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OceanStor 9000 V300R006C00 File System Feature Guide 12

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Huawei uses machine translation combined with human proofreading to translate this document to different languages in order to help you better understand the content of this document. Note: Even the most advanced machine translation cannot match the quality of professional translators. Huawei shall not bear any responsibility for translation accuracy and it is recommended that you refer to the English document (a link for which has been provided).
Application Scenarios

Application Scenarios

Using the HDFS feature to incorporate itself into the Hadoop ecosphere, the OceanStor 9000 is applicable to big data analysis scenarios in finance and operator industries.

Big Data Analysis in the Finance Industry

Figure 14-7 shows the typical application of the OceanStor 9000 in the big data analysis scenario in the finance industry.

Figure 14-7  Big data analysis in the finance industry

Facing the fierce competition in the Internet finance industry, financial enterprises are in urgent need of reconstructing a decision-making and service system based on big data mining to improve their competitiveness and customer satisfaction. In the era of big data, banks are transforming from transaction-centric to data-centric to cope with the challenges posed by a large number of multidimensional real-time data and Internet services.

The big data platform consisting of the Hadoop cluster and the OceanStor 9000 provides the following services to help financial industries improve competitiveness:

  • Real-time query of historical transactions

    User transactions made in the past seven years or longer can be queried in real time.

  • Real-time credit investigation service

    Credit card investigation time is reduced from three days to less than 10 minutes.

  • Microcredit service forecast

    The forecast accuracy rate of top 1000 promising microcredit users is 40 times higher than before.

  • Precision marketing

    • The period of collecting the distributed eBank logs is shortened. User preferences are analyzed based on the eBank logs to achieve precision marketing and improve the eBank users' experience.

    • Only less than 20% of the original recommended short messages can cover all effective users.

Big Data Analysis in the Operator Industry

Figure 14-8 shows the typical application of the OceanStor 9000 in the big data analysis scenario in the finance industry.

Figure 14-8  Big data analysis in the operator industry

The era of big data presents the following challenges for operators:

  • Amount and types of data to be processed grow at an explosive speed. However, the unstructured data can be analyzed by the existing architecture only at a slow speed.

  • The existing application systems are constructed in a siloed form, leading to repetitive data storage, difficult data sharing among different systems, and slow service decision-making and analysis.

The big data platform consisting of the Hadoop cluster and the OceanStor 9000 provides the following solutions to help operators improve competitiveness:

  • Construct a unified big data charging data record (CDR) platform and operation data analysis CDR platform.

    • The CDRs generated in the past 6 to 24 months (before it was 3 months) can be queried in real time.

    • The time for concurrently analyzing operation CDRs is shortened from five days to one day.

  • Construct a unified PB-level big data platform to store service data in a unified manner. The distributed computing capacity of the big data platform is used to concurrently process diverse analysis tasks and obtain service decision-making results quickly.

    • The new service release period is shortened from one month and a half to one week.

    • Existing users are retained. The off-net ratio of the VIP users is greatly reduced.

  • Allow data share and interface openness under the condition that data security and privacy are ensured. In this way, big data can provide share services externally to boost service innovation and business success.

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Updated: 2019-06-27

Document ID: EDOC1000122519

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