Exploration of big data platform for operators based on DataOps

doi:10.12267/j.issn.2096-5931.2022.2.009

Information and Communications Technology and Policy ›› 2022, Vol. 48 ›› Issue (2): 55-60.doi: 10.12267/j.issn.2096-5931.2022.2.009

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Exploration of big data platform for operators based on DataOps

XIAO Wenbin¹, LI Yufei², ZHANG Zhanmei¹

1. China Mobile Communications Group Guangdong Co., Ltd., Guangzhou 510000, China
2. Cloud Computing & Big Data Research Institute, China Academy of Information and Communications Technology, Beijing 100191, China;

Received:2021-12-10 Online:2022-02-15 Published:2022-03-14

Abstract

Abstract:

In the era of digital economy, data becomes the core asset of operators.DataOps is an effective mode to release data value, and big data platform is the technical basis to realize DataOps. This paper sorted out the development of DataOps and big data platform, elaborated the connotation of DataOps, proposed the method of applying DataOps to the big data platform of operators, and constructed the framework of big data platform of operators based on DataOps.

Key words: DataOps, big data platform, agile data delivery, intelligent data governance, data asset operation

CLC Number:

TP309.2

XIAO Wenbin, LI Yufei, ZHANG Zhanmei. Exploration of big data platform for operators based on DataOps[J]. Information and Communications Technology and Policy, 2022, 48(2): 55-60.

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http://ictp.caict.ac.cn/EN/10.12267/j.issn.2096-5931.2022.2.009

http://ictp.caict.ac.cn/EN/Y2022/V48/I2/55

[1]	LENNY L. Three reasons why dataOps is essential for big data success[R], 2014
[2]	Gartner. Introducing DataOps into your data management[R], 2021.
[3]	中国信息通信研究院. 大数据白皮书[R], 2021.
[4]	工业和信息化部. “十四五”信息通信行业发展规划[R], 2021.
[5]	中国信息通信研究院. 数据资产管理白皮书(5.0版)[R], 2021.
[6]	IBM. Deliver business-ready data fast with DataOps[R], 2019.
[7]	GENE K, JEZ H, PATRICK D, et al. DevOps实践指南[Z]. 刘征译, 2021.

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Exploration of big data platform for operators based on DataOps

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