Top 50+ Data Warehouse Interview Questions – The Complete Guide

A survey projected a growth rate for data warehouse developer jobs as 21% in 2018-2028. With increasing job opportunities and a handsome salary, it has become a lucrative job for many. This blog includes data warehouse interview questions for fresher, intermediate-level, and experienced candidates to practice and revise for your job interview.

Data Warehouse Interview Questions for Freshers

The following are data warehouse interview questions for freshers:

Q1. What is a data warehouse?

A data warehouse is a data management system that stores and analyzes data from a wide range of sources to get useful business insights and make informed decisions based on these insights. It involves cleaning, integrating, and consolidating data.

Q2. What is data mining?

Data mining is the process of examining large amounts of data for hidden patterns and trends. It categorizes data and further facilitates data-driven decisions. It estimates the probability of future events by using advanced mathematical algorithms for data segments. 

Q3. What is business intelligence?

Business intelligence combines various strategies and technologies for data analysis and management of business data for informed decision-making in organizations. 

Q4. What is OLAP?

OLAP or On-Line Analytical Processing, is a software technology that helps in the multidimensional examination of business data or information, complex estimations, advanced data modeling, and trend analysis. 

Q5. What is OLTP?

OLTP or On-Line Transaction Processing, is a technology that supports transaction-oriented tasks. It modifies the data as it is received and executes several concurrent transactions.

Also, explore the difference between OLTP vs OLAP.

Q6. What is a dimension table?

A dimension table is a table that contains dimensions keys, values, and attributes used to describe dimensions or objects in the fact table. The primary key in this table uniquely identifies each dimension row or record and links the dimension table to the fact table.

Q7. What is a fact table?

A fact table is a table that contains facts or business information used for reporting and analysis. Foreign fields that connect a fact table to other dimension tables are also saved here.

Q8. What is a surrogate key?

A surrogate key is a substitute for a natural primary key. It uniquely identifies each row used as a primary key for the table.

Q9. What is ETL?

ETL is Extract, Transform, and Load, a software that reads the data from a specified data source and extracts the desired subset of that data. Using rules, it transforms the data into the desired state. The load function loads the resultant data to the target database.

Q10. Name the different types of data warehouses?

Different types of data warehouses are enterprise data warehouses, data marts, and operational data stores.

Q11. What is an enterprise data warehouse?

The enterprise data warehouse is a warehouse that provides a centralized location from where all corporate data from several sources is accessed. The employees of an organization can utilize it for accessing data and performing analytics. The main purpose of this data warehouse is to give a comprehensive overview of any object in that data model.

Q12. What is a data mart?

A data mart is a subset of a data warehouse that is focused particularly on the business domain of a team, department, or subject area. It is a pattern specific for retrieving client data in a data warehouse environment.

Q13. What is an operational data store?

An operational data store helps the user to directly access the data from the database and facilitates transaction processing. It also helps in the integration of data from several sources for efficiency in business activities, analysis, and reporting.

Q14. What is a view and materialized view in a data warehouse?

The view is a virtual table formed from one or more base tables or views. It can be used instead of the tables. The materialized view is the table that contains the result of a query. It provides indirect access to the table data. It usually stores summarized data.

Q15. What is a factless fact table?

A factless fact table is a fact table with no measures. There are two types of factless fact tables. One is the table when no measured value of an event exists, but a relationship develops between dimension members of different dimensions. The other type of factless fact table is a table used to describe conditions. It supports negative analysis reports.

Q16. What is an aggregate table?

An aggregate table is a table that contains existing warehouse data grouped into certain levels of dimensions. It is faster and more reliable to retrieve data from an aggregate table than the original table with more records. It improves the performance of a query by reducing the load on the database.

Q17. What is real-time data warehousing?

Real-time data warehousing is the processing of data in real time. It is the system reflecting the condition of the warehouse in real time. The warehouse updates each time, wherever the system executes a transaction, and makes the data available quickly.

Q18. What is active data warehousing?

Active data warehousing is the process of collecting transactions as they change and integrating them into the warehouse. It also maintains planned cycle refreshes. The user can automate routine processes in an active data warehouse. Here, the decisions are sent automatically to the OLTP systems.

Q19. Name some data warehouse solutions?

Some of the popular data warehouse solutions are Oracle Exadata, Google Cloud BigQuery, AWS Redshift, Snowflake, Apache Hive, and Microsoft Azure.

Q20. What is an ER diagram?

ER or Entity-Relationship diagram illustrates the relationship between the entities in the database. It shows the structure of each table and the links between them.

Intermediate Level Data Warehouse Interview Questions

Data warehouse concepts interview questions for the intermediate level are as follows:

Q21. What is metadata?

Metadata is data used to define other data. It gives more details about the data such as the number of columns used, data type of fields, fixed width, etc.

Q22. What is data purging?

Data purging is a group of techniques and procedures that permanently erase and remove data from storage space. It frees up the storage space to use it for other purposes.

Q23. What is data warehouse modeling?

Data warehouse modeling is the process of designing the schemas of the large volumes of data in the warehouse. Various kinds of modeling types are dimension data models, conceptual data models, logical data models, and physical data models.

Q24. Explain the characteristics of a data warehouse.

The following are the characteristics of a data warehouse:

• Subject-Oriented: In a data warehouse, a concise and straightforward view of a particular subject is provided instead of focusing on the current operations of an organization. 
• Integrated: A data warehouse integrates the data from multiple heterogeneous sources such as mainframe and relational databases, flat files, and online transaction records. Integration is necessary to ensure reliability and consistency in naming conventions, encoding structure, attribute types, column scaling, etc.  
• Time-Variant: Historical data is stored in different time intervals such as weekly, monthly, annually, etc. It has a wide-ranging time limit, and we can predict data with the help of a specific time interval. 
• Non-Volatile: A data warehouse is non-volatile. Data stored in it cannot be modified, altered, or updated. When new data is inserted, the old data remains. It allows the maintenance of historical data. The two types of operations available are data loading and data access.

Q25. What are the different types of fact tables?

Different types of fact tables are as follows:

• Transactional Fact Table: It provides a basic view of business processes and is used to depict the occurrence of an event at any given time. But the facts measure are valid only for that specific time and for the specific incident. Various features that this table provides the user are extensive dimensional grouping, drill-down, and reporting features. 
• Periodic Snapshot Fact Table: It shows the condition of things at a specific point in time. For example, it will depict the performance of an activity at the end of each day, week, month, or any other time interval. Since the data here is not detailed, the snapshot fact table relies on a transactional fact table for retrieving detailed data.
• Accumulating Snapshot Fact Table: It is used to depict a process with a well-defined beginning and end. Here, we will find multiple data stamps reflecting the predictable events occurring over a lifespan. It has an extra column where the date of the last update of the row is given.

Q26. What are the advantages of a data warehouse?

The advantages of a data warehouse are as follows:

1. It gives instant access to all your essential information saved in it and saves a lot of time.
2. It enhances the quality of data by converting the stored data into a shared structure and improving the consistency and integrity of the data. 
3. It enhances the business intelligence of your organization by consolidating data from multiple sources.
4. It improves security by including advanced security features in its design. 
5. It can store historical data that helps an organization to study and analyze different periods.

Q27. What are the various types of dimension tables?

The various types of dimension tables are slowly changing dimensions, degenerate dimensions, roleplay dimensions, junk dimensions, and conformed dimensions.

Q28. Explain slowly changing dimensions and degenerate dimensions?

Slowly Changing Dimensions: Here, the dimension attributes vary slowly over time rather than at regular intervals. 

Degenerate Dimension: Here, the dimension attributes are contained in the fact table instead of a separate dimension table.

Q29. Explain the roleplay dimension, junk dimension, and conformed dimension?

• Roleplay Dimension: It is a table with several relationships with the fact table. It happens when the same dimension key and its associated attributes are linked to several foreign keys in the fact table. 
• Junk Dimension: It is a collection of low-cardinality attributes and contains several varied features that are unrelated to each other. 
• Conformed Dimension: They are dimensions that are the same or a proper subset of other dimensions. This dimension is shared by several subject areas or dart marts.

Q30. Give the main differences between a fact table and a dimension table.

The main differences between a fact table and a dimension table are:

• The fact table contains the attributes’ measurements or metrics. The dimension table is a companion table that stores the attributes that the fact table uses to derive the facts. 
• The fact table contains information in both numeric and textual format, whereas the dimension table contains information only in textual form. 
• The fact table does not have a hierarchy, whereas the dimension table does have one.
• The fact table has fewer attributes and more records than the dimension table. The dimension table has more attributes but fewer records. 
• The fact table grows vertically, but the dimension table grows horizontally.

Q31. What is a data cube?

A data cube is a multidimensional model that aggregates data in a cube for faster and easier analysis. It uses OLAP technology for online analytical processing. It stores information in terms of dimensions and facts. In data warehousing, the user can implement an n-dimensional data cube. A data cube can further be divided into two categories, multidimensional and relational.

Q32. What is a star schema in a data warehouse?

A star schema is a multidimensional data model that organizes data in the design of a star. It contains both fact and dimension tables, but there are fewer foreign-key joins in this design. It is optimized to query large data sets quickly.

Q33. What is a snowflake schema in a data warehouse?

A snowflake schema is a multidimensional data model that organizes data in the design of a snowflake. It contains fact tables, dimension tables, and sub-dimension tables. Here, the primary dimension table is joined with the sub-dimension tables. Note that the primary dimension table is the only table that can be joined with the fact table.

Q34. Define data warehouse architecture?

It is a framework that defines the data warehouse design and highlights how the various components of database integrate to work together. There are three common types of architecture. They are one, two, and three-tier architecture. In the basic architecture, we can directly access data derived through many sources. Other architectures include cleaning and processing data before storing them in the warehouse and customizing the architectural design for various groups within the organization.

Q35. What is the purpose of a staging area in the data warehouse architecture?

The staging area is where the data gathered from external sources is structured in a specific format and validated before being loaded into the data warehouse. The process is done with the help of an ETL tool.

Q36. What is VLDB?

A VLDB or very large database is a database that contains a large number of tuples or occupies a large physical file system storage space.

Q37. What is a cloud data warehouse?

The cloud data warehouse is a database created and stored on cloud storage. It is optimized for business intelligence and analytics. This data warehouse does not have physical hardware. It is essential due to the increase in data sources.

Q38. What is XMLA?

XMLA or XML for Analysis is a SOAP(Simple Access Object Protocol)-based XML protocol considered the standard for accessing data in the OLAP method, data mining, or data sources available on the internet.

Q39. What is cluster analysis in data warehousing?

Cluster analysis defines the object without giving the class label. It assigns some sets of objects into groups known as clusters and analyzes data stored in the data warehouse. It compares the cluster with the already running cluster.

Q40. What is agglomerative hierarchical clustering?

The agglomerative hierarchical clustering follows the bottom-to-top approach, where the clusters are read from the bottom to the top. It means the sub-component is read first and then the parent component. It consists of objects that create their clusters which are then merged to form larger clusters. A continuous process of merging takes place until all the clusters are not merged into a complete big cluster containing objects of chart clusters.

Q41. What is divisive clustering?

The divisive clustering follows a top-to-bottom approach and here the parent component is read first, then the sub-component. Here the parent cluster keeps dividing into smaller clusters until each cluster has a singular object to represent.

Q42. What is the chameleon method in data warehousing?

The chameleon method in data warehousing is a hierarchical clustering algorithm that finds similarities between a pair of clusters through dynamic modeling. It uses a two-phase algorithm to find clusters in a data set. It operates on a sparse graph that represents data items through nodes and weights of the data items through edges.

Q43. Explain bottom-up approach architecture in a data warehouse?

The following are the steps for the bottom-up approach in a data warehouse:

• The data is collected from external sources. 
• This data goes through the stage area where it is structured using the ETL tool. 
• Then it is imported to the data mart instead of the data warehouse. The data marts are built and they allow for reporting, focusing on a specific industry.
• Finally, data marts are incorporated into the data warehouse

Q44. What are the main stages of the ETL testing process?

The main stages of the ETL testing process are:

• Identification of data sources and requirements
• Acquisition of data
• Implementation of business model and dimensional modeling
• Building and publishing data
• Report building

Q45. Explain dimensional modeling?

Dimensional modeling is a data structure technique that aims to optimize the database for quick retrieval of data. It is used to read, summarize, and analyze numeric data such as balances, values, counts, weights, etc.

Q46. What is a slice operation?

Slice operation is a filtration process used in data warehouses. From a given cube it selects a specific dimension and a new sub-cube is created. In this operation, only a single dimension is used.

Q47. What is a data lake?

A data lake is a large-scale repository to store all kinds of data, be it structured, semi-structured, or unstructured. It stores the data in its original format without any restrictions on account size or file size. Here, a user can run different kinds of analytics such as dashboards and visualizations, real-time processing, big data processing, and machine learning.

Q48. What are the advantages of a cloud data warehouse?

Some of the advantages of a cloud warehouse are:

• The cost of ownership for a cloud data warehouse is less as compared to an on-premises data warehouse that requires expensive technology, outage management, lengthy updates, and high maintenance. 
• Cloud encryption technology for data protection makes cloud-based data warehouses much safer.
• Cloud data warehouses can quickly integrate additional data sources enhancing the speed and performance. 
• With cloud data warehouses there is an improvement in disaster recovery. Services offered are asynchronous data duplication, automatic snapshots, and backups, access to data from multiple nodes, etc.

Q49. What are some industries where a data warehouse is used?

Some of the industries where the data warehouse is used are

• Banking and Finance: In these industries, data warehouses ensure standard security compliances, get updates on customer deposits and loans, and understand and compare the performances of different branches. They are also used to offer customers better strategies to manage their expenses based on their records. 
• Agritech: They help optimize agricultural practices. Data analysis related to crop inventory, yields, pesticides, etc., can help agribusinesses to identify and solve problems of soil quality, damage due to excessive pesticide, etc.
• Healthcare: Data warehousing in healthcare helps with personalized healthcare services such as diagnostics, prescription, follow-up, etc., provided through a single platform. 
• E-commerce: They help e-commerce platforms optimize their performance and operations by tracking and visualizing key performance indicators such as conversion rates, sales, storage, demand, etc.

Q50. Differentiate between data warehouse and database?

Some of the major differences between a data warehouse and a database are as follows:

Data Warehouse	Database
It is mainly used to analyze historical data.	It is used to execute basic business procedures.
The data collection is subject-oriented.	The data collection is application-oriented.
It uses OLAP or OnLine Analytical Processing.	It uses OLTP or OnLine Transaction Processing.
Here, tables and joins are straightforward because the data warehouse is denormalized.	Here, tables and joins are complicated because the database is normalized.
The data structure is based on a dimensional and normalized approach.	The data structure is based on a flat relational approach.

Q51. Differentiate between data warehouse and data mart?

Some of the major differences between data warehouse and data mart are as follows:

Data warehouse	Data Mart
It is a collection of data gathered from several departments in a company.	It is a subset of a data warehouse focused on a specific department of a user group.
The data stored here is detailed.	The data stored here is simple and limited.
It is used for strategic decision-making.	It is used for tactical decision-making.
The process of designing a data warehouse is challenging.	The process to design a data mart is simple.
The data is collected from a variety of sources.	The data is collected from a limited number of sources.

Q52. Differentiate between data warehouse and data lake?

Some of the major differences between a data warehouse and data lake are as follows:

Data Warehouse	Data Lake
The data is stored in a data warehouse after it is cleaned and processed.	The data is stored in a data lake in its unprocessed state or the original form.
It captures only structured data.	It captures semi-structured and unstructured data other than structured data.
It is apt for operational users since it is structured and easy to use.	It is apt for those users who want to perform in-depth analysis.
The storage is expensive and time-consuming.	The storage is less expensive than a data warehouse.
The schema is decided before the data is stored.	The schema is decided after the data is stored.

Conclusion

To appear for an interview, you must prepare questions covering important concepts about the data warehouse. Above, we have covered data warehouse interview questions for freshers, intermediate-level, and experienced candidates. How many of them were you able to answer correctly?