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What is Data warehousing? Type, Definition & Examples

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19th Feb, 2023
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What is Data warehousing? Type, Definition & Examples

What is Data Warehousing?

Data warehousing refers to a process where data is collected from different sources and managed well to provide insights that can help the business. The process of data warehousing involves a virtual warehouse where all the data is stored from heterogeneous sources. 

A data warehouse is considered to be the nucleus of any business intelligence platform. This is because the platform extracts all kinds of data from the warehouse. A data warehouse uses different components and technologies that help extract meaningful insights from the data. Estimated to reach $7.69 billion by 2028, data warehousing is on its way to fuel millions of businesses towards reaping data-driven benefits. 

The data warehouse does not contain the operational database of an organisation. It only stores the decision support database. Also, it works like storage, but it is not actual storage. It creates an architectural framework where users can access present and historical decision-support information. 

The entire data warehousing system serves different purposes for different businesses. Hence, it is called by different names like Decision Support System, Business Intelligence Solution and Executive Information System. 

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Now that you know what is data warehousing, it is important to understand all the aspects that govern the process and its advantages and disadvantages. 

Types of Data Warehouses

While different companies use different kinds of data warehouses, three standard data warehouses are used by most companies. Let’s have a look at some of these warehouse types: 

Enterprise Data Warehouse

An enterprise data warehouse works as a central warehouse where access is shared across the company. It acts as a support and decision-making service provider for the entire organisation. It provides a consistent method for gathering and displaying data. Additionally, it allows categorising data by subject and granting access by such divisions.

Operational Data Store

When neither OLTP nor data warehouse systems can satisfy an organisation’s reporting requirements, operational data stores, also known as ODS, are needed. The data warehouse in ODS is continuously updated. As a result, it is frequently chosen for mundane tasks like keeping employee records.

Data Mart

The data mart refers to a part of a data warehouse designed to manage a certain division, area, or business unit. Every company division has a central repository or data mart where data is kept. Periodically, the ODS stores data from the data mart. The data is subsequently transmitted from the ODS to the EDW, where it is used and stored. It acts as a warehouse subset that manages a particular business division. 

Based on the type of organisation, the data warehouse type is decided. Types of data warehouses and their concepts can be asked as technical interview questions for freshers. 

Working of a Data Warehouse

Different aspects of a data warehouse come into the picture regarding its working. It is a central repository where all the information is collected from multiple data sources. There is a transactional system in place through which data flows into the data warehouse. 

The data can be structured, unstructured or semi-structured, depending on its source. Once the data enters the warehouse, it is processed and analysed so users can utilise it with the help of different business intelligence tools. The data warehouse is also where data from multiple sources come together and become a singular database that can be used for data mining. 

The data warehouse becomes the one-stop destination for all the data the organisation can extract and analyse. It makes everything available at the fingertips of the data users. Data warehousing simplifies the process of data mining, which seeks out varying patterns in the data that could result in increased revenue and profitability.

Benefits of Data Warehouse

There are several benefits of a data warehouse. Some of these benefits include the following:

  • Business users can easily access crucial data from various sources using data warehouses.
  • Consistent data on multiple cross-functional operations is provided via a data warehouse. Ad hoc reporting and querying are also supported.
  • Data warehouses assist in integrating several data sources to lessen the strain on the production system.
  • Using a data warehouse can speed up analysis and reporting overall.
  • The user can use it more easily for reporting and analysis thanks to restructuring and integration.
  • Users can obtain crucial data from numerous sources in a single location with the help of data warehouses. As a result, it saves users time when obtaining data from various sources.

Drawbacks of Data Warehouse

While a data warehouse has several benefits, there are a few drawbacks too. These drawbacks include the following:

  • An unsuitable choice for unstructured data
  • The development and implementation of a data warehouse are time-consuming tasks.
  • Data Warehouses can easily become outdated.
  • Changes to data types and ranges, data source schema, indexes, and searches are challenging.
  • The scope of a data warehousing project will constantly expand, even with the finest project management efforts.
  • Users of warehouses may occasionally create unique business rules.
  • Organisations must invest a significant amount of their resources in training and implementation.

Examples of Data Warehousing 

Different sectors are making use of data warehousing. Some industries that make use of data warehouses and how they use them are mentioned below: 

Social Media

Utilising data-driven insights, social media platforms like Instagram, Facebook and Twitter work with data related to their users to extend better services and run optimised ads. 

Retail Chain

Data warehouses are frequently utilised in retail chains for distribution and marketing. Additionally, it aids in keeping track of products, consumer purchasing trends, promotions, and pricing policies.

Finance and Banking

Data warehousing is often utilised in the finance and banking domain to comprehend patterns obtained through frequent expenditures to present relevant offers to their customers. 

E-commerce Industry

The e-commerce sector also utilises data warehouses to assess customer behaviour and trends in hopes of presenting better customer service, inventory management, improved pricing policies and more. 

Tax Collection

Data warehouses are utilised by governments globally in order to upkeep and analyse each person’s tax data and health insurance records by responsible authorities.


In this industry, warehouses are largely used to track market trends, assess consumer trends, and analyse data patterns.


Based on customer feedback and travel habits, this industry uses warehouse services to plan and predict the locations for its advertising and promotion efforts.

Interview Questions and Answers for Freshers

Data warehousing has become an interesting conversation starter in interviews. Therefore, you should know the common technical interview questions for freshers. Let’s look at a few interview questions and answers for freshers. 

Q. What are the steps to implement a data warehouse system?

Ans. Three important steps are used to implement a data warehouse system. These three strategies can be used to access information from the data warehouse. First, you need to use the enterprise strategy to identify the current architecture tools and the data points needed. Post that comes the phased delivery phase. Here, the information is phased into different sections based on the requirements. The third stage is iterative prototyping. Here, the data warehouse is tested iteratively. 

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Q. What are some of the most commonly used data warehouse tools?

Ans. Several data warehouse tools are used in the modern day. Some of these tools include MarkLogic, Oracle and Amazon RedShift. 

Q. What is the role of a load manager in a data warehouse?

Ans. The front component is another name for the load manager. It completes all tasks necessary to extract and load data into the warehouse. These activities also involve transformations to prepare the data for the data warehouse.

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Wrapping Up!

Understanding the concept of data warehousing is very important if you are a part of any modern business using data. Several courses can help you better understand the importance and work of a data warehouse. One such course is upGrad’s Master of Science in Data Science from the University of Arizona. This online course takes you through 9 programming tools and languages. You also get access to a job opportunities portal. 

Some of the best industry experts organise several masterclasses to offer you the best of relevant in-demand skills along with the upGrad benefits such as career mentorship sessions, Python programming boot camp and more.

Book your seat now to jumpstart a successful Data Science career!

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Pavan Vadapalli

Blog Author
Director of Engineering @ upGrad. Motivated to leverage technology to solve problems. Seasoned leader for startups and fast moving orgs. Working on solving problems of scale and long term technology strategy.

Frequently Asked Questions (FAQs)

1What are the five end-user access tools?

The five end-user access tools are data reporting, query, application development, EIS, and OLAP. These end-user access tools enable teams and individuals to access the stored company data that can be leveraged to further improve company performance in the long run.

2What is the role of a Query Manager?

It executes all actions necessary for the administration of user inquiries. This data warehouse component's activities use direct queries to the necessary tables to schedule the execution of queries.

3What are the four stages of the Data Cycle?

The Data Cycle, as the name suggests, captures the entirety of its initiation to its end, which comprises four stages. These include creation, storage, usage or sharing and archival or destruction. Each stage can be leveraged wisely to reap maximum value from data.

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