Intelligent enterprise data construction method

Abstract

The invention relates to an intelligent enterprise data construction method, which is characterized in that through mutual cooperation of a data integration module, a data processing module, a data synchronization module and a data asset center, operation is carried out in a calculation and data architecture level on the basis of storage integration, and unified management of various data sources is carried out through a one-key access mode. A database or schema which needs to be accessed is specified, unstructured and semi-structured data are uploaded, a uniform access interface channel is opened for a downstream data processing flow, FTP data source acquisition is performed, data expansion and maintenance are performed, and on the basis of storage integration, calculation engine switching of different scenes is supported ecologically through a hadoop ecological technology, so that the calculation efficiency is improved. And carrying out real-time query and offline query. Therefore, a mainstream heterogeneous data source can be connected, a unified data acquisition mode and synchronous management are implemented, smooth transition between off-line data and real-time data is realized, and data reliability and abnormal complement are ensured.

Description

technical field [0001] The invention relates to a data processing construction method, in particular to an enterprise data intelligent construction method. Background technique [0002] As far as existing enterprise data management is concerned, at least 75% of enterprises cannot make deep use of the data they have. The main reason is that the data of enterprises is fragmented and unsystematic. [0003] Judging from the current more common enterprise data construction, there are the following defects: [0004] 1. Data standards are controversial. The chimney-style development and local business service support have led to frequent problems of indicators with the same name and different calibers; historically, different business systems have been gradually launched iteratively, and problems such as inconsistent attribute encoding of the same object are prominent. [0005] 2. The data quality is uneven. Repeated construction results in a long task chain, numerous tasks, ti...

AI Technical Summary

Problems solved by technology

Chimney-style development and local business service support lead to frequent problems with indicators of the same name and different calibers; historically, different business systems are gradually launched iteratively, and problems such as inconsistent attribute coding of the same object are prominent

[0005] 2. Data quality varies

Duplicated construction leads to lengthy task chains, numerous tasks, tight computing resources, and poor data timeliness; document accumulation defined by caliber is out of touch with development code implementation, and the risk of data accuracy assurance is high

[0006] 3. Demand response is relatively slow

Chimney-style development has a long development cycle, low efficiency, and insufficient application-oriented services, resulting in slow business response. While the business is not satisfied, the technology feels that there is no precipitation and growth; there are insufficient talents who understand both business and data, and the needs are understood. Development and implementation involve a lot of communication, and the service efficiency is poor

[0007] 4. Cost resources are difficult to control

Repeated construction wastes technical resources; it is even more difficult to go online and offline, and source system or business changes cannot be reflected in the data in a timely manner. In addition, the data is not standard, making R&D and maintenance even more difficult. At the same time, a large number of useless calculations and storage cause waste of resources

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