Suitable performance metrics and relevant benchmark use cases are a crucial component to achieve these goals. We discuss efforts to define suitable metrics for cloud computing in general, briefly describe hardware and software components that impact Hadoop ...

HPC environments have traditionally been designed to meet the compute demand of scientific applications and data has only been a second order concern. With science moving toward data-driven discoveries relying more and more on correlations in data to form scientific hypotheses, the limitations of existing HPC approaches become apparent: Architectural paradigms such as the separation of storage ...