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Data Warehouse Interview Questions

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by Yamuna Karumuri
Last modified: July 16th 2021

If you're looking for Data Warehouse Interview Questions & Answers for Experienced or Freshers, you are in the right place. There are a lot of opportunities from many reputed companies in the world. According to research Data Warehouse has an impressive market share. So, You still have the opportunity to move ahead in your career in Data Warehouse Analytics. MindMajix offers Advanced Data Warehouse Interview Questions 2021 that helps you in cracking your interview & acquire a dream career as a Data Warehouse Analyst. 

Data Warehouse Interview Questions and Answers

1. What is data warehousing?

As the name itself suggests that data warehouse is nothing but a central repository of all that data that can be used by different parts of the organization. In general, the repository can be physical or it can be logical as well.  So the data warehousing focuses on the process of accumulating the data altogether and sees how that can be analyzed and accessed at a later point in time.

In the data warehousing concept, they are usually two approaches:

  1. Top-down approach
  2. Bottom top approach

The name data warehousing is given by William H.Inmon, he is considered as Father of Data Warehousing. During this explanation about data warehousing, he specified that data warehousing is nothing but a

  1. Subject-oriented
  2. Integrated
  3. Time-variant
  4. Nonvolatile collection of data.

All of these factors support in terms of making decisions.

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2. What is the main difference between a data warehouse system and an operational database?

The main difference between the data warehouse and the operational database is as follows:

Data warehouse:

A data warehouse is nothing but a collection of all the data that is related to an organization and this data can be used for the data analysis within the organization.

Operational database:

As the name itself is self-explanatory, all the data that is currently being used by the organization for transactional purposes can be considered as an operational database.

3. Explain what is data mart in detail?

A data mart is nothing but an access layer of the data ware environment that is set up and it is widely used to get the data exported to the users. In a sense, data mart can be considered as the subset of the data that we already have in the data warehouse environment. Basically, a data warehouse has a whole chunk of data that is not tailored to a specific team or a department. With the help of data mart, the data can be tailored to a granular level where the information can be extracted and customized so that it can be useful information for a team within an organization.

4. Explain what is a dimension of data warehousing? What are the primary functions of the dimensions?

A dimension can be defined as classification where it categorizes the measures and facts in an orderly fashion. Using these facts and measures, it will help the users to define and provide necessary answers for the business operations.

For example:

The common dimensions that are used are:

  1. People
  2. Products
  3. Place
  4. Time, etc.

The primary functions of the dimensions are as follows:

  • Filtering

5. Define what is a warehouse management system?

  • Grouping
  • Labeling

Usually, these factors are all utilized in the concept of slicing and dicing the data. Out of which slicing refers to filtering the data and dicing the data refers to grouping the data.

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6. What is a data cube and how does it help?

  • A data cube is nothing but a multidimensional database which is an optimized version for data warehouse and OLAP applications. The OLAP applications stand for “online analytical processing applications.
  • The query language that is used in the cube is different from that of the query language used in traditional databases. The language used in data cubes is called “MDX”, which stands for multidimensional expressions.
  • The data cube is defined by the dimensions and the facts and this is how the data is represented in multidimensional cubes
  • A Warehouse management system is also known as WMS. All the previous and old school warehouse management systems provided a simple storage functionality for all the organization data. But the current day warehouse management systems provide a simple storage location at the same time provide a data analysis layer where the data can be analyzed from normal to complex levels. The modern-day warehouse management system has also provisioned with RFID (Radio Frequency Identification) and voice recognition capabilities.
  • No matter what when it comes to data movement or data storage, a warehouse management system is concrete in terms of its basic functionalities.

7. What are the important responsibilities of a warehouse manager?

The important responsibilities of warehouse manager are as follows:

  1. First and foremost performs integrity and consistency changes
  2. Continuously created indexes and also updates where it is necessary
  3. Grouping of the data based on the requirement from the data pool
  4. Frequent backups
  5. Involve in preparing data models as per the requirements from the project teams
  6. Give due attention to maintenance and development processes so that they meet the SOX guidelines.
  7. Constant analysis so that they can make necessary changes to see increased productivity of the data warehouse environment

8. What is a Query manager and what it is responsible for?

The following are the bullet points for Query manager:

  1. With the use of Query manager, all the operations and user queries are managed
  2. Based on the end user access, the complexity of the query manager can be evaluated and defined. It all depends upon the facilities provided by the end users.
  3. Usually, this component is enabled with the end user access tools.

As the name implies, the query manager is responsible for all the user queries that are generated within the environment. Based on the queries used, the data is extracted.

9. What is Load Manager? What is the use of Load manager?

The following are the bullet points of the Load manager, they are as follows:

  1. All the typical data related operations like extraction of the data and loading the data into the data warehouse are carried under a load manager.
  2. Some of the operations that are included and not limited to simple data transformations where it can find a data entry point into the data warehouse
  3. Usually, the size and complexity of the composition depend upon the data warehouses and it is constructed by taking external tools into consideration and custom build programs in few cases.

The following are few things that can be expected from the load manager:

  1. The load manager is responsible for extracting the data from the source system.
  2. Data upload from the source where the data got extracted previously into the temporary data storage.
  3. It has the ability to perform simple data transformation functions where the data can be stored in the data warehouse.

10. What is included under Data Warehouse and what are the benefits of having it in an organization?

The following activities are involved in Data Warehouse:

  1. Retrieving the data
  2. Analyzing the data
  3. Extraction of the data, sometimes customized extraction is also applicable
  4. Loading of the data
  5. Managing and transforming the data

The following are the benefits of the Data Warehouse implementation:

  1. It helps us to maintain a copy of the information from different source transaction systems.
  2. It helps to gather data from different systems into one
  3. Helps us in maintaining data history, most of the time this cannot be maintained at the source data system
  4. Improves the data quality, as the data is cleansed and transformed so that it matches exactly the way the data is needed
  5. Help in data reconstruction so that the raw data that has been gathered can make sense to the relevant users.
  6. More importantly, it saves times
  7. Generates High ROI because the data that has been gathered so far is helpful for the business users where they consider the feedback from the data analysis.

11. What is normalization?

The term normalization is also considered as “Database Normalization”.
This is a process of rearranging or organizing the columns and the tables that are associated in a relational database. By doing this activity, reduces data redundancy and also helps in improving data integrity.

Further, this process also helps in simplifying the database design so that the optimal structure is enabled. In short, normalization helps the data to split into additional tables to incorporate the data and at the same time makes it easy while retrieving the data.

12. What is a fact table? Explain how many fact tables are there is a star schema?

A fact table is nothing but a table that consists of information about measurements, facts, metrics of a business process. It is usually located in the center of a star schema. A star schema is also called a snowflake schema. Usually, a fact table consists of two types of columns:

  1. The first column has the fact data
  2. The second column has the foreign key relation

There is only one fact table that is stored in the star schema or snowflake schema. So, multiple fact tables are stored under fact constellation schema.

13. What is the main benefit of normalization?

The use of the normalization process, it helps in reducing data redundancy. It helps to maintain valid data that makes more sense to the users whenever it is needed.

14. What is data marting? Explain the different kinds of costs associated?

The data marting is also called a “data mart”. A data mart is nothing but a process of redefining information about a specific data set that makes sense for a particular group.

The different kinds of costs associated with data marting are as follows:

  1. Hardware related costs
  2. Software related costs
  3. Network access-related costs
  4. Time costs

15. Expand what DMQL stands for and what is the language that is used in DMQL?

DMQL stands for Data Mining Query Language. This language is used for schema definition. The language that is used in DMQL is nothing but SQL language. SQL stands for Structured Query Language.

16. What does a slice operation mean? How many dimensions are used in slice operation?

A slice operation is nothing but a filtration process. So within this process, only one dimension is used in slice operation.

17. What does a dice operation mean? How many dimensions are used in dice operations?

A dice operation is nothing but a grouping process, the data is grouped based on certain categories. So within this process, 2 or more dimensions are used in dice operation.

18. What is data modeling? Explain what is included in data warehouse modeling?

Data modeling is a process of representing the data view in the form of a graphical way. So within the data modeling process, the following activities are included:

  • Designing a data warehouse database in detail Follows all the principles and patterns of data warehousing and business intelligence.
  • They are several data modeling tools that are available where the data can be displayed in the best graphical way.

The data warehouse modeling includes:

  1. Top-down drive approach
  2. Fact tables and dimensions tables
  3. Multidimensional model or often called a star schema
  4. Normalization and denormalization

19. What are the characteristics of a data warehouse?

The key characteristics of a data warehouse are as follows:

  1. The part of data can be denormalized so that it can be simplified and improve the performance of the same.
  2. A large amount of historical data is stored and used whenever it is needed.
  3. A lot of queries are involved where a lot of data is also retrieved based on the queries
  4. The data load is controlled
  5. Ad hoc queries and planned queries are very common when it comes to data extraction.

20. What is a snowflake in the data warehouse?

Within a data warehousing environment, snowflaking is nothing but dimensional modeling. Within this multiple dimensions are stored in multiple related tables. A snowflake schema is one of the variations of a star schema.

The snowflake schema is used to improve the performance of the queries. The snowflaking concept is widely used in data warehouses and data marts to support a specific set of queries.

About Author

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NameYamuna Karumuri
Author Bio

Yamuna Karumuri is a content writer at Mindmajix.com. Her passion lies in writing articles on the IT platforms including Machine learning, PowerShell, DevOps, Data Science, Artificial Intelligence, Selenium, MSBI, and so on. You can connect with her via  LinkedIn