Excerpt: Data modeling has been around in the minds of data processing, storage, and computer programming. The term came into common use around the time database management systems began to evolve in the 1960s. However, planning and architecting a new structure was done long before “data modeling” was ever coined.
Table of contents:
- What is a Data model?
- Why do you need a data model?
- Types Of Data Modelling
- What is SQL?
- How is Data modeling related to SQL?
- What a Relational Database Management System (RDBMS) plays a key role in Data Modelling
As more data, databases, and varieties of data have emerged, data modeling has become more structured and formalized. Nowadays, it’s more important than ever as tech experts struggle to deal with new data sources. Sources include IoT sensors, devices that detect location such as clickstreams and social networks), and a flurry of non-structured and uncompressed data (text audio, video raw sensor output) with rates and speeds beyond the capabilities of conventional techniques that are complex. However, it is now made simpler.
There is a constant demand for new systems, innovative database structures and techniques, and data models to tie this new development effort together. Here, we’ll be discussing the relationship between data modeling and SQL. By understanding how data modeling and SQL work together, you’ll be able to develop more efficient and effective databases.
What is a Data model?
Data models are a general overview of the data in an application. It outlines the data needed, their source, and how they’re organized.
This differs from a schema for databases because the data model incorporates how data is organized throughout all data sources. In contrast, the schema is typically limited to one specific source. It is more focused on how data is organized in abstract terms than the specifics of each source.
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The data model aims to build a framework for how the application will handle and process data. This then forms the basis for building the app’s automation processes and user interfaces.
Why do you need a data model?
A data model is a tool that helps you organize, understand, and document data. It is the foundation for designing database systems. A data model is a collection of concepts describing data’s structure, behavior, and attributes. Data modeling is designing a data structure, typically in the form of a diagram, to organize, store, and retrieve data. The term “data modeling” is used in various contexts, but the basic idea is always the same: to design a structure that will make it easy to store and retrieve data.
Data models are used to:
- Help you understand data
- Document data
- Design database systems
Data models are important because they:
- Help you understand data
- Help you document data
- Help you design database systems
Without a data model, it would not be easy to understand, document, or design database systems.
Types Of Data Modelling
Data Models are classified into three based on the concepts they follow:
- Object-Based Data Models
These models focused on how the data are represented. This model uses objects instead of tables or columns, making it more organized and easier to access information. Additionally, this architecture is well-suited for applications that handle complex relationships between various items.
● E-R Model – The E-R model is a three-schema design pattern that easily creates schemas in database tables.
● Semantic Model – A semantic model is a database model that defines information structure in terms of its meaning instead of its physical representation.
● Functional Model – This design helps simplify complex queries by allowing you to map relations between tables directly.
● Object-oriented Model- It creates and maintains the relationships between data elements. This allows for more organized and manageable databases and easier access to specific information.
- Record-Based Data Models
A record-based data model is a database design that focuses on collecting, storing, and retrieving data records. This database is often used in applications where you need to track individual users or items.
● Network Models – A network model in a database consists of connected entities. They can be bidirectional or unidirectional.
● Hierarchical Models –The hierarchical model in a database management system (DBMS) divides the database into various levels or schemas. Each schema represents a particular data type, such as tables, views, etc.
● Relational Models – These consists of tables that are related to each other. Each table stores data in rows and columns, and the relationships between the tables are defined as foreign keys.
- Physical Models
In this model, all the data in the database are stored physically in files, records, or anything else in a secondary storage device. Here the database structure includes external data structures and their association.
● Unifying Model
● Frame memory
What is SQL?
SQL stands for structured query language. Every relational database software interacts with a language known as SQL because it’s a high-level and easy-to-use language. Its guidelines are provided by a standard organization called ‘ANSI’ adopted by all database vendors like Oracle, my SQL, Microsoft, etc.
How is Data modeling related to SQL?
Most of what data scientists are doing with SQL is retrieving data.
SQL is the language that is essential for data modeling. We create a data model using an SQL server. The data modeling can only be done through RDBMS (Relational Database Management System), a major concept in SQL. In RDBMS, we get to know the workflow of the data. The concept of RDBMS helps achieve data integrity and avoids the complexity of data. In RDBMS, the Entity Relationship diagram is used for modeling the data. For this, one should be familiar with data normalization. In RDBMS, we use the data tables to get the relation between each data in the data table, and according to that, we create a suitable ER (Entity Relationship) diagram.
How Relational Database Management System (RDBMS) plays a key role in Data Modelling
Nowadays, most databases are hierarchically and network-based, which needs help providing adequate infrastructure for business models or requirements. The initiation of a relational database helps maintain data more efficiently and in a simpler manner. A relational database uses logical relations in storing and representing data in tables. Various key concepts are necessary for creating relational data models, such as attribute, relation, tuple, degree, cardinality, column, relation key, and attribute domain.
In RDBMS, the relational data model works using some conditions or constraints for reviewing the validation of a relation: –
- Domain Constraints: Domain Constraints gives importance to each attribute, whether it is absolute to have a value that lies in a range of values. The possibility of not having an attribute value for the corresponding domain leads to not being an apt data type which violates the Domain Constraints. Therefore, for every tuple, the attribute value must be unique. The datatypes supporting the domain constraints are integers, characters, Booleans, variable length, real numbers, strings, etc.
- Key Constraints: A key constraint specifies that each relation must have an attribute or set of attributes to identify a tuple within a tuple uniquely. It cannot have a NULL value and must be unique.
- Referential Integrity Constraint: In Referential integrity constraints, the relational data model is based on foreign keys. It is defined between two linked tables. It is subject to referential integrity constraints; if a relation references a key attribute in another table or the same table, that key must exist in the given relation.
The possibilities for designing databases have changed. The most popular database design in the past was a row-oriented relational system that utilized disk storage technology as a traditional method. The information for an ERP’s general ledger, also known as inventory management, was stored in various tables that required revision and modification.
However, today, many more options are available for database design, including column-oriented relational databases, NoSQL databases, and cloud-based databases. Each has its advantages and disadvantages, so it is important to choose the right one for your needs.
A well-thought-out data model and database design are essential for creating reliable, functional, and secure applications and databases that integrate with data analysis tools and warehouses and enable information exchange between business associates and across multiple applications. Data models created with SQL are well-thought-out and help to ensure data integrity, making your data useful and reliable.
Archit Gupta is a Digital Marketer, and a passionate writer, who is working with MindMajix, a top global online training provider. He also holds in-depth knowledge of IT and demanding technologies such as Business Intelligence, Salesforce, Cybersecurity, Software Testing, QA, Data analytics, Project Management and ERP tools, etc.