A data model is a logical structure that the database adopts, including the relationships and constraints that determine how data is stored and organized, and how data is accessed. Unstructured data can be found in word processing documents, emails, audio or video files, and design programs. A data model presents all the data in a good, clean package for database processing. Therefore, to some extent, data modeling is about the appearance of the data.

Q2. What is Data Modelling and What is the Idea Behind It?

Ans. Data modeling is a way of structuring and organizing data so that the database can be easily used. Data modeling is often used in conjunction with database management systems. Data that has been modeled and produced for this system can be identified in a number of ways, for example, based on what it represents or its relationship to other data. The idea is to make the data as presentable as possible so that it can be analyzed and integrated without any effort.

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Q3. What Are the Different Types of Data Modelling?

Ans. It is among the most commonly asked data modelling interview questions.

There are three basic types of data models, including –

Conceptual data models – A conceptual data model identifies the highest-level relationships between different entities and helps in organizing and defining business concepts.

Features of a conceptual data model

Includes important entities and defines relationships between the entities
No attributes are specified
No primary key is specified

Physical data models – The physical data model represents how the model will be built in the database. A physical database model displays all table structures, including column name, column data type, column constraints, primary key, foreign key, and relationships between the tables.

Features of a physical data model

All tables and columns are specified
Foreign keys can identify relationships between tables
Denormalization can occur at this level
Physical attributes make a physical data model different from logical data models

Logical data models – A logical data model explains the data in very detail, irrespective of its implementation in the database or data storage structures. The features of a logical data model include:

Normalization occurs at this level
Specified attributes for every entity
Specified primary key for every entity
Specified foreign keys

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Q4. What are the Basic Steps Involved in Data Modelling?

Ans. The basic steps involved in data modelling are –

Choosing a data source
Selecting data sets for every data source
Identifying different types of entities and their attributes, columns, and metrics
Identification of the relationship between different data sets and tables
Adding hierarchies to the data models to ensure better data discovery and navigation
Assigning roles and permissions to access the data models
Standardizing the data sets to reduce data redundancy
Deploying the models and denormalizing the data sets to improve performance

Q5. What is the Difference between Data Modelling and Object Modeling?

Ans. Traditional data modeling is different from object modeling because traditional data modeling only focuses on data. Object modeling allow you to explore the behavior and aspects of domain data and see how the application interacts with the information received from an external source. Object modeling deals with how the application interacts with the information received from an external source, whereas using data models can only explore problems of data.

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Q6. What is a Data Mart?

Ans. A Data Mart is a subset of a data warehouse. They comprise of repositories of summary data collected for analysis in a specific section or unit within an organization. A data mart only gets data from a few sources, which can be corporate Data Warehouse, internal operational systems, or external data sources.

Data mart is a query-oriented system where the internal distribution of the data is clear. A data mart is an indexing and extraction system, focused on a specific business area and is designed to meet the needs of a specific group of users. Data Marts are also fast and easy to use because of their low data consumption.

Q7. What is Normalization? Why is it important?

Ans. Normalization is the method of organizing the data in the database efficiently. It is about building tables and establishing relationships between those tables according to certain rules. It is a crucial step to remove redundancy and inconsistent dependency in the data sets, making them flexible.

Redundant data leads to wasted disk space, increased data inconsistency, and slowed DML queries, thus it is important to moralize the data to be able to efficiently use it.

Q8. What is Denormalization?

Ans. Denormalization is the reverse of normalization, where the normalized schema is converted into a schema that has redundant information. Performance is improved by using redundancy and by keeping redundant data consistent. The reason for performing the denormalization is the overhead produced in the query processor by an over-normalized structure. There are several denormalization techniques, including – Adding Redundant columns, Adding derived columns, Collapsing the tables, Snapshots, Materialized Views, Pre-Join Tables, Encoded Values, etc.

Q9. What are the Key Differences Between Normalization and Denormalization?

The key differences between normalization and denormalization are –

Q10. What is a Relational Model?

Ans. The relational model for data modelling is an approach to manage and organize data into one or more tables of columns and rows. Each row is identified using a unique key.

Q11. What is a Relational Database?

Ans. A relational database is a digital database that is created basis the relational data model and stores and provides access to data points related to one another.

Data Modelling Interview Questions and Answers