What is Data Modeling?
The method of creating a data store model is called data processing in a database. This introduces theoretical data objects and connections between different data objects. Data modelling is a data formulation process in a standardized format in an information system. It helps to quickly analyze data, which helps to meet business needs. The data modelling process requires data modellers who work correctly with stakeholders and prospective IT users. Data modelling ends with the development of a model of data supporting the infrastructure for the business information system.
Understanding Data Modeling / Scope
It occurs at three different layers:
- Physical model: It is a schema that says how data is stored physically in the database.
- Conceptual model: It is the user’s view of the data, i.e. the high level which the user sees.
- Logical model: It sits between the Physical model and conceptual model, and it represents the data logically, separate from its physical stores.
Hierarchical Data Modeling: These models were used to replace file-based systems. The data was kept in a tree-like one too many arrangements.
Relational Data Modeling: It’s true that the hierarchical model helped us to move from file-based systems, which reduced complexity but still, one had known the specific physical data storage employed. The relational database follows the relational model where data is stored in tables, unlike the Hierarchical database, where it is stored in a tree-like structure. In short, it reduced the complexity more when compared to the hierarchical model.
How does Data Modeling make work so easy/why should we use it?
It helps us in a visual representation of data and enforces business logic, regulations, policies, etc., on data. It is a guide that is used by scientists and analysts in the designing and implementation of a database. So, without data modeling, the job of analysts and scientists to implement the business requirements on a database becomes difficult.
Why do we need Data Modeling? / What can you do with it?
The main goal of using it is:
- To ensure that all data objects are represented correctly as if it is not done correctly, we would get incorrect results.
- It helps, as stated earlier, to design a database at conceptual, physical, and logical levels.
- It helps to design the relational tables, primary keys, foreign keys, etc.
- Database developers can create a better physical database with a good model as it becomes a guiding tool for them.
- It helps to identify missing and redundant data.
- It helps us to have a better IT infrastructure and to have easy and cheap maintenance when required in the long run through it’s time-consuming initially.
Now let’s create a sample data model to understand how to work with a model. To do this, we have to follow certain steps:
- First, we have to understand the requirements; In this case, we will create a model for an online store. So, keeping that in mind, we need two tables a) customers b) products.
- The next step is to get the attributes of the tables or entities.
a. customer table can have attributes like:
b. Product table can have attributes like:
In the customer table, we can have Id as Primary key and similarly, Product Id in the Product table will be the primary key as shown in the below diagrams.
Now, we will design the relationship between these two tables. So to connect the customer and product table, we will create a table called purchase which will be like an order table (i.e. which customer ordered which product).
If you look in the figure above, the customer-purchase reference is OK because every purchase has one customer, and one customer has many purchases. So, this reference is okay. One more thing we have taken user_account_id as a foreign key (the reference to the id in customer key). Similarly product_id. There is still a problem with product-purchase reference as several products can be bought in one purchase as well several purchases can include the same product.
To overcome this, we will design an intermediary table known as purchase_item, which will be connected with purchase and product. In the below figure, we can see the problem resolved.
There are various advantages are as follow:
- It helps businesses to communicate, plan across their organization.
- It helps to recognize the correct source of data that can be used to populate the model.
- This can be used to define relationships between different tables like primary key, foreign key, etc.
Who is the right audience for learning this technology?
It is very essential. The right audiences for learning modeling techniques are individuals who are data architects and data analysts. Most individuals start as data analysts and then move up the ladder.
How will this technology help you in career growth?
According to Glassdoor, the average salary in the market for modelers is projected to earn about $78,601 on an average. So you can see that it is a well-paid job. Most big companies invest in modelers as they are very essential for keeping the integrity of data.
In conclusion, we can say that the model created by modelers ensures consistency in naming conventions, integrity, and security of data because good data will enable the business in the correct, efficient utilization of their data.
This has been a guide to What is Data Modeling. Here we have covered the definition, scope, working, skills, along with advantages and career growth in Data Modeling. You can also go through our other suggested articles to learn more –