Proceedings of the 2017 2nd International Conference on Automation, Mechanical and Electrical Engineering (AMEE 2017)

The Hadoop Technology Applies in Power Big Data Platform

Authors
Jianyong Hu, Jilin Chen, Mei Xie, Bo Gao, Zhihong Yu, Jianfeng Yan, Ying Lv
Corresponding Author
Jianyong Hu
Available Online September 2017.
DOI
10.2991/amee-17.2017.24How to use a DOI?
Keywords
Power Big Data; Hadoop; distributed storage; parallel computing
Abstract

The utility industry has entered into Big Data era as the construction and development of smart grid. It becomes more critical to store and process the Big Data efficiently, and to make effective utilization when the power Big Data are massive complicate. This paper analyses the status of power Big Data. Then, it introduces the main architecture of power Big Data platform based on Hadoop technology. Several key technologies are analyzed including data storing and processing. Finally, discusses the application of power Big Data platform based on Hadoop technology

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2017 2nd International Conference on Automation, Mechanical and Electrical Engineering (AMEE 2017)
Series
Advances in Engineering Research
Publication Date
September 2017
ISBN
978-94-6252-393-7
ISSN
2352-5401
DOI
10.2991/amee-17.2017.24How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Jianyong Hu
AU  - Jilin Chen
AU  - Mei Xie
AU  - Bo Gao
AU  - Zhihong Yu
AU  - Jianfeng Yan
AU  - Ying Lv
PY  - 2017/09
DA  - 2017/09
TI  - The Hadoop Technology Applies in Power Big Data Platform
BT  - Proceedings of the 2017 2nd International Conference on Automation, Mechanical and Electrical Engineering (AMEE 2017)
PB  - Atlantis Press
SP  - 113
EP  - 116
SN  - 2352-5401
UR  - https://doi.org/10.2991/amee-17.2017.24
DO  - 10.2991/amee-17.2017.24
ID  - Hu2017/09
ER  -