Data Warehouse Interview Questions 2025

Data Warehouse Interview Questions for Freshers

This blog section comprises easy data warehouse interview questions. Let us get started!

1. What are Data & Information?

Data refers to unorganized facts which are to be further processed to derive meanings from them. Information, in simpler terms, refers to processed data. We can derive conclusions and meaningful information from that.

2. What is Data Warehousing?

In simple terms, a Data Warehouse is a repository or a central space where we can store our data and perform various operations afterward. Data Warehouse is in the form of a Relational Database. Storing and managing data from various sources in the data warehouse is called Data Warehousing.

3. What is a Database?

A database represents a systematic and organized collection of data that can be easily accessed and managed. It is application-oriented, stores real-time data, and uses Online Transactional Processing(OLTP). 

4. Mention various types of Data Warehouses.

There are three main types of data warehouses which are:

1. Enterprise Data Warehouse
    
2. Operational Data Store
    
3. Data Mart

5. What do you understand about Metadata?

Metadata is a kind of data only, which is evident from the fact that it has the word ‘data’ in it. Metadata is data about data. Metadata provides information about the data stored in the data warehouse or database. It can be the size of the data, source of data, owner of the data, etc.

6. What is a data cube?

A data cube is a logical multidimensional model(3-D model). It is built with the help of data from tables in a database. The body of the cube contains dimension members, and the edges of the data cube contain data values.

7. Mention some disadvantages of the Data Warehouse.

The disadvantages of a data warehouse are as follows:

1. A data warehouse can store huge amounts of data; hence, maintenance is expensive.
    
2. It is also challenging to integrate data from many different sources.
    
3. Data is rigid and has to be stored in a particular format in the data warehouse.

8. Give some data warehousing applications.

Data Warehouse is a repository or a central space where we can store our data and perform various operations afterward. It has various applications.

Some of them are mentioned below.

1. Finance Industry
    
2. Banking services 
    
3. Healthcare Sector
    
4. Retail sector
    
5. Controlled manufacturing

9. What is a Data Lake?

A data lake is a central repository similar to a data warehouse, but it has less storage space and can store any type of data, whether structured or unstructured.

10. What is Data Purging?

Data purging is the technique of removing inactive data from a particular location or storage space permanently. Purging frees up a large amount of space. It moves the data from the base table in the database to the archive/history table.

11. What is ETL?

ETL stands for Extract, Transform, Load. As the name suggests, it is a three-phase process. Extract refers to extracting the data from the source. Transform means changing the data so that the analysis can be done. It involves the process of cleansing, duplicating, updating, etc. Load means loading the transformed data into the target data warehouse.

12. What is Active Data Warehousing?

An active data warehouse is one that not only combines real-time or nearly real-time data for instant decision-making but also keeps past data for analysis. The traditional idea of data warehousing, which mostly relies on batch processing and historical data analysis, is outclassed by this approach.

13. What is SDLC in a data warehouse?

In a data warehouse, the term "SDLC" (Software Development Life Cycle) refers to the organized process of organizing, developing, implementing, testing, deploying, maintaining, and maybe retiring a data warehouse system. It guarantees that the data warehouse complies with organizational requirements and develops over time to suit changing demands.

14. What is Data Mart?

The data warehouse contains data from various sources. Now, we want to focus on detailed data from the data warehouse. Here, we use the Data Marts. Data marts contain data that focuses on a specific subject line of business. In simpler words, it is a subset of a Data Warehouse.

15. What is structured and unstructured data? Give examples.

Structured Data: Structured data has a particular format and uses DBMS storage methods. Structured data has a fixed Schema. Examples-Relational DBMS, Excel files, etc.

Unstructured Data: Unstructured data, as the name implies, is data with no structure and no fixed schema. Example-XML files, audio files, etc.

16. What is the star schema in Data Warehouse?

Star Schema is a data warehouse schema with a central fact table associated with multiple-dimension tables. The dimension table is connected with the fact table with the help of a foreign key. It is the most straightforward schema and is called the star schema as it has a star-like structure.

17. What do you understand by OLTP & OLAP?

OLTP stands for Online Transactional Processing. It refers to the real-time processing of the transactions occurring concurrently. OLAP stands for Online Analytical Processing. It refers to the analytical processing of data and operating queries on it. 

Data Warehouse Questions for Experienced

18. What is a dimension table?

A dimension table in a database or data warehouse contains descriptive information or attributes about the objects or events being analyzed in the system. The dimension table is typically joined to one or more fact tables to provide additional context to the data in the fact tables.

19. Explain the difference between fact table and dimension table?

The central repository of quantifiable facts is a fact table, which is a collection of numerical data and metrics in a data warehouse. A dimension table, on the other hand, gives context to these facts by supplying descriptive features that permit insightful analysis and reporting.

20. Mention some functions performed by OLAP.

OLAP stands for Online Analytical Processing. It refers to the analytical processing of data and operating queries on it. The main functions performed by OLAP are

1. Roll up
    
2. Slice
    
3. Dice
    
4. Drill-down
    
5. Pivot

21. Mention the features of the Data Warehouse.

Data Warehouse has the following features:

1. Subject-oriented: The information stored in the data warehouse is related data based on a particular theme.
    
2. Time-variant: It is designed to store historical data and capture changes over time. It also provides the ability to analyze data.
    
3. Integrated: Data stored in the data warehouse is collected from various sources and integrated into one place.
    
4. Non-volatile: The data warehouse data is not deleted when new data is added.

22. Explain Snowflake Schemes.

Schema is a logical view of the database. Snowflake Schema is the schema that has one fact table ad multiple dimension tables. The dimension tables are further connected to other dimension tables (normalized).

23. What is Data Mining? How is it different from Data Warehousing?

Mining, in layman's terms, is digging deep. Data mining is the extraction of interesting or usable patterns or knowledge from a vast amount of data. Data Warehouse stores the data, whereas data mining extracts meaning from the stored data. In this way, it is different from the Data Warehouse. 

24. Explain the ETL cycle's 3-layer architecture.

The ETL cycle’s 3-layer architecture is described below:

• Staging layer: After extracting data from various sources, it is stored
   
• Data integration layer: Data is then transformed and transferred to the database in this layer. Data is further put into hierarchical groups, facts, and aggregates
   
• Access layer: The Access layer, as the name suggests, is used by end-users to access and retrieve the data

25. Differentiate between OLTP & OLAP.

The difference between OLTP & OLAP is as follows:

26. Describe an ER Model.

An ER model stands for the Entity-Relationship model. An entity is a real-world object. An entity can have several properties called attributes. ER model is a graphical model or diagram representing various entities' relationships. To make an ER model, we require three basic symbols - rectangle, oval & diamond.

27. What are the four 4 stages of data warehouse?

A data warehouse has four phases:

• Offline operational databases and data warehouses: For the purposes of offline analysis, data is initially gathered from a variety of operational sources and stored in the data warehouse
   
• Offline Data Warehouse with Integrated Data Marts: For more focused analysis, data marts are arranged at this step around particular business sectors
   
• Real-time data warehousing: Data is updated almost instantly, facilitating quicker decision-making
   
• Integrated Data Warehousing with Real-Time Data Warehousing: In this level, data from various operational sources are combined in real time to provide a more thorough and current picture of business activities
   

28. What are the components of a Data Warehouse?

Four main components of the Data Warehouse are

• Load Manager: It has the role of extracting and loading the data into the warehouse
   
• Warehouse Manager: It manages the data inside the warehouse. It is responsible for performing data analysis and various other operations
   
• Query Manager: It manages the operations related to user queries
   
• End-user access tools: These are the tools that the end users use to access the data

29. What is Dimensional Modeling?

Many data warehouse designers use the design idea of dimensional modeling when creating their data warehouses. The Facts table and the Dimension table are the two kinds of tables used to hold all the data in this design model. The dimension table contains the context of measurements, i.e., the dimensions on which the facts are computed, while the fact table includes the facts/measurements of the company. A technique for building data centers is called dimension modeling. There are three varieties of modeling.

• Modeling conceptually 
   
• Logical modeling
   
• Physical Simulation

30. Differentiate between Data warehouse and Data Lake.

The difference between Data Warehouse and Data Lake is as follows:

31. Explain the various views of the Data Warehouse.

The four views of the data warehouse are 

1. Top-down view : This view represents the data or information that is present in the warehouse.
    
2. Data source view: In this view, we use operational systems to manage, store and collect the data to form individual data source tables and integrated data source tables.
    
3. Data warehouse view: This view visualizes the data in the form of fact tables and dimension tables. Various schemas, such as Star, snowflake, etc., are represented here.
    
4. Business Query View : This view represents the data from the viewpoint of an end-user. This involves the front end. 

32. What are ACID Properties?

The ACID Properties are described as follows:

• Atomic: It ensures that all statements combined in a transaction are executed as a single unit of work
   
• Consistent: It ensures that the database's integrity is consistent and remains the same before and after the transaction
   
• Isolation: Concurrent transactions occur simultaneously without being affected by each other. One transaction cannot affect another
   
• Durability: The changes after the execution of a transaction become permanent. Alternatively, the effects of the transaction are permanent

33. Explain the three-tier architecture of the Data Warehouse.

The three-tier architecture of the data warehouse is as follows:

Bottom Tier

The Bottom Tier involves using the backend tools to perform actions like extraction, loading and transforming the data on the Data Warehouse server. The ETL cycle is followed at the bottom tier.

Middle Tier

The middle tier comprises an OLAP server. The OLAP server is an intermediate between the end user and the database. The OLAP can be performed using two models. The two models are ROLAP & MOLAP. ROLAP stands for Relational OLAP. It helps in the conversion of multidimensional databases to relational databases. MOLAP stands for Multidimensional OLAP. It helps in carrying out the functions in the multidimensional database itself.

Top Tier

The Top Tier involves the front-end layer visible to the users. It is also called the client layer. We use various tools, such as Query, Reporting, Analysis, and Data mining tools, in this tier. 

34. Differentiate between Data Warehouse and Database.

Data Warehouse is a repository or a central space where we can store our data and perform various operations afterward. On the other hand, a database represents a systematic and organized collection of data that can be easily accessed and managed. The difference between them is as follows.

Data Warehouse MCQ

1. Which of the following is a core component of a data warehouse?

A. OLTP
B. ETL
C. ERP
D. CRM

Answer: B. ETL

2. What is the primary purpose of a data warehouse?

A. Transaction processing
B. Data storage for operational systems
C. Analysis and reporting
D. Storing raw data

Answer: C. Analysis and reporting

3. A star schema in data warehousing consists of:

A. Multiple fact tables and one dimension table
B. One fact table and multiple dimension tables
C. One fact table and one dimension table
D. Multiple fact tables and multiple dimension tables

Answer: B. One fact table and multiple dimension tables

4. What does OLAP stand for in the context of data warehousing?

A. Online Analytical Processing
B. Online Access Processing
C. Offline Analytical Processing
D. Online Application Processing

Answer: A. Online Analytical Processing

5. Which of the following is a characteristic of a data warehouse?

A. Volatile
B. Non-volatile
C. Dynamic
D. Real-time

Answer: B. Non-volatile

6. In data warehousing, what is a "slice" in the context of OLAP?

A. A division of a database
B. A selection of data from a specific perspective
C. A form of indexing
D. A query optimizer

Answer: B. A selection of data from a specific perspective

7. Which of the following is a data warehouse architecture?

A. Two-tier architecture
B. Three-tier architecture
C. One-tier architecture
D. Peer-to-peer architecture

Answer: B. Three-tier architecture

8. Data mining is often performed in which layer of a data warehouse?

A. Staging layer
B. Data integration layer
C. Data analysis layer
D. Data presentation layer

Answer: C. Data analysis layer

9. What is the process of combining data from multiple sources into a single data warehouse called?

A. Data extraction
B. Data mining
C. Data integration
D. Data partitioning

Answer: C. Data integration

10. In a snowflake schema, dimension tables are:

A. Denormalized
B. Highly normalized
C. Partially normalized
D. Not normalized

Answer: B. Highly normalized

Frequently Asked Questions

Q1. What is a Database in the context of Data Warehousing?

A database is a systematized compilation of information kept in electronic format. In data warehousing, a database acts as a basic platform where data can be stored, structured, and retrieved in a thrifty manner to support analysis and reporting.

Q2. Why is a Data Warehouse important for businesses?

A Data Warehouse helps to aggregate business data from different source systems for historical analysis, trend tracking, and report generation. It enhances data quality as well as consistency and accessibility, facilitating more strategic business decisions.

Q3. What are the key features of a Data Warehouse?

Other key features include:

Subject-oriented: Focus on specific business concerns such as sales or finance.

Integrated: Incorporates data from numerous sources.

Time-variant: Captures and retains historical data.

Non-volatile: Data is stable and seldom updated or removed.

Conclusion

In this article, we discussed the data warehouse interview questions. Data warehousing is a critical aspect of modern data management and analytics, making it an essential skill for professionals in the fields of data science, business intelligence, and IT. This blog has covered some of the most important Data Warehouse Interview Questions to help you prepare effectively for your next interview. 

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