Data processing device and method based on dimension modeling

Abstract

The invention provides a data processing device and method based on dimension modeling, the device comprises a data source layer, a data integration layer, a data component layer and a data application layer, the input end of the data source layer is connected with a data source, and the output end of the data source layer is sequentially connected with the input ends of the data integration layer, the data component layer and the data application layer; a unified theme division mode is not required, theme division is performed on a data integration layer and a data component layer according to, the characteristics of each layer in the data warehouse, different theme division modes adopted by each layer, and a business process and 'entity + behavior ', so that the resource cost is greatly reduced by using the data warehouse model architecture disclosed by the invention; the data timeliness is improved, and the service usability is enhanced.

Description

technical field [0001] The invention belongs to the field of data warehouses, and in particular relates to a data processing device and method based on dimension modeling. Background technique [0002] In the era of big data, for any Internet food delivery company, it is necessary to have the ability to quickly acquire, count, and analyze data, so as to analyze users from multiple angles and mine user information. Therefore, the establishment of a data warehouse is an inevitable trend. Efficient and reasonable data warehouse model architecture, and the establishment of data warehouses with the help of related tools are the most concerned issues for Internet food delivery companies. [0003] In the prior art, the data warehouse model architecture usually includes five layers: data source layer (ODS), data detail layer (DWD), data service layer (DWS), application service layer (ADS), and dimension table layer (DIM). The data source layer usually stores the source data intact,...

AI Technical Summary

Problems solved by technology

[0004]1. The layering mechanism is not clear, and the impact of several key factors such as data stability, individuality, commonality, and timeliness cannot be fully considered, and it is difficult to guide from the overall perspective Each business or each link of the production link, and the data is counted repeatedly, increasing resource costs

[0005]2. The topic division is unreasonable. Themes are only divided according to the business process, which cannot meet the diverse online analytical processing (OLAP) requirements of the data warehouse

[0006]3. The data service layer and the application service layer have serious data expansion, greatly increasing labor costs, and resulting in tight computing resources and low data timeliness

It will directly affect the output of subsequent core reports, recommendations, and models, reduce the efficiency of business data analysis, and make it difficult to maximize the value of data

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