Sharing project experience in data processing and data warehouse through MySQL development

In today’s digital era, data has been generally considered to be the basis and capital for corporate decision-making. However, the process of processing large amounts of data and transforming it into reliable decision support information is not easy. At this time, data processing and data warehousing begin to play an important role. This article will share a project experience of implementing data processing and data warehouse through MySQL development.

1. Project Background

This project is based on the needs of data construction of a commercial enterprise and aims to achieve data aggregation, consistency, cleaning and reliability through data processing and data warehouse. The database management system implemented this time is MySQL version 5.7. The goal of this project is to collect, process, integrate, standardize and store data from different systems to provide data analysis and decision-making support for enterprises.

2. Project Practice

1. Scheme Design

First carry out scheme design, clarify project requirements, determine data sources, data quality, data cleaning, data standardization, data construction Key requirements such as molds. And comprehensively consider the implementation technology stack, cost and other dimensions to formulate technical plans and implementation plans.

Data processing uses MySQL stored procedures and custom functions to clean and standardize the original data; import the processed data into the data warehouse through data modeling and ETL tools.

2. Data source collection

First collect source data in the system according to preset rules. These data include transaction records of each system, customer behavior records, etc.

3. Data cleaning

Clean the data source, including filling in missing data values, processing abnormal data, etc. Perform preliminary cleaning of source data through MySQL stored procedures and custom functions to improve data quality.

4. Data standardization

Through the standardized data table structure, data from different sources are merged into a common standardized data format, which facilitates later analysis and management.

5. Modeling and import

Establish a data warehouse, design it based on the Star Schema model, and use ETL tools to extract, transform, and load source data into the data warehouse. At the same time, drill down and analyze the data required according to the designed role dimensions.

6. Data analysis and decision support based on data warehouse

This project achieves orderly management and multi-dimensional analysis of data by designing a data warehouse. Through drill-down analysis, we can gain insights into the patterns behind the data and provide decision support information to help business managers make timely decisions.

3. Summary

This project implements data processing and data warehouse through MySQL development, integrating original, non-standard, incomplete and inconsistent data into a standard, scalable, The easy-to-query and highly optimized data warehouse provides enterprises with decision support and data analysis. The completion of this project not only improved the company's data management level, but also provided strong support for the company's future decision-making.

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