How to design a database to meet the business needs of nearly 400 fields?

Database design strategy to deal with massive fields

Many business scenarios require the integration of data from multiple external platforms, which often leads to a surge in the number of database table fields, bringing huge maintenance and development challenges. This article uses a case where three platform data needs to be integrated (each platform has more than 100 fields, totaling nearly 400 fields), to explore how to optimize database design and solve the maintenance problems of traditional vertical table sub-scheduling solutions. After filtering, the actual number of fields used is about 300.

Optimization strategy:

First, data preprocessing is crucial . It is recommended to conduct preliminary processing during the data collection stage and create an independent source data table for the interface data of each platform. This retains the original data form, which facilitates subsequent traceability and debugging. Field filtering can be performed after data collection, leaving only the necessary fields behind.

Secondly, reasonable data integration . If you need to integrate data from different platforms for display, you need to design an integrated data table. This requires careful evaluation of the necessity of each field and avoids redundancy. It is recommended to use data modeling methods such as ER graphs to group these 300 fields according to business relevance and split them into multiple tables to avoid too many single table fields.

Third, the applicability of NoSQL database . NoSQL databases such as MongoDB store data in the form of documents, which can better adapt to changes in the number of fields and improve query efficiency. But pay attention to data consistency and transaction management. NoSQL databases are more flexible, but development and maintenance costs are also increased accordingly, and they need to be chosen based on actual conditions and team technical capabilities.

Finally, build an efficient data conversion layer . The data conversion layer is responsible for cleaning, converting and integrating data from different data sources to generate data structures that meet business needs. This can be a standalone service or integrated into the data acquisition or processing process. It can be implemented using ETL tools or custom scripts, and the key is to handle data source differences flexibly to ensure data accuracy and consistency.

Through the above strategies, we can effectively respond to business needs in nearly 400 fields and improve database maintenance efficiency and scalability.

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