The amount of data in our industry and the world is exploding. Data is being collected and stored at unprecedented rates. The challenge is not only to store and manage the vast volume of data (“big data”), but also to analyze and extract meaningful value from it. There are several approaches to collecting, storing, processing, and analyzing big data. The main focus of the paper is on unstructured data analysis. Unstructured data refers to information that either does not have a pre-defined data model or does not fit well into relational tables. Unstructured data is the fastest growing type of data, some example could be imagery, sensors, telemetry, video, documents, log files, and email data files. There are several techniques to address this problem space of unstructured analytics. The techniques share a common character tics of scale-out, elasticity and high availability. MapReduce, in conjunction with the Hadoop Distributed File System (HDFS) and HBase database, as part of the Apache Hadoop project is a modern approach to analyze unstructured data. Hadoop clusters are an effective means of processing massive volumes of data, and can be improved with the right architectural approach.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Considerations for big data: Architecture and approach


    Contributors:


    Publication date :

    2012-03-01


    Size :

    965011 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    UAS DAA architecture considerations

    Eckstein, Bruce | IEEE | 2015

    Free access

    Open Systems Architecture Considerations

    Filmer, B. | Online Contents | 2003



    Lunar Navigation Architecture Design Considerations

    D'Souza, Christopher / Getchius, Joel / Holt, Greg et al. | NTRS | 2009