In the world of software engineering, data modeling is the process of creating a data model for an information system. Some formal techniques are applied to make the data models. Introduction to data modeling may be explained as a technique to define and organize business processes.
People use data modeling to analyze, understand, and clarify the data requirements of the users. Then, on this basis, it creates a visual description of the business.
Introduction to Data Modeling
Organizations use it as a vital tool to unlock the value of their data. They also consider data modeling important to fasten up their application development. Research shows that organizations that use this deliver products to markets at a faster speed. The production time is reduced, and the cost reduction is made.
Thus, there is a great demand in the job market for people who know how to do data modeling. Freshers are sometimes nervous about the type of questions the interview panel will ask them. Hence, a comprehensive list of questions and their answers help them a lot. Some essential questions and their answers are as follows:
Data Modeling Interview Question
1.What do you understand by data modeling?
Data modeling is a diagrammatic representation. It shows how the entities are linked to each other. People regard it as the first step towards the database design. At first, people created a conceptual model. Then, they make the logical model. Finally, they move on to the physical model. These are created in the data analysis and the designing phase in the life cycle of software development.
It may also be described as a process of creating a model. The data may store that model in a database. Veteran computer engineers describe it as a conceptual representation of different data objects.
2.What is the different design schema in data modeling?
There are two different types of design schema. They are as follows:
- Star Schema
- Snowflake Schema
3.Explain different types of data models?
Basically, there are three types of data models. They are as follows:
- Conceptual: A conceptual data model defines what the system should contain. Business stakeholders, as well as data architects, typically create this model to define and organize business rules.
- Logical: It defines how the system should be implemented. Business analysts typically create this analytical data model to develop a technical map of data structures and rules. It moves between the theoretical and physical data models. Put, it permits the logical representation of the data to exist, apart from physical storage.
- Physical: This elaborates how the system will be implemented with a specific DBMS system. Developers typically create this model to implement the model. The schema describes here how the data is physically stored in the database.
4.Explain your experience regarding data modeling?
I used to work for a health insurance company earlier. There, we have interfaces that are built-in Informatica. At first, the data is fetched from the Facets database. The Informatica interfaces build processes and transform this data. They send out valuable information to the vendors too
We had different entities. All of them were linked to each other. Subscribers, members, enrolment, bill, healthcare providers and commission formed these entities. Every data entity has its data attribute. E.g., The provider identification number will be a data attribute of the provider.
5.Which schema is better- star schema or snowflake schema?
The choice of a particular schema always depends upon the scenario and the requirement of a project.
Star schema is in de-normalized form. Hence, the users require lesser joins for a query. On the other hand, snowflake schema is in normalized form. So, it will need a higher number of joins than a star schema. So, the query is convoluted.
The execution of snowflake schema will be slower than star schema. In a star schema, the question is more straightforward. So, it runs faster.
Also, star schema contains a high level of redundant data. So, it isn’t easy to maintain. Snowflake data do not have redundant information. So, it is easy to maintain.
It is better to opt for a snowflake schema if the purpose of the project is to do more dimensional analysis. On the other hand, if it is better to opt for star schema in case the goal of the project is to do more of a metrics analysis.
6.What do you mean by normalization?
The process of designing the database in a way that data redundancy is reduced, without compromising on integrity.
7.What is the purpose behind using normalization for a data modeler?
There are multiple purposes behind using normalization for data modelers. They are as follows:
- Remove redundant data
- Reduce the complexity of data
- Ensure data dependency and that the information is logically stored
- Ensure the relationship between the tables and the data staying in the tables.
8.What do you mean by the table?
A table consists of data that is stored in columns and rows. The columns show the data in vertical alignment.
Columns are also known as fields. Rows represent the horizontal alignment of the data. They are also known as records or tuples.
9.What do you mean by denormalization?
A technique where redundant data is added to an already normalized database is known as denormalization.
10.What is the purpose of denormalization?
Denormalization sacrifices the write performance to improve the read performance.
11.Could you explain critical relationship types found in a data model?
Three main relationship types are found in they are as follows:
- Identifying: Generally, a relationship line connects the parent tables with the child tables. However, the reference column of the child table may be a part of the primary key in the table. Then, the tables are connected by a thick line. It signifies an identifying relationship.
- Self-recursive: It is a standalone column in a table. It is connected to the primary key in the same table.
- Non-Identifying: A dotted line may connect the tables in a child table’s reference column. It may signify a no-identifying relationship. The tables are associated with a dotted line only when the queue isn’t a part of the primary key of the table.
Frequently Asked Data Modeling Interview Questions
12.Explain some of the most common errors in data modeling?
We can encounter a few common errors in the data model. The standard errors are as follows:
- Missing Purpose: In certain situations, the user doesn’t have any idea about the mission or goal of the business. If the data modeler doesn’t have a clear understanding of the business model of a company, it faces difficulty in creating a specific business senario.
- Unnecessary Surrogate Keys: Surrogate keys are only required when the natural key cannot fulfil the responsibility of a primary key. Unnecessary surrogate keys are an error.
- Creating Overly Broad Data Models: This becomes too complicated if the tables are higher than 200. It increases the chances of failure.
- Inappropriate Denormalization: Denormalization tends to create redundant data. It is challenging to maintain redundant data. The users should not use this tactic without a valid reason.
13.What is granularity?
The level of information stored in a table is known as granularity. It is of two types-high or low. Low granularity contains only low -level information- like that is found in fact tables. High-level granularity has transaction-level data.
14.How is metadata necessary in the context of data modeling?
Metadata is the type of data that covers what kinds of data are in the system, who uses it, and for what. Alternately, it may be defined as “Data about data.”
15.Explain the two different design schemas?
The star schema has a fact table in the center. Multiple dimension tables surround it. A snowflake schema is similar to it.
The only difference is that a snowflake schema has a higher level of normalization. As a result, the schema resembles a snowflake.
16.What do you mean by an Enterprise data model?
An Enterprise data model consists of all the entries that an enterprise requires. The data models are split up into different subject areas for clearer understanding. It helps a standard and consistent view and interpretation of data elements and their relationships across the enterprise.
17.How would you explain the slowly changing dimension?
In data-warehousing, different dimensions are used to manage historical data as well as current data. Four different types of slowly changing sizes are available: from SCD Type 0 to SCD Type 3.
18.Differentiate between Forward Engineering and Reverse Engineering
A process where Data Definition Language (DDL) scripts are generated from the data model itself is known as Forward Engineering. The DDL scripts may be used to create databases.
On the other hand, Reverse Engineering creates data models from a script or a database.
19.What do you understand by relational data modeling?
Relational data modeling refers to a visual representation of objects in a relational database.
20.What is OLTP data modeling?
OLTP is an acronym, the full form of which is Online Transactional processing. It is an approach by which data models are constructed for transactions. All online transactions and bank transactions are examples of OLTP data modeling.
21.What is the data model repository?
The data model and its essential data, like attribute definition, entity definition, data types and columns are known as the data model repository. The repository may be accessed by the data modelers and their whole team.
Scenario Based Data Modeling Interview Questions
22.What do you mean by ERD?
Basically, data models are tools used to analyze and describe the data requirements. They also describe the assumptions and data conditions in the system. Then, the ERD plays a vital role. ERD stands for Entity-relationship Diagram. It is a logical representation of identities. The purpose of an ERD is to define the relationship between entities. The entities are found inbox. The arrows symbolize the relationships.
23.What do you mean by data sparsity? How does it affect Aggregation?
Data sparsity defines how much data is there for the specified dimension or entity of a model. In case insufficient information is stored in the sizes, more space is required to reserve the aggregations. As a result, an extensive database is a result. Data sparsity helps us overcome this issue.
24.What do you mean by junk dimension?
Junk dimension is a grouping of low-cardinal attributes like flags or indicators. They are removed from other tables and “Junked” into an abstract dimension table. They are frequently used to initiate rapidly changing dimensions within data warehouses.
25.Why are NoSQL Databases more useful than Relational Databases?
NoSQL databases have many advantages over Relational Databases. The benefits are as follows:
- They have a dynamic schema. It allows the database to change or evolve as soon as needed.
- They allow replication which helps in better recovery options.
- They are scalable. NoSQL database may grow or shrink as necessary.
- They may store structured, semi-structured or unstructured data.
- NoSQL databases have sharding. Sharding is the process of splitting up and distributing.
26.Suppose unique content gets applied to a column. If the user tries to place two nulls in it, will it generate an error?
The column will not generate an error because the null error values are never equal. The users may put in as many null values in a queue as they like, but no error will be generated.
27.How would you differentiate between the logical data model and analytical data modeling?
A logical data model is linked to the business requirements. Analytical data modeling is used to create a logical data model.
28.What do you mean by constraint? Why are restrictions significant for a database?
A constraint is a rule that is imposed on data. The different types of discretion include Composite keys, null values or foreign keys.
29.Define an unique constraint of a database?
The users add unique content to avoid duplicate values within the column.
30.What do you mean by check constraint in a database?
The users use a check constraint to define the range of values within a column.
31.What do you understand by factless fact? Why is it used?
A factless fact table is a fact table which only contains dimensional keys. It doesn’t include any fact measure in it.
It is necessary for specific business situations. E.g.:- A user may need to maintain an employee attendance record system. Here, they may have a factless fact table, with three keys. The factless fact table may offer the flexibility of design here.
32.All databases should be in 3NF. Explain?
The statement is false. All databases don’t need to be in 3Nf. The users may also create a database without normalization. So, the database doesn’t need to be in 3nf.
33.How may many child tables be created from a single parent table?
The number of fields or columns that are present in the parent table is equal to the number of child tables that may be created out of a single parent table.
34.Explain the fact tables?
A fact table is a central table that contains numeric values, also known as measurements. It is surrounded by dimension tables and is also found in a star schema or snowflakes schema.
35.What do you mean by data modeling technique?
Data modelling technique is the representation of the logical data model and physical data model, according to business requirements.
36.What are the different types of fact table?
Three types of fact tables are available. They are as follows:
- Additives: They are measures that may be added across any dimension.
- Non-Additive: They contain measures that may not be added across any size.
- Semi-additive: They have standards which may be added across some dimensions.
37.How would you differentiate between the logical data model and physical data model?
The differences between logical data model and physical data model are as follows:
- The logical data model is the representation of the database in a logical way. The physical data model is the representation of the physical database.
- The logical data model has several parameters, like the business rule and critical entry. A physical model has several parameters, like database performance and material storage.
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Conclusion: Data modelling is a technique used to design the database. It helps the users fetch different types of complicated SQL queries in the DWL environment. Hence, software developers are keen to learn.