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Top 35 Data Warehouse Interview Questions & Answers For Experienced

Gone are the days when data processing involved storage, assimilation, retrieval etc. Today, we need robust database management systems where data can be collected from multiple sources, processed concurrently, instant results could be produced, and customer-centric business operations could be ensured too.

Multiple industry verticals like healthcare, IT sector, and government agencies are turning to data warehousing that is powered by Artificial Intelligence and helps to beat the competition. It has given all new dimensions to the businesses and jobs are mushrooming around it.

Data warehousing and Business Intelligence are lucrative career options today for those who are passionate about data management. In this blog, we would help you to learn top data warehousing interview questions with detailed answers and crack your next interview efficiently.

Data Warehouse Interview Questions

Data Warehousing Interview Questions and Answers 

Q1). How will you define the concept of Data Warehousing?

A data warehouse is the data repository that is used for the decision support system. A data warehouse is made up of a wide variety of data that has a high level of business conditions at a particular point of time. In simple words, this is a repository of integrated information that is available for queries and analysis.

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Q2). Define the concept of Business Intelligence.

Business Intelligence is also named Decision Support Systems that refers to technologies, applications, and practices for the collection, integration, and analysis of business-related information or data.

Q3). What are the dimension tables?

A dimension table contains attributes of measurements stored in fact tables. This table is made up of hierarchies, nodes, and categories that can be used to traverse in nodes.

Read: Practical guide to implement Random Forest in R with example

Q4). Define the meaning of Fact table.

A Fact Table contains the measurements of business processes and it will contain the foreign keys for dimension tables.

Q5). Define the meaning of Data mining.

Data mining is a process for analyzing data from different perspectives or dimensions and summarizing the same into meaningful content. Data can be retrieved or queried from the database in their own format.

Q6). Explain the different stages of data warehousing.

  • Offline Operational Database
  • Offline Data Warehouse
  • Real-Time Datawarehouse
  • Integrated Datawarehouse

Q7). How will you define the OLAP?

OLAP or Online Analytical Processing is set to be a system that collects, processes, and manages multi-dimensional data for analysis purposes.

Q8). How can you define the OLTP concept?

OLTP or online transaction processing is an application that modifies the data whenever received from a large number of users.

Q9). How can you differentiate OLTP and OLAP from each other? OLTP vs OLAP

OLTP OLAP
Data is extracted from a single original source. Data is extracted from multiple sources.
Simple queries are made by users. Complex queries are generated by the system.
Normalized small database. De-normalized large database.
There are fundamental business tasks. There are multi-dimensional business tasks.

Q10). What is the full form and meaning of ODS?

ODS or Operational Data Source is a repository of real-time operational data instead of long-term trend data.

Q11). How will you differentiate the View and Materialized View?

A View is a virtual table that takes outputs from queries and can be used in place of tables. A materialized view is indirect access to table data by storing the result of a query into a separate schema.

Q12). How will you define the ETL?

ETL means Extract, Transform, and Load. This is a software application that can read the data from a particular data source and extracts the needed subset of data. In the next step, it will transform the data using lookup tables or rules and convert it to the desired state. In the end, load function is used to load the resulting data from the target database.

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Q13). What do you mean by the real-time data warehousing?

A real-time data warehouse captures the business data as soon as it occurs. When a business activity gets completed, data will become available for instant use.

Q14). Define the concept of VLDB. VLDBs

(Very Large Databases) are those databases whose size is defined as more than one terabyte. These are the decision support systems that are used to serve a large number of users.

Read: What Exactly Does a Data Scientist Do?

Q15). Define the meaning of aggregate tables.

Aggregate table contains the data of an existing warehouse that has been grouped to a certain level of dimensions. This is easy to retrieve data from aggregate tables as compared to original tables because of a large number of records. It can reduce the load in the database server and increases the overall performance of a query.

Q16). How to load the time dimensions?

Time dimensions can be loaded with the help of a program where 100 years are represented with one row per day.

Q17). What are factless fact tables?

A factless fact table does not contain any number fact column in the fact table.

Q18). Define the conformed fact.

A conformed fact is a table that can be used across multiple data marts and multiple fact tables.

Q19). What are non-additive facts?

Non-additive facts cannot be summed up for any of the dimension available in the fact table. If there is any change in the dimension then the same facts can be useful.

Q20). Define the concept of Datamart in brief.

A Datamart is a special version of the data warehouse that contains a snapshot of the operational data and helps business people to make decisions based on past experiences and trends. A data mart is more focused on easy access to information from fact tables.

Data Warehousing Interview Questions and Answers for Experienced

Q21). How can you differentiate the Data Warehouse and OLAP?

A data warehouse is a place where whole data is stored for analysis, OLAP is used for data analysis, aggregations, summation, and information planning at the minor level.

Q22). What is the Entity-Relationship diagram?

An Entity-Relationship diagram illustrates the relationship among entities in a database. This diagram shows the structure of different tables and links between tables.

Q23). What are different types of data warehouse architecture?

  • Single-tier Architecture: The objective of this architecture is to minimize the data volume by reducing the data redundancy. It is not used in practice frequently.
  • Two-tier architecture: This architecture can separate the physically available resources from the data warehouse. It can support a limited number of users only. Because of network limitations, there can be connectivity issues in this architecture.
  • Three-tier Architecture: This is the widely accepted architecture with a top, middle and the bottom tier.

  Q24). How can you define the data analytics in the context of the data warehousing?

Data analytics is the process of analyzing raw data with the purpose of deriving meaning business decisions about the data. The role of a data warehouse is enabling data analysis.

Q25). How can you define a subject-oriented data warehouse?

A subject-oriented database stores data around a subject such as customers, sales, people etc.

Read: Difference Between Data Scientist and Data Analyst

Q26). List different types of OLAP servers.

  • Relational OLAP
  • Multidimensional OLAP
  • Hybrid OLAP
  • Specialized SQL Servers

Q27). What are the different functions performed by OLAP?

The different type of functions performed by OLAP include drill-down, slice, dice, roll up, pivot etc.

Q28). Define the star schema.

A star schema is used in data warehousing where a single table references a number of dimension tables. For the star schema, all keys from dimension will flow to the fact table. It is very much similar to the ER diagram so named as the Star Schema.

Q29). Define the snowflake schema. This is similar to start schema where a single table references a number of dimension tables. These dimension tables are further normalized into multiple related tables. As tables are snow flaked to smaller other tables, it is called a snowflake schema.

Q30). What language can be used for the schema definition?

Data mining Query Language (DMQL) can be used for the schema definition.

Q31). List the different type of dimensions in data warehousing.

  • Conformed dimension
  • Junk dimension
  • Degenerated dimension
  • Role Playing dimension

Q32). What is the role of mini dimension in Data Warehousing?

Mini dimensions are used when a large number of rapidly changing attributes are separated into smaller tables.

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Q33). What do you understand by the term Data Cube?

A data cube helps to represent the data in multiple facets. Data cubes are generally defined in terms of facts or dimensions.

Q34). Explain the concept of Data Modeling.

This is a methodology that consists of dimensions or fact tables. A fact table is further used to store various transactional measurements from dimensional tables that qualify the data.

Q35). What is summary information in Data warehousing?

Summary information is the location within a data warehouse where predefined aggregations are stored.

In this blog, we have included maximum questions for the topic Data Warehousing. With a deep understanding, this is easy to clear the interview in first attempt only. To know more on database concepts and Data Warehousing, join our database certification course and get all necessary skills that are essential for getting hired by top MNCs worldwide.


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