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High Performance Storage for Big Data Analytics and Visualization

High Performance Storage for Big Data Analytics and Visualization

Armando Fandango, William Rivera
Copyright: © 2018 |Pages: 22
ISBN13: 9781522531425|ISBN10: 1522531424|EISBN13: 9781522531432
DOI: 10.4018/978-1-5225-3142-5.ch010
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MLA

Fandango, Armando, and William Rivera. "High Performance Storage for Big Data Analytics and Visualization." Handbook of Research on Big Data Storage and Visualization Techniques, edited by Richard S. Segall and Jeffrey S. Cook, IGI Global, 2018, pp. 254-275. https://doi.org/10.4018/978-1-5225-3142-5.ch010

APA

Fandango, A. & Rivera, W. (2018). High Performance Storage for Big Data Analytics and Visualization. In R. Segall & J. Cook (Eds.), Handbook of Research on Big Data Storage and Visualization Techniques (pp. 254-275). IGI Global. https://doi.org/10.4018/978-1-5225-3142-5.ch010

Chicago

Fandango, Armando, and William Rivera. "High Performance Storage for Big Data Analytics and Visualization." In Handbook of Research on Big Data Storage and Visualization Techniques, edited by Richard S. Segall and Jeffrey S. Cook, 254-275. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-3142-5.ch010

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Abstract

Scientific Big Data being gathered at exascale needs to be stored, retrieved and manipulated. The storage stack for scientific Big Data includes a file system at the system level for physical organization of the data, and a file format and input/output (I/O) system at the application level for logical organization of the data; both of them of high-performance variety for exascale. The high-performance file system is designed with concurrent access, high-speed transmission and fault tolerance characteristics. High-performance file formats and I/O are designed to allow parallel and distributed applications with easy and fast access to Big Data. These specialized file formats make it easier to store and access Big Data for scientific visualization and predictive analytics. This chapter provides a brief review of the characteristics of high-performance file systems such as Lustre and GPFS, and high-performance file formats such as HDF5, NetCDF, MPI-IO, and HDFS.

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