Topic model-based bank data warehouse construction method and device

Abstract

The invention provides a topic model-based bank data warehouse construction method and apparatus. The topic model-based bank data warehouse construction method comprises the steps of classifying original data into a corresponding topic model data table according to a preset topic model data table; loading a plurality of topic model data tables into a heterogeneous data temporary table according toa pre-generated priority rule; and inserting the heterogeneous data temporary table into a heterogeneous data base layer target table to generate a target data warehouse. The method provided by the invention has the advantages that compared data sets are few, a checking mode is irrelevant to a loading algorithm and a table primary key, and the accuracy of a checking result is high.

Description

technical field [0001] The invention relates to the field of big data technology, in particular to the construction and verification technology of a big data warehouse in the financial industry, and in particular to a method and device for building a bank data warehouse based on a subject model. Background technique [0002] In the existing technology, commercial bank enterprise-level data warehouses are generally built based on TERADATA all-in-one machines. In the past ten years, data warehouses have played an important role in promoting the business development of commercial banks. Although the all-in-one machine is a software-hardware database product designed for the analysis and processing of large amounts of data, it consists of a set of integrated servers, storage devices, operating systems, database management systems, and some pre-installed and optimized databases for data warehouse purposes. It has relatively mature solutions for data management and processing. How...

AI Technical Summary

Problems solved by technology

Although the all-in-one machine is a software-hardware database product designed for the analysis and processing of large amounts of data, it consists of a set of integrated servers, storage devices, operating systems, database management systems, and some pre-installed and optimized databases for data warehouse purposes. It has relatively mature solutions for data management and processing. However, with the continuous expansion of data capacity and business expansion of commercial bank enterprise-level data warehouses, the disadvantages of all-in-one machine expansion and high maintenance costs are obvious. Therefore, , there is an urgent need for a method suitable for commercial banks to carry out IT architecture transformation of enterprise-level data warehouses

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