Annotation
Data volumes are ever growing, for a large application spectrum going from traditional database applications, scientific simulations to emerging applications including Web 2.0 and online social networks. To cope with this added weight of Big Data, we have recently witnessed a paradigm shift in the way data is processed through the MapReduce model. First promoted by Google, MapReduce has become, due to the popularity of its open-source implementation Hadoop, the de facto programming paradigm for Big Data processing in large-scale data-centers and clouds.
The goal of this tutorial is to serve as a first step towards exploring the Hadoop platform and also to provide a short introduction into working with big data in Hadoop. An overview on Big Data including definitions, the source of Big Data, and the main challenges introduced by Big Data, will be presented. We will then present the MapReduce programming model as an important programming model for Big Data processing in the Cloud. Hadoop ecosystem and some of major Hadoop features will then be discussed. Finally, we will discuss several approaches and methods used to optimise the performance of Hadoop in the Cloud.
The goal of this tutorial is to serve as a first step towards exploring the Hadoop platform and also to provide a short introduction into working with big data in Hadoop. An overview on Big Data including definitions, the source of Big Data, and the main challenges introduced by Big Data, will be presented. We will then present the MapReduce programming model as an important programming model for Big Data processing in the Cloud. Hadoop ecosystem and some of major Hadoop features will then be discussed. Finally, we will discuss several approaches and methods used to optimise the performance of Hadoop in the Cloud.
Several hand-ons will be provided to study the operation of Hadoop platform along with the implementation of MapReduce applications.
This course is a substitute for the Hadoop session, which could not be held during the PRACE Winter School 2015.
Level
basic/intermediate
Language
English
About the tutor(s)
Dr. Shadi Ibrahim is a permanent Inria research scientist within the KerData research team. He obtained his Ph.D. in Computer Science from Huazhong University of Science and Technology in Wuhan of China in 2011. His research interests are in cloud computing, big data management, data-intensive computing, high performance computing, virtualization technology, and file and storage systems. He has published several research papers in recognized big data and cloud computing research journals and conferences, among which, several papers on optimizing and improving Hadoop MapReduce performance in the cloud and one book chapter on MapReduce framework.
