Top Data Warehouse Interview Questions and Answers

Prepare for your data warehouse interview with confidence using our comprehensive collection of interview questions and expertly crafted answers. Ace your interview and secure your dream job in data warehousing.

Looking for data warehouse interview questions and answers to prepare for your upcoming data warehouse interview? You have landed on the right page! This guide covers frequently asked data warehouse interview questions and answers for fresher and experienced candidates. Through this data warehouse interview questions exclusive guide, you will be able to crack interview questions like what is a data warehouse, how a database is different from data warehousing, what is OLTP and OLAP, cloud-based data warehouses, Kimball and Inmon data warehouse designs, and more.

To learn about data science, read our blog – What is data science?

This blog is divided into the following categories:

• Data Warehouse Interview Questions for Freshers
• Data Warehouse Interview Questions for Experienced Candidates

Let’s Begin!

Data Warehouse Interview Questions for Freshers

Listed below are the Data Warehouse Interview Questions for Freshers. Read on –

Q1. Define Data Warehousing in simple words.

Ans. This is one of the commonly asked data warehouse interview questions, which you can answer by saying –

Data warehousing can be called a repository of data, which helps management teams drive apt business decisions.

It is a process that involves data collection and data management, which helps provide significant insights to businesses. Being the core of Business Intelligence (BI), data warehouse analysis is one of the most sought-after careers in 2021. Today, the data warehouse is essential for almost every industry, including verticals like healthcare, IT, automation, retail, logistics, and government agencies.

You may also be interested in exploring: 

Popular Data Science Basics Online Courses & Certifications	Popular Machine Learning Online Courses & Certifications
Popular Statistics for Data Science Online Courses & Certifications	Popular Python for data science Online Courses & Certifications

Q2. What are the different stages of Data warehousing?

Ans. There are four different stages of data warehousing, including:
1. Offline Operational Database
2. Offline Data Warehouse
3. Real-Time Data Warehouse
4. Integrated Data Warehouse

Q3. Are databases and data warehousing the same thing?

Ans. The database is a way of storing information in an organized format, represented as a table, columns, and rows. Companies use dedicated database management software (DBMS) to store crucial data.

While data warehousing also helps store data, it is used to store a large chunk of data, allowing users to use data for complex queries. For this, users take the help of Online Analytical Processing (OLAP).

Though databases and data warehouses are relational data systems, they serve different purposes. Below are some key differences:

Database	Data Warehouse
Helps in data recording	Helps in data analyzing
Uses Online Transactional Processing  (OLTP)	Uses Online Analytical Processing (OLAP)
Table are normalized and hence complex to use	Table are denormalized and thus easy to use
Application-oriented	Subject-oriented
Stores data from a single application	Stores data from multiple applications
Real-time data availability	Data refreshed from the source system as per requirements
Uses ER modelling technique	Uses data modelling technique
Used in industries like banking, airlines, universities, sales, etc.	Used in healthcare, insurance, retail chains, telecommunications, etc.

Check out the top Database and SQL courses. 

Q4. How is OLTP different from OLAP?

Ans. OLTP stands for Online Transactional Processing, which deals with current data and is characterized by short write transactions. The main objective of OLTP is to record all the current updates, insertions, and deletions, and thus, it is less time-consuming and easy to maintain. Also, OLTP acts as a source of data for OLAP.

The focus of OLAP is to store historical data that OLTP has processed. OLAP helps in data analysis and supports reaching meaningful interpretations. Some of the noticeable differences between OLTP and OLAP are:

OLTP	OLAP
Online transaction system	Data retrieval and analysis system
Helps in data insertion, update, and deletion	Helps in deriving multi-dimensional data for analyzing
Short and frequent transactions	Long and less frequent transactions
Less complex queries	More complex queries
Data integrity is a concern	The possibilities of Data integrity is dependent on OLTP

Q5. What are some benefits of cloud-based data warehouses?

Ans. Below are the top reasons why companies prefer to use cloud-based data warehouses over traditionally used on-site warehouses –

Flexible and dynamic infrastructure – You will have a range of infrastructure available for your perusal and can explore further to find the optimal infrastructure for your business.

Secure data – Cloud data is much more secure and low on cost. Cloud encryption services like multi-factor authentication make data transportation even more secure.

Increased data usage – Cloud data warehousing provides flexibility and agility. With a cloud data warehouse, it is easier to control the cluster size, CPU, and RAM to fit the requirements of the unique projects.

Business capabilities – Cloud data warehousing offers better business capabilities like scalability, availability, disaster recovery, extensibility, accessibility, flexibility, execution capacity, security, etc.

Optimized for data analytics – The cloud data warehouse is optimized for data analytics because it uses Massively Parallel Processing (MPP) and columnar storage, which are known for offering better performance and helping in executing complex queries

Check Out Our Data Science Courses

Lower cost of ownership – With a very low cost of capital, flexibility, and infrastructure capabilities, the risks of running a data warehouse project are reduced, and the chances of success are increased, along with the recused ownership costs.

Explore courses related to Data Warehousing: 

Popular Business Data Mining Courses	Popular Data Visualization Courses
Top Business Intelligence Tools Courses	Top Data Analysis Courses

Q6. What are the advantages and disadvantages of the Inmon approach?

Ans. Below are some advantages and disadvantages of top-down or Inmon design:

Advantages of Inmon Design	Disadvantages of Inmon Design
Easy to maintain, and though the initial cost is high, subsequently, the project development cost is low.	Represents a large chunk of data; thus cost of implementing the design is high
Offers consistent dimensional views of data across all Data Marts	Requires more time for initial set-up
A highly robust approach toward frequent business changes	Represents substantial projects, and hence it is complex

Q7. What are the advantages and disadvantages of the Kimball approach?

Ans. Below are some advantages and disadvantages of bottom-up or Kimball design:

Advantages of Kimball Design	Disadvantages of Kimball Design
Contains consistent Data Marts which are easy to deliver	The overall cost is high
Data Marts showcase reporting capabilities	Data Mart and data warehouse positions are differentiated
Initial setup is quick and easy; hence it is easy to accommodate new business units by merely creating new Data Marts and clubbing it with other data warehouses.	At times difficult to maintain

Q8. Which are the different types of data warehousing?

Ans. There are three types of data warehousing:

1. Enterprise Data Warehouse

It merges organizational data from its different functional areas in a centralized manner. It helps with data extracting and transformation and offers a detailed overview of any particular object in the data model.

2. Operational Data Store

It gives the option to produce the data from the database instantly and supports business operations by integrating contrast data from multiple sources.

3. Data Mart

Data Mart stores data from a particular functional area, comprising a subset of data saved in the data warehouse. It helps the analyst in swiftly analyse the data by shrinking the volume of a large chunk of data.

Explore the concept of Business Analytics. 

Q9. Name 3 types of Data marts.

Ans. Below are the 3 different types of Data Marts:

1. Dependent – It sources organizational data from a single data warehouse and helps develop more Data Marts.
2. Independent – Here, no data depends on a central or enterprise data warehouse, and data can be used separately for an independent analysis.
3. Hybrid – It helps in ad hoc integration and is used when a data warehouse comprises inputs from different sources.

Q10. What is data warehouse architecture?

Ans. Conceptualized with a relational database management system (RDBMS), data warehouse architecture is a central repository for informational data. Here, the central repository includes several key components that make the environment operative, compliant, and accessible to operational systems.

Must Read – What is Data Visualization?

Q11. What is the three-tier architecture of a data warehouse?

Ans. Below is the three-tier data warehouse architecture:

1. Bottom Tier

It represents the data warehouse database server, also known as the relational database system. It uses backend tools and utilities to feed data and perform functions like – Extract, Clean, Load, and Refresh.

2. Middle Tier

It represents the OLAP Server, a form of the extended relational database management system. It is known to implement multidimensional data and operations.

3. Top Tier

It factors the front-end client layer and holds query, analysis, and data mining tools.

Q12. What are the different stages of data warehouse decision support evolution?

Ans. Below are the 5 stages involved in data warehouse decision support evolution:

1. Report
2. Analyze
3. Predict
4. Operationalize
5. Active warehousing

Q13. Name the components of data warehousing.

Ans. Below are the 5 components of data warehousing:

1. Data Warehouse Database
2. Sourcing, Acquisition, Clean-up, and Transformation Tools (ETL)
3. Metadata
4. Query Tools
5. Data Warehouse Bus Architecture

Do Read –  Top 6 Industries Hiring Data Scientists in 2021

Q14. Name some of the popular data warehouse tools.

Ans. Below is the list of popular query tools:

Tools	Availability
Amazon Redshift	Licensed
Teradata	Licensed
Oracle 12c	Licensed
Informatica	Licensed
IBM Infosphere	Licensed
ParAccel (acquired by Actian)	Open Source
Ab Initio Software	Licensed
Cloudera	Open Source

Also explore: 

• Top paid and free online courses by Coursera
• Top online edX courses
• Popular Udemy courses

Data Warehouse Interview Questions for Experienced Candidates

Q15. What do you know about Amazon Redshift’s architecture?

Ans. Amazon Redshift, based on PostgreSQL, is the most popular cloud service offered by Amazon Web Services. This tool is popularly used for handling Petabyte-scale data. Its unique features help the analyst to query data in seconds. With almost negligible cost, Redshift is easy to set up and maintain.

Redshift can be integrated with other BI and analytical tools and works with Extract, Transform, and Load (ETL) tools.

Below are some features of Redshift:

1. Columnar storage and MPP processing
2. Compression (column-level operation)
3. Management and Security
4. Data Types
5. Updates and Upserts

Explore popular Data Science Courses

Q16. State something about real-time data warehousing.

Ans. Real-time data warehousing is a concept, which reflects the real-time state of the warehouse by capturing the data as soon as it occurs. It has low latency data, which is fast, scalable, and simple to use.

Q17. What are the benefits of real-time data warehousing?

Ans. Below are some benefits of using real-time data warehousing:

• Eases decision making
• Resolves the problem of ideal data load
• Ensures quick recovery and permits more rapid interventions
• Eliminates batch window
• Easy to optimize by running transformations in the database

Q18. What should you avoid when planning to construct a real-time data warehouse?

Ans. One must avoid mistakes like:

• Not focusing on data integrity when constructing real-time data
• Overlooking traditional OLTP systems
• Not initiating business process changes in real-time data warehousing

Q19. What do you mean by SCD?

Ans. SCD stands for a slowly changing dimension, which is used to store and manage historical data. It is among the most critical tasks that support tracking dimension record history.

Understand the concept of Big Data Analytics. 

Q20. Which are the three types of SCD?

Ans. Below are the three types of slowly changing dimensions:

• 1st Layer – SCD 1 – Overwriting current record with the new record
• 2nd Layer – SCD 2 – Creating another dimension record to an existing customer dimension table
• 3rd Layer – SCD 3 – Creating a current value field to include new data

Q21. Define Schema in data warehousing.

Ans. The following table defines different schemas in data warehousing:

Schema	Description
Bus Schema	It works on top-down planning concepts and contains a set of tightly integrated data marts directly linked with conformed dimensions and fact tables.
Star Schema	Each dimension is represented with only one dimension table, which consists of a set of attributes.
Snowflake Schema	Some dimensional tables are normalized, which splits the data into additional tables.

 Check out the top Data Exploration courses. 

Q22. State the difference between Star and Snowflake schema.

Ans. Below is the list of differences between star schema and snowflake schema:

Star Schema	Snowflake Schema
Dimension hierarchy is stored in a dimensional table	Hierarchy is divided into multiple tables
Dimension table surrounded fact tables	Other dimension tables further surround dimension tables
A single join reflects the relation between fact and dimension table	Requires multiple joins to establish the relationship
DB design is simple	DB design is complex
Data redundancy is possible	Data redundancy is hardly possible
Fast cube processing	Cube processing is a bit slow
Denormalized Data structure	Normalized Data Structure

Q23. Define a Galaxy schema.

Ans. Galaxy schema, also known as Fact Constellation Schema, contains two fact tables and dimensional tables. In other words, it can be called a combination of stars.

Become Machine Learning Expert Now>>

Q24. What are the types of fact tables?

Ans. In the dimensional model, the fact table is the primary table, which contains facts and foreign keys to the dimension table. It is used for measurement in the business process. The fact table has three different types:

Fact Table Types	Description
Additive	All dimensions must have measures
Semi-Additive	Measures must be added to only some dimensions and not all
Non-Additive	Only contains some fundamental unit of measurement

Q25. What are the types of dimension tables?

Ans. Joined via a foreign key, a dimension table includes the dimension of facts. It is also known as denormalized tables that offer descriptive characteristics of facts. Below are the types of dimension tables:

• Conformed dimensions
• Outrigger dimensions
• Shrunken rollup dimensions
• Dimension-to-dimension table joins
• Junk dimensions

Must Read: Guide for Starting a Career in Data Science

Q26. Give the steps to start and shut down the database.

Ans. Below are the steps to start a database:

• Start an instance
• Mount the database
• Open the database

Below are the steps to shut down a database:

• Close the database
• Dismount the database
• Shutdown the instance

Q27. Define the surrogate key.

Ans. Surrogate key functions as a substitute for the natural primary key.

Q28. What do you mean by virtual data warehousing?

Ans. It is a collective view of the finished data and does not include historical data. The virtual data warehouse’s main objective is to help make analytical decisions and translate raw data into a more presentable format. Along with this, it also offers a semantic map.

Read More – What is Python?

Q29. Define XMLA.

Ans. XMLA or XML for Analysis is the Simple Object Access Protocol, used as a standard for obtaining data in OLAP.

Q30. Differentiate between View and Materialized View.

Ans. Below table highlights the difference between the view and materialized view:

View	Materialized View
Provides tail raid data to access data from its table	Contains pre-calculated data
Does not occupy space due to its logical structure	Occupy physical data space
All changes are affected in the corresponding tables	No changes are affected in similar tables

Check out the top Data Science Interview Questions and Answers

Q31. When do you use bteqexport?

Ans. Whenever the total number of rows is less than half a million, bteqexport is used.

Q32. When do you use fastexport?

Ans. “fastexport” is used when the total number of rows is more than half a million.

Q33. Name the primary functions of dimensions.

Ans. The primary functions of the dimensions are:

• Filtering
• Grouping
• Labeling

Q34. What were your key job responsibilities in the previous company as a data warehouse manager?

Ans. Some of my prime responsibilities are:

• Work on creating data warehouse process models
• Verify the integrity of warehouse data and ensure consistent changes
• Implement data extraction procedures
• Maintain data standards
• Handle data-related troubleshooting
• Use different computer languages and methods to perform data analysis
• Implement metadata processes
• Review data designs, codes, and test plans
• Use database management software like Apache, MongoDB, and Oracle to perform data warehousing functions. smoothly

Q35. Which data warehousing skills did you master?

Ans. In the data warehousing interview, you can discuss your critical technical skills. You can say – some of my strengths are:

• Enterprise system management software
• Apache Avro
• Human resource management software HRMS
• Data mining software like Rapid-I RapidMiner, SAP NetWeaver Business Warehouse
• CRM software
• Data analysis
• MS office

Q36. What is Metadata?

Ans. Metadata consists of information that characterizes data, describing the content, quality, conditions, history, availability, and other data characteristics. Metadata allows a person to locate and understand data, including information required to determine what data sets for a particular geographic location, information necessary to determine if a data set is appropriate for specific purposes, information required to retrieve or get an already identified set of data and the information required to process and use it.

Q37. What are the benefits of metadata?

Ans. Primary benefits of Metadata include – 

Facilitates search and analysis – Metadata helps to find data more easily and allows data analysis from the source itself, favouring self-documentation, transformation, and reporting, among other functions.

Improves data governance – Managing metadata in a standardized environment ensures good data governance, contributing to the program’s success.

Integration aid: In hybrid integration, metadata is key. Using a centralized metadata repository for shared use between IT and business users facilitates governance and the application of best practices. Metadata is very useful in hybrid structures as it improves data management in an integrated way.

Facilitates standardization – Metadata eliminates errors and improves the quality of metadata throughout its life cycle, along with a complete vision of said cycle, from start to end.

Improves reporting – Metadata management improves reporting, delivering them safely and reliably. This is due to the ease of intervention that makes the processes of higher quality.

Interoperability – Metadata facilitates interoperability since metadata standards have been defined, and there are shared protocols for exchanging this information. Protocols such as Z39.50 or CSW have helped in simultaneous searches for data in distributed systems.

Q38. How does a data cube help?

Ans. Data cubes help us represent data in multiple dimensions. Data cubes are defined by dimensions and facts and are created from a subset of attributes in the database.

Do Explore – Deep Learning Online Courses & Certifications

Q39. What is a load manager?

Ans. A load manager performs the necessary operations to extract and load the process. The size and complexity of the load manager vary between specific solutions from the data warehouse to the data warehouse.

The load manager helps to pull data from the source system and quickly load the extracted data into the temporary data warehouse. It also performs simple transformations in a structure similar to the data warehouse. It is supported by underlying DBMS and allows client programs to generate SQL to be executed at a server.  

Q40. What do you mean by data extraction?

Ans. Data extraction is extracting data captured within semi-structured and unstructured sources such as emails, PDF documents, PDF forms, text files, social media, barcodes, and images. Data extraction is done with the help of enterprise-grade data extraction tools, making incoming business data from unstructured or semi-structured sources usable for data analysis and reporting.

Structured formats can be processed directly in most business intelligence tools after some debugging. However, an ideal PDF Scraping Tool should also support common unstructured formats, including DOC, DOCX, PDF, TXT, and RTF, allowing businesses to use all the information they receive.

Q41. What is the difference between Data Extraction Vs Data Mining?

Ans. The data extraction process deals with extracting important information from sources such as emails, PDF documents, forms, text files, social media, barcodes, and images with the help of content extraction tools. On the other hand, data mining is a process used to look for patterns, anomalies, and correlations in the data. Data mining tools allow users to analyze data from multiple perspectives to identify hidden patterns in large data sets.

Q42. Name essential approaches to data warehouse design.

Ans. There are two data warehouse design approaches, Kimball and Inmon.

Inmon approach or top-down was proposed by Mr Bill Inmon, the Father of data warehousing. In this approach, first, it is recommended to prepare a data warehouse, and then Data Marts are created. Through this strategy, the data warehouse becomes the central point of the Corporate Information Factory (CIF), which acts as a logical framework for BI.

Kimball approach, also known as a bottom-up approach, suggests creating Data Mart first and later integrating it to a more massive data warehouse to complete a data warehouse. This integration of Data Mart is known as a data warehouse bus (BUS) architecture.

Learn more about Data Analysis. 

Q43. What Does Junk Dimension Mean?

Ans. Junk dimensions are the single dimensions used to store the tiny dimensions known as junk attributes. The junk attributes are text attributes and flags transmitted into a different sub-domain called the junk dimension.

We can consider the example of car colours and car bodies. As we can see, these attitudes are limited in number, and if created as single dimensions, the dimensions would be limited to a single attribute. To eliminate these small dimensions, we create a single “junk” dimension that cross-joins all possible attributes into a single dimension for use in the fact table.

Q44. What is data replication?

Ans. Data replication is copying and storing business data at multiple locations. The replication process can be single or continuous, depending on the organization’s requirements.

The primary purpose of data replication is to improve the availability and accessibility of the data and the robustness and consistency of the system. Data replication works by copying data from one location to another, for example, between two local hosts in the same location or in different locations.  

Read More – What is Deep Learning?

Q45. What are the benefits of database replication?

Ans. The main benefits of database replication are –

Increased reliability – By replicating the database across multiple servers, you ensure the data will be available even in a serious hardware failure. The distributed database management system can route affected users to another of the available nodes.

Performance improvement – Since the data is distributed on different servers, the multiple accesses do not saturate the servers. This is especially important for applications with thousands or hundreds of thousands of simultaneous requests, leading to increased performance.

Improved data security – In a traditional transactional system, all updates to a database are saved on the same disk. Data security then depends on the backup strategy implemented on that server. The security of data increases with database replication since updates are written on multiple servers, such as disks, several power supplies, CPUs, etc.

Data analysis support – Typically, data-driven companies duplicate data from numerous sources in their data warehouses, such as data warehouses or data lakes. This makes it easier for the analytics team dispersed in multiple locations to undertake shared business intelligence projects.

Q46. What is dimensional modelling?

Ans. Dimensional modelling is a way of bringing data to how these will be converted into useful information for business users. The ultimate goal is that they can intuitively and quickly find the information they need.

The application of the dimensional model takes place in the logical design phase, which allows the translation of the resulting schema from the conceptual design to the logical plane.  This technique is widely accepted and is often chosen as the preferred one to represent analytical data because it simultaneously meets the following requirements:

• Arrange and structure the data in a way that is understandable to the business user
• Generates high performance in searches from the reporting layer

Q47. What is ODS?

Ans. ODS or Operational Data Store, is a real-time operational data repository.

Q48. What is ETL?

ETL is the short form of Extract, Transform and Load. ETL is software that reads data from the given data sources and extracts the required information. It then transforms data using rules and lookup tables, making it user-friendly and ready to use.

Q49. What is VLDB?

VLDB or Very Large Database is a big-size database with more than one terabyte size. VLDBs are decision support systems catering to a large number of users.

Q50. What are factless fact tables?

Ans. As the name suggests, factless tables are tables with no numeric fact column.

We hope these data warehouse interview questions and answers were helpful.