DATA MODELING

 WHAT IS DATA MODELING?

Data modeling is the process of creating a simplified diagram of a software system and the data element contains text and symbols to represent the data and how it flows. Data models serve as a blueprint for creating a new database or reengineering an existing one. Overall, data modeling assists an organization in successfully using its data to satisfy business information demands. Data modelers, data architects, and other data management experts have created data models with the help of business analysts, executives, and users. Helps to define the primary and foreign keys, relational tables, and stored procedures. Helps to define the database and conceptual, logical, and physical levels. Identify any redundant or missing data. Make maintenance and upgrades on the IT infrastructure faster and more affordable. WHY USE A DATA MODEL? A data model helps design the database at the conceptual, physical, and logical levels. Data model structure helps to define the relational tables, primary and foreign keys, and stored procedures. Database developers may use it to build a physical database since it gives a clear image of the fundamental data. Although the initial data model generation is labor-intensive and time-consuming, it ultimately makes IT infrastructure upgrades and maintenance less expensive and quicker. TYPES OF DATA MODELS: There are several data models available, Mainly THREE types of data models used: Conceptual data model Logical data model Physical data model Other data model types are: Graph data model Relational data model Hierarchical data model Entity-relational data model The data models are used to represent the data and how it is stored in the database. NOTE: Database Schemas and Data Models are not the same. The database schema is a collection of conceptual tools for defining data, data relationships, and consistency constraints, whereas the data model is a collection of conceptual tools for describing data, data relationships, and consistency constraints. CONCEPTUAL DATA MODEL: The conceptual data model is a structured business view of the data needed to support business operations, record business events, and track quality measures. Mainly it is used by business stakeholders. This data model defines WHAT the system contains. It is not tied to specific database and applications technology. LOGICAL DATA MODEL: Logical data models show how data entities are related and describe the data from a technical perspective. This model is typically created by data architects and business analysts. It establishes the structure of data elements and the relationships among them. It is mainly used for: 1.      Business requirements 2.      Quality data structures There are THREE main components of the Logical Data Model: 1.      Entities 2.      Attributes 3.      Relationships PHYSICAL DATA MODEL: The construction of a physical data model is based on a logical model. This data model describes HOW the system will be implemented using a specific DBMS system. It represents relational data objects and their relationships. It is used to generate DDL statements which can then be deployed to a database server. Types of data models: 1.      Flat-file 2.      Hierarchical 3.      Relational 4.      Object-Oriented DATA MODELING SOFTWARE/ TOOLS: some important data mode tools are: ER/Studio DbSchema Pro Ewin Data Modeler Archi SQL Database Modeler IBM InfoSphere Data Architect PgModeler ADVANTAGES AND DISADVANTAGES OF DATA MODELING: ADVANTAGES OF DATA MODELING: It verifies that the items depicted are correct. The ability to create a strong link between tables, stored procedures, primary and foreign keys. Data Model is used as a reference for developing SQL, and SQL logic may be readily constructed utilizing it. Ability to find a solid relationship between the tables, stored procedures, and primary and foreign keys. Clear scope. Identify the source of data. Document data mapping. Control of data redundancy. Build software faster. Data consistency. Security. Enforcement of standards. Reduce cost. Helping businesses to communicate within and across organizations. Allowing the business to document data mappings in the ETL process DISADVANTAGES OF DATA MODELING: You must know the physical data are stored characteristics to develop a data model. A complete application must be modified to accommodate even minor structural changes. Database management systems don't provide a language for set manipulation. In DBMS, there is no standard data manipulation language. CONCLUSION: In this article, we learn about data modeling, types of data models, advantages and disadvantages of data models. The primary goal of a designing data model is to ensure that data objects provided by the functional team are accurately represented. The main disadvantage is that even minor changes in structure necessitate changes to the entire application.

Data modeling is the process of creating a data model for the data to be stored in a database. This data model is a conceptual representation of data objects, the associations between different data objects, and rules.