Intro
A few months ago, before studying ERD, the concept of relational data modeling was organized through living coding and YouTube. For the time being, the article is a series related to relational data modeling. It will even cover the third normalization and non-normalization.
Hope you enjoy it!
Step of Data Modeling
The step in which backend developers model data is generally as follows.
1. Understand tasks (Work analysis, understand requirements)
It means identifying (analyzing) tasks by looking at documents such as screen design documents and planning documents.
2. Conceptual data modeling
Conceptual data models define the overall structure of business and data and are used to construct business concepts as defined by business stakeholders and data designers. For example, an enterprise may have data about customers, employees, and products, and each data bucket that is an entity has a relationship with another entity; both the entity and the entity relationship are defined in the conceptual model.
3. Logical data modeling
The logical data model is based on a conceptual model based on the specific properties of data within each entity and the specific relationship between these properties. For example, if customer A buys product B from sales team employee C, it becomes a technical model of rules and data structures defined by data designers and business analysts, and helps them make decisions about the physical models needed for data and business requirements.
4. Physical Data Modeling
A physical data model is a concrete implementation of a logical data model, which is commonly created by DataBase Administrator (DBA) and backend developers. Developed for specific database tools and data storage technologies, it provides data to meet the needs of users across business systems along with data connectors.
Key Concepts of Data Modeling
Database modeling concepts are divided into three categories: conceptual data modeling, logistics data modeling, and physical data modeling. From abstract to discrete, the concept of data modeling creates a blueprint for how organizations organize and manage data. Personally, it is 'integrity', 'efficiency (optimization)' and 'expandability' that are important.
Conclusion
There are several types of data modeling. Hierarchical data modeling, graph data modeling, relational data modeling, entity-relational data modeling (ERD), object-oriented data modeling, dimensional data modeling... The article on this series will be summarized for relational data modeling
Reference
https://www.youtube.com/watch?v=zZmRQHgLhMo&feature=youtu.be
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