Top 30 Data Modeling Interview Questions in 2024

Data modeling is a key aspect of database design and management, involving the creation of conceptual models to represent the structure of data and its relationships within a database. This process is crucial in ensuring that the data is stored efficiently, accurately, and in a way that is easy to access and modify.

Given the importance of data modeling in software development, database administration, and system analysis, data modeling interview questions are common in various technical roles. This article aims to provide an overview of data modeling, outline the roles that often require expertise in this area, and list essential interview questions and answers to prepare candidates for their interviews.

• Database Administrator
• Data Architects
• Data Analysts
• Software Engineer
• System Analysts
• Business Analysts

1. What is a relation?

In database terminology, a relation is a set of tuples having the same attributes. It’s essentially a table in the context of relational databases. Each tuple (row) in a relation represents a distinct item or entity, and each attribute (column) represents a property of that entity. Relations are defined by a schema, which describes the types of the attributes. The uniqueness of tuples in a relation is often enforced by a key.

2. Why should we not use strings as keys?

Strings are generally avoided as keys in databases for efficiency reasons. They usually require more memory and are slower to compare and sort compared to integer keys. Integers are easy to compare, often taking O(1) time to evaluate. Strings, however, take O(n) time to evaluate, where n is the length of the string. A VARCHAR(256) string would, in the worst case, take 256 times more processing power to index and compare. Additionally, strings can have issues with case sensitivity and whitespace differences, making them less reliable as unique identifiers.

3. Why does normalization help preserve data integrity?

Normalization, a process of organizing data in a database, aims to reduce redundancy and improve data integrity. By dividing a database into multiple related tables and ensuring that each table represents one concept or entity, normalization helps prevent inconsistencies and anomalies in data.

In simpler terms, a distinct entity should only be represented once. Let’s look at the following table, for example.

Student Relation:

StudentID	Name	University	Number of Students
1	Gretchen	New York University	30,523
2	Reanne	Texas Tech University	24,321
3	Sheena	Texas Tech University	24,321

The relation above is not normalized. Simply put, Texas Tech University, an entity that’s not a student, and is a distinct entity, should not be represented twice. In this case, however, it is represented twice in the University column. This is problematic as this means that the University entities will take up additional space every time an entity (not only the Student entity, but other entities as well) has any sort of association with the university entity.

Moreover, any data related to the University column, (ex: Number of Students) will need to be updated for every unique entity. For example, when a student enrolls in Texas Tech University, all students enrolled in the university will need their “Number of Students” column to be updated.

Here’s a quick fix for that:

Student Relation

StudentID	Name	UniversityFK
1	Gretchen	1
2	Reanne	2
3	Sheena	2

University Relation

UniversityID	University	Number of Students
1	New York University	30,523
2	Texas Tech University	24,321

4. Explain what a primary key is, and how it is related to a foreign key.

A primary key is a unique identifier for each record in a database table. It ensures that each record can be uniquely identified. A foreign key, on the other hand, is a reference in one table to a primary key in another table. This relationship enables the representation of relational data, linking records between tables and maintaining data integrity across the database.

StudentID	Name	UniversityFK
1	Gretchen	1
2	Reanne	2
3	Sheena	2

Example: In the relation above, StudentID is a primary key. Meanwhile, the UniversityFK is a foreign key.

5. Explain why natural keys may be a bad idea.

Natural keys are derived from real-world data (like email addresses or social security numbers) and may pose challenges due to their potential to change over time and lack of inherent uniqueness. These properties can lead to complications in maintaining the integrity of the database, as changes in the real world need to be reflected accurately in the database. Moreover, natural keys may not always provide the necessary uniqueness, leading to potential conflicts in identifying records uniquely.

Example: Using an email address as a primary key can be problematic if a person’s email address changes, necessitating updates across multiple tables and records, which can be both complex and error-prone. Additionally, two individuals might share the same name, causing ambiguity if the name is used as a key. Therefore, synthetic keys like auto-incremented IDs are generally preferred for their stability and uniqueness.

6. What is an index, and how does it improve database performance?

An index in a database is a data structure that improves the speed of data retrieval operations, akin to an index in a book. It allows the database engine to quickly locate the data without scanning the entire table. This is especially beneficial for large tables and columns that are frequently queried.

The most common type of index in relational databases is the B-tree (balanced tree) index. A B-tree is a self-balancing tree data structure that efficiently maintains sorted data and allows for logarithmic time complexity in searches, sequential access, insertions, and deletions.

When a query is executed, the database engine uses the index, particularly the B-tree structure, to rapidly locate the rows it needs. This is in stark contrast to a full table scan, which has a time complexity of O(n) where n is the number of rows in the table. The binary search approach of a B-tree reduces the number of comparisons needed to locate a row, typically achieving a time complexity of O(log n).

This asymptotic efficiency is what makes indexes particularly powerful in large databases, where they can significantly cut down the search time by avoiding the need to examine every row and instead quickly zeroing in on the desired subset of data.

For example, if you are looking for the University details of studentID 532 with universityID 29, a non-indexed column would sequentially scan all the values until it finds universityID 29. Suppose that you have ten thousand universities in the University column, in the worst case, it would take ten thousand scans to find the University of studentID 532. However, with an index, this would approximately take four scans.

7. What are stored procedures, and how are they beneficial in a database system?

Stored procedures are pre-written SQL code that can be saved and executed in the database. They are beneficial because they allow for the encapsulation of complex business logic within the database, reducing the amount of data transferred over the network. Stored procedures also provide a layer of security by abstracting the underlying database schema, and they promote code reuse.

8. How would you model a many-to-many relationship in a relational database?

In a relational database, a many-to-many relationship is modeled using a junction table, also known as an associative or linking table. This table is crucial as it holds the foreign keys from each of the related tables. For instance, if you have two tables, Students and Courses, where a student can enroll in multiple courses and a course can have multiple students, the junction table, say StudentCourses, will contain the primary keys from both Students and Courses tables as foreign keys. Each record in the StudentCourses table represents a unique association between a student and a course, thereby effectively mapping the many-to-many relationship. This approach ensures normalization, reduces redundancy and maintains data integrity.

9. What is referential integrity in a relational context?

Referential integrity is a critical concept in relational databases that ensures the consistency and reliability of the data. It involves maintaining the correctness and validity of the relationships between tables. This is achieved through constraints that enforce the existence of a valid relationship between foreign keys and primary keys in related tables. For example, if a foreign key in one table refers to the primary key of another table, referential integrity dictates that you cannot have a value in the foreign key column that does not exist in the primary key column of the referenced table. This rule prevents the creation of orphan records (where a child record does not have a corresponding parent record) and ensures that relationships between tables remain valid over time.

10. Determine the concept of a composite key in a relational database.

A composite key in a relational database is used when no single column can serve as a unique identifier for table rows. It is a combination of two or more columns in a table that can be used to uniquely identify each row in the table. The columns that make up a composite key are often foreign keys that individually do not guarantee uniqueness but together do. Composite keys are particularly useful in representing relationships where each of the participating entities contributes to the identity of the association. For instance, in a StudentCourses junction table mentioned earlier, the combination of StudentID and CourseID can act as a composite key. Each of these IDs is not unique by itself in the junction table, but their combination is unique, effectively preventing duplicate entries and accurately representing the many-to-many relationship.

Data Modeling Basics

1. What is data modeling?

Data modeling is a visual representation of data flow, policies, internal information system architecture, and how they each relate to other objects, structures, and data points.

Data modeling revolves around the needs of the company, typically expanding and contracting as initiatives start and finish. Moreover, the methods of modeling data follow specific rules in order to properly assess and accurately design database systems, which are then implemented down to the last detail.

2. What are the four different types of data modeling?

• The hierarchical data model derives its architecture from the shape of a tree, and each data retrieval request requires the whole tree to be traversed. The relationships inside this model are one-to-many. As such, this model is known to be relatively inefficient compared to more modern alternatives.
• The E-R (Entity-relationship) data model focuses on the relationships between entities and truly excels as a visual model. That visualization is also great at depicting the database design objectives.
• A relational model uses data tables to collect individual elements into relations. Developed by IBM researcher E.F. Codd, relational models rarely touch on the physical properties of the data storage technology being used.
• Finally, the network model uses an approach akin to a graph in which there are nodes and edges. This model allows you to make connections between fundamental relationships. Moreover, it allows for flexibility in representing one-to-many, many-to-one, many-to-many, and one-to-one relationships.

3. What are the steps of data modeling?

Data modeling, as an approach, requires extensive and rigorous planning in order to both properly convey business requirements and provide stakeholders with a clear path as to how data flows.

Despite the differing approaches toward data modeling, there is a tendency within data modeling methods to trace the following steps:

1. Identifying and exploring entities - in this stage, we will determine the entities, their definitions, and critical properties. For example, for a “student” entity, there will be properties such as “ID”, “First Name”, “Last Name”, “Year”, and more.
2. Identifying entity relationships - now that we have structured our key entities and their properties, we will generate and connect these entities based on their designated relationship. For example, we could assign the entity “Teacher” and connect them with a “Student.”
3. Mapping attributes to entities toward business requirements - at this point, we will be evaluating and mapping entities in regard to their ability to fulfill a business requirement. For example, at a learning institute, are relationships mapped thoroughly to help generate a fully interactive database?
4. Determining the degree of normalization - while it may seem like normalization is a binary approach, wherein either you apply it to your data or not, normalization is most often applied only up to a certain degree. In this stage of data modeling, it is crucial to find the balance between efficiency and speed versus the resources you have available.
5. Iterate and finalize - now that you have made your data model, it is best to start again and build the entire model from the beginning– this time with a contextual view of how to approach your data modeling project.

Technical Data Modeling Questions

4. What is denormalization in data modeling and how does it differ from normalization?

Normalization and denormalization are database modeling methods that counteract each other within the “time vs. space” complexity debate. If you have a bit of a background working with algorithms, it is likely that you have encountered the Big O notation, by which an algorithm’s time and space complexity can be expressed.

Typically, time and space have an inverse relationship; the more extensive an algorithm’s memory requirement is, the faster the algorithm will perform. The exact relationship present to the question can then lend itself towards normalization or denormalization.

Normalization focuses on removing redundant data to maximize space efficiency. In big data, these space savings can go from minuscule to massive (even potentially reducing one’s operating costs).

However, because of the reduction in space, queries are typically slower. Denormalization works to deprioritize space and increase the algorithm’s speed in performing queries and joins. This is because denormalization allows for data duplication, by which creating queries and joins is easier and faster.

5. What is data governance, and why is it important?

Data governance is a set of self-determined rules and policies for handling your database system. It is an integral part of data modeling and, in a sense, defines your database’s philosophy and approach.

An important thing to remember about data governance is that these so-called “self-determined” rules should not be made in a vacuum and should still be compliant with both industry standards and other technologies you plan to use during the life cycle of a database.

Data governance also provides structure to the policies that take care of how data:

• Is handled
• Is secured
• Is kept private
• Is disposed
• Maintains Integrity

11. What are the ACID properties? When is ACID useful?

ACID stands for Atomicity, Consistency, Isolation, and Durability. These are key properties that guarantee database transactions are processed reliably.

• Atomicity: Ensures that all operations within a transaction are completed successfully. If one part of the transaction fails, the entire transaction fails, maintaining data integrity.
• Consistency: Signifies that a transaction can only bring the database from one valid state to another, maintaining database invariants.
• Isolation: Ensures that concurrent execution of transactions leaves the database in the same state as if the transactions were executed sequentially.
• Durability: Assures that once a transaction has been committed, it will remain so, even in the event of a system failure.

ACID properties are crucial in systems where the integrity of data is paramount, such as financial systems, where it is vital that transactions are processed reliably.

12. What are database triggers, and when are they useful?

Database triggers are procedural code that is automatically executed in response to certain events on a particular table or view in a database. They are useful for maintaining the integrity of the information in the database and can be used to perform tasks like automatically updating or checking the consistency of data when a change occurs. Triggers can help to enforce complex business rules at the database level, log changes to data, or automatically propagate changes through related tables.

13. What is a partial dependency in relational database design?

In relational database design, a partial dependency means that a non-primary key attribute is functionally dependent on part of a composite primary key. This situation violates the rules of the second normal form (2NF) and can lead to data redundancy and inconsistency. It occurs when an attribute in a table is dependent on only a portion of a composite key rather than the whole key.

14. When should we consider denormalization? Make a case for denormalized design.

Denormalization should be considered when there is a need for optimizing database performance, especially for read-heavy operations. It involves introducing redundancy into a relational database by incorporating data from related tables into one table. This can reduce the number of joins needed for queries, thus enhancing query performance. However, denormalization can also increase the complexity of data maintenance and the risk of data inconsistencies. It’s a trade-off between read performance and the integrity and maintainability of the data. In scenarios like reporting and data warehousing, where quick data retrieval is more critical than data normalization, denormalized design is often preferred.

15. Explain the Boyce-Codd normal form. How is it different from the third normal form?

Boyce-Codd Normal Form (BCNF) and the Third Normal Form (3NF) are both stages in the normalization process of a database, designed to reduce redundancy and improve data integrity. They are closely related but have distinct characteristics.

BCNF is an extension of the Third Normal Form and can be seen as a stricter version of 3NF. A table is in BCNF if, and only if, for every one of its non-trivial functional dependencies (X → Y), X is a superkey. This means that:

• The left side of every non-trivial functional dependency is a superkey, which is a key that uniquely identifies a row in a table.
• BCNF addresses the anomalies left by 3NF when it comes to certain types of functional dependencies, particularly those involving composite keys.

BCNF is designed to handle certain types of redundancy that 3NF does not address, specifically in situations where 3NF tables have composite keys and there are functional dependencies between these composite key attributes.

16. Differentiate views from a concrete view.

In database systems, a view is a virtual table based on the result-set of an SQL statement. It contains rows and columns, just like a real table, but the data comes from one or more tables referenced in the SQL statement. A concrete view, on the other hand, typically refers to materialized views. A materialized view is a database object that contains the results of a query. Unlike a standard view, which dynamically calculates the data when accessed, a materialized view is stored on the disk, which can improve performance but requires extra storage space and can become outdated.

17. Suppose that we change the value of a primary key, what complications could happen?

Changing the value of a primary key can lead to several complications:

• Referential Integrity Breach: If the primary key is referenced by foreign keys in other tables, changing it can break these links, leading to referential integrity issues.
• Index Rebuilding: Since primary keys are often indexed, changing them might necessitate rebuilding the index, which can be resource-intensive.
• Application Impact: Applications using the database might expect the primary key to remain constant. Changing it could cause unexpected behaviors or errors in these applications.
• Data Inconsistency: If the change is not properly propagated to all related records and systems, it can lead to data inconsistencies.

18. Case Study: International e-Commerce Warehouse

More context. Let’s say you work for an e-commerce company. Vendors can send products to the company’s warehouse to be listed on the website. Users are able to order any in-stock products and submit returns for refunds if they’re not satisfied.

The front end of the website includes a vendor portal that provides sales data in daily, weekly, monthly, quarterly, and yearly intervals.

The company wants to expand worldwide. They put you in charge of designing its end-to-end architecture, so you need to know what significant factors you’ll need to consider. What clarifying questions would you ask?

What kind of end-to-end architecture would you design for this company (both for ETL and reporting)?

19. What is a STAR schema, and when is it used?

A STAR schema is a type of database schema used in data warehousing and business intelligence. It consists of a central fact table surrounded by dimension tables. The fact table contains quantitative data (like sales amount, units sold) and keys to dimension tables, which contain descriptive attributes related to the data in the fact table (like time, product, and store). This schema is used for its simplicity and efficiency in handling common types of business intelligence queries.

20. Case Study: Digital Classroom System Design

More context. As a data engineer for Slack, they asked you to design their new product, “Slack for School”. When designing their database, you ponder upon the following questions:

1. What are the critical entities, and how would they interact?
2. Imagine we want to provide insights to teachers about students’ class participation. How should we design an ETL process to extract data about when and how often each student interacts with the app?
3. Suppose a teacher wants to see the students’ assignment submission trends over the last six months. How would you write a SQL query to retrieve this data?

How would you answer?

21. Explain the implementation of a multi-tenant architecture in database design.

Multi-tenant architecture in database design is a principle where a single instance of the software application serves multiple customers or tenants. Each tenant’s data is isolated and remains invisible to other tenants. In the database context, this can be implemented in several ways:

1. Separate Databases: Each tenant has its own database. This ensures maximum data isolation but can increase the maintenance overhead and resource consumption.
2. Shared Database, Separate Schemas: Tenants share a database, but each has its own schema. This balances isolation with resource utilization.
3. Shared Database, Shared Schema: All tenants share the same database and schema. Data for each tenant is differentiated by a tenant ID in each table. This approach maximizes resource sharing but requires careful management to maintain data security and privacy.

22. Case Study: Payment Data Pipeline

More context. Let’s say that you’re in charge of getting payment data into your internal data warehouse.

How would you build an ETL pipeline to get Stripe payment data into the database so analysts can build revenue dashboards and run analytics?

23. How would you create a tree-like structure using relations?

Creating a tree-like structure in a relational database involves designing tables that can represent hierarchical relationships. One common approach is to use a self-referencing table, where each record includes a foreign key that references the primary key of its parent. For example, in an employee table, each employee record could have a manager_id column that references the employee_id of their manager. This method is simple but can become complex to query for deep hierarchies. Alternative approaches include nested set models and materialized path models, each with its trade-offs in complexity, ease of querying, and performance.

24. Explain the concept of Conformed Dimensions and their importance in dimensional modeling.

Conformed dimensions are dimensions that are consistent and reusable across multiple fact tables or subject areas in a data warehouse. They have the same meaning and content when used in different tables. For instance, a time dimension with attributes like year, quarter, month, and day can be used across various fact tables (like sales, marketing, etc.). The importance of conformed dimensions lies in ensuring consistency across different areas of analysis, facilitating the integration of data from different sources, and enabling more comprehensive and coherent business intelligence reporting.

25. Describe a Snowflake Schema and how it differs from a Star Schema.

A Snowflake Schema is a variant of the Star Schema in dimensional modeling. In a Snowflake Schema, dimension tables are normalized, meaning that the data is organized into additional tables to eliminate redundancy and dependency. This contrasts with a Star Schema, where dimension tables are denormalized with all the related data in a single table. While the Snowflake Schema can lead to less data redundancy and smaller database size, it can also result in more complex queries and potentially slower performance due to the increased number of joins.

26. Case Study: Flight Modeling

More context.Suppose that you work for an airline company. They are planning to further digitize their systems and are tasking you to create a subset of their database’s model. The airline company insists that the database model allows for calculations to determine the shortest paths between each airport. Note that paths should be weighed.

Optional challenge: implement Dijkstra’s Algorithm with your database model.

In this case study, you might be tasked with designing a database model for an airline company focusing on storing and querying flight data. Considerations would include how to efficiently store details about flights, airports, aircraft, and schedules, and how to effectively model relationships such as flights between airports, maintenance schedules for aircraft, and crew assignments. Incorporating a system for calculating shortest paths between airports, possibly using graph theory algorithms like Dijkstra’s, would also be a crucial aspect.

27. Describe an approach for manually sharding a MySQL database in a high-transaction environment.

Manually sharding a MySQL database involves dividing the data across multiple databases or tables to distribute the load and improve performance in high-transaction environments. This can be done based on functional segmentation (e.g., customers A-M in one shard and N-Z in another), or via horizontal partitioning (e.g., splitting a large table into smaller tables based on a certain key). Key challenges include ensuring even data distribution, maintaining data integrity, and implementing a consistent sharding logic across all application components that access the database.

28. When designing a full text SQL database, what techniques and considerations are involved? Explain your approach.

Designing a full-text SQL database involves ensuring efficient storage, indexing, and querying of large text data. Key techniques include:

• Full-Text Indexing: Implementing full-text indexes to enable efficient text search.
• Normalization: Balancing normalization with performance needs; denormalization may be necessary for some text-heavy queries.
• Search Algorithms: Implementing advanced search algorithms and features like relevance ranking, natural language processing, and fuzzy matching.
• Scalability and Performance: Considering partitioning and sharding for scalability, and caching for performance.
• Security: Ensuring data security and compliance with data handling regulations, especially for sensitive text data.

29. When would you choose MyISAM over InnoDB as a storage engine?

MyISAM might be chosen over InnoDB in scenarios where read performance is more critical than write performance, and data integrity and transactions are not a primary concern. MyISAM offers faster read operations and simpler design, but it lacks support for transactions, foreign keys, and is not as crash-safe as InnoDB. MyISAM can be suitable for read-heavy applications, like analytics and archiving, where the data doesn’t change often.

30. Case Study: OLAP System and Aggregation Strategy for a Retail Company

More context. You are a data architect consulting for a large retail company. The company has recently implemented an OLAP system to analyze sales, inventory, and customer behavior data. Their data warehouse is updated daily with transactional data from over 500 stores nationwide. The company’s management team uses this OLAP system to make key business decisions.

The management team needs to access various reports: daily sales summaries, monthly customer behavior trends, and quarterly inventory analyses. However, they have noticed that the system’s response time is slow, particularly when generating monthly and quarterly reports. Additionally, they require the ability to perform ad-hoc, drill-down analyses on specific data points.

You have been asked to propose an aggregation strategy that optimizes the OLAP system’s performance for both regular reporting and ad-hoc analytical queries. In your proposal, address the following points:

• Evaluate why the current OLAP system might be experiencing slow response times, especially for monthly and quarterly reports.
• Propose a detailed aggregation strategy. Discuss how data should be aggregated at various levels (daily, monthly, quarterly) to improve performance.
• Explain how your strategy balances the need for detailed transactional data with the performance benefits of aggregated data.
• Describe measures to ensure data accuracy and integrity during the aggregation process.
• Suggest how the system can handle ad-hoc, drill-down queries efficiently in the context of your aggregation strategy.

Solution

This case study on optimizing an OLAP system for a retail company involves addressing several key areas: understanding the causes of slow response times, developing an effective aggregation strategy, balancing detailed data needs with performance, ensuring data accuracy and integrity, and facilitating efficient ad-hoc queries. Here’s a proposal:

Evaluating Slow Response Times

The slow response in the OLAP system for monthly and quarterly reports can likely be attributed to the high volume of transactional data, complex calculations required for aggregation, inefficient data indexing, and possible hardware limitations. Also, network latency could be a factor if the system is cloud-based or distributed.

Proposed Aggregation Strategy

To improve performance, implement a multi-level aggregation strategy. Daily data from each store should be aggregated at the close of business, summarizing key metrics like sales and inventory changes. These daily aggregates would then be compiled into more comprehensive monthly and quarterly reports, reducing the processing load for more extensive time periods.

Balancing Detail and Performance

Maintain a balance between detailed data and performance by storing transactional data in its most detailed form and simultaneously creating aggregated summaries for faster access. Utilize data partitioning to handle the dataset efficiently and consider archiving less frequently accessed data.

Ensuring Data Accuracy and Integrity

Ensure data accuracy and integrity through stringent validation processes at data entry, regular audits comparing aggregated data against detailed transaction records, and robust error logging and correction mechanisms.

Handling Ad-Hoc Queries

For ad-hoc, drill-down queries, maintain flexible aggregation layers to allow detailed analysis starting from summary data. Implement efficient indexing aligned with common query patterns and use caching for frequently accessed information to enhance response times.

Preparing for a data modeling interview involves a strategic blend of revisiting core concepts, practical application, and staying informed about current trends in the field. The goal is to demonstrate both your theoretical knowledge and practical skills in data modeling. Here are five key tips to help you prepare effectively:

1. Master Core Concepts and SQL

Solidify your understanding of fundamental data modeling concepts such as normalization, relationships, and database schema design. Additionally, ensure your SQL skills are sharp, as they are often critical in data modeling roles.

2. Practice with Real-World Scenarios

Engage in exercises that involve creating data models for real-world situations. This practice helps in understanding how to apply theoretical knowledge to practical problems, a skill highly valued in interviews.

3. Understand Various Database Models

Familiarize yourself with different types of databases (relational, NoSQL, OLAP) and their use cases. Understanding the strengths and limitations of each model is crucial for answering scenario-based questions.

4. Review Advanced Topics and Trends

Keep yourself updated with advanced topics like data warehousing, big data technologies, and the latest trends in the field. This knowledge can set you apart, especially for senior roles.

5. Reflect on Past Projects and Experiences

Prepare to discuss your previous work and projects related to data modeling. Be ready to highlight the challenges you faced, how you addressed them, and what you learned. This not only shows your experience but also your problem-solving and critical-thinking skills.

By focusing on these areas, you can enter your data modeling interview with confidence, ready to demonstrate your expertise and understanding of both the theoretical and practical aspects of the field.