Top 30 Data Warehouse Interview Questions and Answers in 2022

Top 30 Data Warehouse Interview Questions and Answers in 2022

If you appear for a data warehouse interview, you must be looking for Data Warehouse interview questions and answers. They are available for both freshers and experienced professionals. There are several opportunities from many reputed companies in the world. Data Warehouse has impressive market shares as per the latest research. So, you still have a great opportunity to advance your career in Data Warehouse Analytics.

A data warehouse lets us manage the collected data, which, in turn, helps provide major business insights. It is a basic Business Intelligence (BI) field, making Data Warehouse Analysis one of the most sought-after career options today. This article has compiled some of the most critical data warehouse interview questions that companies generally ask.

We have presented Data Warehouse interview questions to help you crack your interview and acquire a dream career in this area. These are 30, along with their answers, and are common. Mostly, they are asked, but as we know, a data warehouse is a big area. Therefore, you should keep yourself ready overall, i.e., questions might change as per the interviewer and apply for the job position.

1. What Is Data Warehousing?

Data warehouses are more than central data repositories that various departments within an organization can access. It is generally possible to have a physical repository or a logical repository.  In other words, data warehousing focuses on accumulating the data and seeing how it can be analyzed and accessed in the future.

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

  • Top-down approaches
  • Bottom, top approaches

William H.Inmon gives the name data warehousing. Data Warehousing is credited to him as its father. He explained data warehouses as follows during this explanation:

  • Subject-oriented
  • Integrated
  • Time-variant
  • Nonvolatile collection of data.

All of these factors support terms of making decisions.

2. What Is The Prime Difference Between A Data Warehouse System And An Operational Database?

Data warehouses and operational databases differ primarily in the following ways:

  • Data Warehouse:

Data warehouses are merely collections of all the data related to an organization, which can be used for data analysis.

A data warehouse is a complete enterprise system used to analyze and report structured and semistructured data from multiple sources, i.e., point-of-sale transactions, marketing automation, customer relationship management, etc. In addition to ad-hoc analysis, data warehouses can be used to create custom reports.

  • Operational Database:

Operational databases are those used by an organization for transactional purposes, as the name implies.

Compared to conventional databases that rely on batch processing, operational databases are oriented toward real-time operations. Real-time modifications can be made to records in operational databases. In addition to SQL, many operational database management systems use NoSQL and unstructured data.

3. Explain What Is Data Mart In Detail?

In a data warehouse environment, the data mart is an access layer to allow the data to be exported to the users. The data mart is essentially a subset of the data we already have in the data warehouse. A whole chunk of data in a data warehouse cannot be tailored to a particular team or department. In an organization, data marts provide teams with the ability to extract and customize information at a granular level.

4. What Is A Dimension Of Data Warehousing? What Are Their Primary Functions?

Dimensions can be defined as an orderly classification of measures and facts. The users can define and provide the necessary answers for their business operations using these facts and measures.

The common dimensions that are used are:

  • People
  • Products
  • Place
  • Time

Their primary functions include:

  • Filtering: It’s about choosing a smaller part of our data for analysis. It is generally temporary.
  • Labeling: tagging a group of samples is called labeling. It makes data queryable.
  • Grouping: classifying data or small buckets.

5. Define What Is A Warehouse Management System?

  • Grouping
  • Labeling

Slices and dices of data are typically based on all these factors. Data can be sliced or diced depending on whether filtered or grouped.

6. What Do You Understand By A Data Cube, And How Does It Help?

Data cubes are multidimensional databases optimized for data warehouse and OLAP applications. Online analytical processing applications are referred to as OLAP applications.

Traditional databases use a different query language than the one used in the cube. MDX is a multidimensional expression language used in data cubes.

In multidimensional cubes, data is represented by dimensions and facts.

WMS stands for the warehouse management system. All the organization’s data was stored in the previous and old-school warehouse management systems. The current warehouse management systems provide a simple storage location while providing a data analysis layer that allows the data to be analyzed from a normal to a complex level. Voice recognition and RFID (Radio Frequency Identification) capabilities have also been incorporated into the modern warehouse management system.

A few basic functionalities of a warehouse management system are the same regardless of whether data is moved or stored.

7. Define Summary Information.

Predefined aggregates are stored within summary information within a data warehouse.

Light summarized data is usually stored on disk storage and is extracted from the low levels of the details found at the current, detailed level. Data warehouse construction requires remembering what unit of time will be summarized and what components or attributes will be included in the summarized data.

8. What Are The Key Responsibilities Of A Warehouse Manager?

The important responsibilities of a warehouse manager are as follows:

  • First and foremost, he performs integrity and consistent changes
  • He continuously creates indexes and also updates where it is necessary
  • He does the grouping of data based on the requirement from the data pool
  • He has to take frequent backups
  • He is involved in preparing data models as per the requirements of project teams
  • To meet SOX guidelines, he must give due attention to maintenance and development processes.
  • He has to do constant analysis so that they can make necessary changes to see increased productivity of the data warehouse environment

9. What Is A Query Manager, And What Is It Responsible For?

The following are the responsibilities of the Query manager:

  • By using Query manager, all the operations and user queries are managed
  • The query manager’s complexity can be evaluated and defined based on the end-user access. This depends upon the facilities provided by the end users.
  • Mostly, this component is enabled with the end user access tools.
  • As the name indicates, the query manager is responsible for all the user queries generated within the environment. Data is perfectly extracted based on the queries used.

10. What Is Load Manager? What Is The Use Of Load Manager?

The following are a few points of the Load manager; they are as follows:

  • All the typical data-related operations like extraction and loading the data into the data warehouse are carried out under the load manager.  
  • In addition to simple data transformations, few operations are included where a data entry point for a data warehouse can be found.
  • Normally, the size and complexity of the composition depend upon the data warehouses, and it is constructed by considering external tools and custom build programs in a few cases.

The following are a few things you can expect from the load manager:

  • The load manager is primarily responsible for extracting the data from the source system.
  • Data uploading from the source where the data got extracted previously into the temporary data storage.
  • It can perform simple data transformation functions where the data can be stored in the data warehouse.

11. What Do You Think Is Included Under Data Warehouse, And What Are The Benefits Of Having It In An Organization?

Below-mentioned activities are involved in Data Warehouse:

  • Retrieving all the data
  • Analyzing all the data
  • Extraction of complete data, sometimes customized extraction, is also applicable
  • Loading the data
  • Managing and transforming the final data

Mentioned below are the benefits of the Data Warehouse implementation:

  • We can maintain a copy of the information from different transaction systems.
  • Using it, you can gather data from multiple systems in one place.
  • We can maintain data history with it; this is not always possible with the source data system.
  • Data quality is improved by cleansing and transforming the data to match exactly the needs of the organization
  • Assisting in reassembling raw data so that the relevant users can understand it.
  • Most importantly, it saves time
  • A high return on investment is generated since the data gathered so far is useful for business users when considering feedback from the data analysis.

12. What Is Normalization?

Normalization can also be referred to as “Database Normalization.”
A relational database is rearranged or organized by rearranging columns and tables. Reducing data redundancy and improving data integrity can be achieved by doing this activity.

Furthermore, this process simplifies the database design to enable the optimal structure. The purpose of normalization is to split the data into additional tables to incorporate it and, at the same time, make retrieval easy.

13. What Do You Understand By A Fact Table? How Many Fact Tables Will You Find In A Star Schema?

A fact table is nothing more than a table containing measurements, facts, and metrics about a business process. In a star schema, it is usually located in the center. Snowflake schemas are also known as star schemas. Fact tables typically contain two types of columns:

  • The first column comprises fact data
  • A second column has the foreign key relation
  • Only one fact table is stored in the star schema or snowflake schema. So, multiple fact tables are stored under the fact constellation schema.

14. What Is Data Marting? Explain The Different Kinds Of Costs Associated.

“Data mart” is another name for data marting. Data marts are nothing more than a way to repurpose information about a specific data set for a specific purpose.

Several types of costs associated with data marting are as follows:

  • Hardware related costs
  • Software related costs
  • Network access-related costs
  • Time costs

15. Define Data Modeling. What Is Included In Data Warehouse Modeling?

Data models are graphical representations of the data view. As a result, data modeling involves the following activities:

Following all the data warehouse and business intelligence principles and patterns when designing a data warehouse database.

Data can be easily displayed in the best graphical ways using several data modeling tools.

The data warehouse modeling includes:

  • Top-down drive approach
  • Fact tables and dimensions tables
  • A multidimensional model or often called a star schema
  • Normalization and denormalization

16. What Are Some Characteristics Of A Data Warehouse?

Data warehouses have the following characteristics:

  • In order to simplify and improve the performance of data, it can be denormalized.
  • Data from the past is stored and used whenever necessary.
  • Numerous queries are involved, and a large amount of data is retrieved based on the queries
  • It is controlled how much data is loaded
  • When it comes to data extraction, ad hoc queries and planned queries are very common.

17. What Is A Snowflake In The Data Warehouse?

Snowflaking is nothing more than dimensional modeling within a data warehouse. Several related tables are stored within this to store the dimensions. An example of a snowflake schema is a star schema.

Snowflake schema improves query performance. In data warehouses and marts, the snowflaking concept is widely used for specific queries.

Snowflaking is nothing more than dimensional modeling within a data warehouse. Several related tables are stored within this to store the dimensions. An example of a snowflake schema is a star schema. Snowflake schema improves query performance. In data warehouses and marts, the snowflaking concept is widely used for specific queries.

18. What Is OLAP, And What Is The Difference Between OLTP And OLAP

OLAP, abbreviated as Online Analytical Processing, is set to be a system that collects, manages, and processes multidimensional data for analysis and management purposes.

Below mentioned are a few differences between OLTP and OLAP:

OLTP
Data is from the original data source
Simple queries by users
Normalized small database
OLAP
Data is from various data sources
Complex queries by the system
De-normalized Large Database
Multidimensional business tasks

19. What Is ETL?

Extract, Transform, and Load is an acronym for ETL. ETL software extracts a desired subset of data from the specified data source. Following that, it transforms the data by using rules and lookup tables and converts it to the desired format. Data is loaded into the target database through the load function.

20. What Are Aggregate Tables?

Tables that contain aggregated warehouse data have been grouped according to certain dimensions. Data from aggregated tables can be retrieved more easily than from the original table.

Using this table makes the database server less burdened and queries run faster.

21. Mention Some Approaches Used By The Optimizer During The Execution Planning Stage. 

The optimizer uses two approaches when executing the plan:

  • A rule-based query is an old technique for determining a query’s results
  • The cost-based method focuses on finding the most efficient method of executing a query. Statistical information about the data must be up-to-date for this to be possible  

22. What Is The Difference Between Agglomerative Clustering And Divisive Hierarchical Clustering?

Clusters are read from top to bottom in agglomerative hierarchical clustering. A large cluster is formed when each object builds its cluster. Until the cluster is large enough, there is continuous merging. On the other hand, the divisive hierarchical clustering approach is a top-down approach. Clusters are divided using this method. Once each cluster has a single object, it is divided into parent clusters again.

23. What Are Three Types Of SCD?

N total, there are three types of SCD, and they are as follows:

  • SCD 1 – This is the new record that replaces the original record
  • SCD 2 – It’s a new record is added to the existing customer dimension table
  • SCD 3 – It’s an original data is modified to include new data

24. What Is BUS Schema?

A BUS schema consists of a suite of confirmed dimensions and standardized definitions if there is a fact table.

25. What Is A Core Dimension?

The core dimension is nothing but a Dimension table that is used as a dedicated single fact table or data mart.

26.  What Is Called Data Cleaning?

The name implies that it is a self-explanatory term— data breaching business rules, cleaning of Orphan records, Inconsistent data, and missing information in a database.

27. What Is Metadata?

Metadata is defined as data about the data. The metadata contains information like the number of columns used, fix width and limited width, ordering of fields, and data types.

28. What Are Loops In Data Warehousing?

Loops exist between tables in data warehousing. The query generation will be slower if there is a loop between the tables, and ambiguity will be introduced. To avoid loops between tables, it is recommended to keep them separate.

There may be loops between tables in DWH. Because more than one path is available, query generation will take longer if loops exist. As a result, ambiguity is also created. By creating aliases or by configuring the context, loops can be avoided.

29. What Are The Types Of Dimensional Modeling?

Following are the Types of Dimensions in the Data Warehouse:

  • Conformed Dimension
  • Outrigger Dimension
  • Shrunken Dimension
  • Role-playing Dimension
  • Dimension to Dimension Table
  • Junk Dimension
  • Degenerate Dimension
  • Swappable Dimension
  • Step Dimension

30. Do You Have Any Questions?

I want to ask a few:

  • What are the prospects in this role?
  • What are the duty timings?
  • Whom will I report?
  • Are there any chances of my promotion in the long term?
  • How many members will be working under me?

Conclusion

This article saw the most frequently asked 30 data warehouse interview questions that would help you with your next interview preparation. To learn more about data warehouse and engineering, you should learn in-depth about the relationship between data science and business.