What is data modelling?
The process of documenting the design of a complex software system through diagrams, text, and symbols that represent the data flow is known as data modelling. This increases the readability and efficiency of the use of data.
What are the different types of data modelling?
The various types of data models are-
- Hierarchical database model
- Relational model
- Network model
- Object-oriented database model
- Entity-relationship model
- Document model
- Entity-attribute-value model
- Star Schema
- The object-relational model
What is a data-modelling tool?
The tools that help in structuring the flow of data, as well as relationships between data, are known as data modelling tools. Examples of such tools are Altova Database Spy, Borland Together, CA Gen, Case Studio 2, CASEWise etc.
Which modelling type should be used for labelled data?
Predictive modelling is used for labelled data. It predicts outcomes of a data using data mining and probability.
What is conceptual data modelling?
The type of data modelling that is concerned with the entities, attributes, and relationships of the data to organise and define the concepts and rules of business is known as conceptual data modelling.
What are the disadvantages of a relational data model?
The disadvantages of the relational data model are-
- It is expensive to set-up and maintains a relational data model.
- The scale and complexity of data make it difficult to read.
- There may be limits on the length of the fields.
- The huge volume of information creates patches of information that can be difficult to locate.
What is the difference between logical and conceptual data model?
The difference between logical and conceptual data models are-
Conceptual Data Model
Logical Data Model
All entities and relationships are included.
Only important entities and relationships are included.
Attributes are not specified.
All attributes are specified.
Primary key is not specified.
Primary key is specified.
What is predictive modelling analytics?
The process of testing and validating a model used to predict the best possible outcome. It finds uses in various branches from machine learning to artificial intelligence and statistics.
What is the disadvantage of the hierarchical data model?
The disadvantages of the hierarchical data model are-
- There is less flexibility as it takes a lot of time to adapt to the changing needs of any organisation.
- The structure poses problems to the ease of vertical communication, inter-departmental communication as well as inter-agency communication.
- Departmentalisation in hierarchical data model creates problems of disunity.
What is logical data modelling?
Logical data modelling is a type of data representation in which data architecture and organization is depicted with the help of graphics without the use of database management technology. It gives all the information about the entities and the relationships between them.
Difference between data mapping and data modelling?
The difference between data modelling and data mapping is that while in data modelling the management of data within an organisational structure is evaluated, in data mapping maps or connections are formed between various data models.
Explain the process-driven approach in data modelling?
Process driven approach in data modelling follows a step-by-step procedure centred on the relationships between organizational processes and entity-relationship models.
What is dimensional modelling in the data warehouse?
Dimensional Model in the data warehouse is a type of database structure that optimizes online queries as well as data warehousing tools. It composed of "fact" tables and "dimension" tables. It is used to read, summarize and analyse numeric data.
What are the different types of dimension?
The different types of dimensional data models include star schema, snowflake schema and galaxy schema.
What is data warehouse?
Data warehousing is a process of collecting and handling data from different sources for a wide range of uses to get meaningful insight.
What are the benefits and drawbacks of using data modelling in the data warehouse?
The advantages of using data modelling in data warehousing are-
- It helps you to manage data by normalising it and defining its attributes.
- It integrates the data available in various systems to reduce redundancy.
- It is crucial to the creation of an efficiently designed database.
- It helps the various departments of an organisation to function as a team.
- Facilitates easy access to data.
The disadvantages of using data modelling in data warehousing are-
- Less structural independence
- The system becomes complex.
What is Surrogate & Foreign key in data modelling?
A key without any business meaning but useful for handling changes in the attributes of a dimension table is known as a surrogate key.
A foreign key is a key that represents that creates a link between one or more types of entities in one table with a primary or secondary key in another table.
Define the cardinality in a data model
In a data model, cardinality means the unique data values in a column. If there are a large number of unique values, cardinality is high. Otherwise, it is called low cardinality data.
Define the check and unique constraint?
A check constraint refers to a value in the database to conform to a particular condition.
A unique constraint does not allow several rows to have the same value in the exact same column or a combination of columns. However, it allows some values to be null.