The Difference Between Data Warehouse and Relational Database

Studies from 2021-2022 show that people generated at least two quintillion bytes of data and 600+ million Tweets daily. Furthermore, they created an average of at least 333 billion emails every 24 hours. It’s hard to imagine how much information we send and receive every day.

Obviously, there has to be space to store it. This is critical, especially for business owners and some companies. So, what can we do? There is an efficient solution, and you may already be familiar with it if your browser has the query: what is relational database with example? In fact, warehouses and databases are the most common forms of data storage. What are they? Are there any differences between them? We have the answers, so just keep reading.

Everything About Warehouse

Before we look at the differences between the solutions, let’s answer some basic questions. So, what is data warehouse and its properties? Put simply, it is a platform for gathering information from different sources for analysis and reporting. The management department in a company then makes corresponding strategies.

The basic principle of warehouses is to collect information from different systems and sources and combine it into one place, allowing you to create reports and make business decisions fast. Besides, it is important to note Online Analytical Processing (OAP), which typically occurs in data warehouses. Instead of transaction processing, this solution features complex queries for established analysis purposes.

Everything About Database

These days, databases are an indispensable tool in the arsenal of every company and business. Speaking about this solution, we often mean RDBMS, which stands for “relational database management systems” because they have been leading the market for more than a decade. What is the reason for such popularity? As one of the advantages, the RDBMS solution does provide faster data retrieval than any other alternative. It also has to do with convenience, since RDBMS distributes information in tables that group together related objects. This is what it looks like in practice.

  • Each row represents an instance of the object contained in the table.
  • Each column stores more detailed information about the object (name, address, and others).

So, databases are used in many fields, especially if it is related to business or specific companies. Among other things, databases often act as the back end of online transaction processing apps (OLTP).

Data Warehouse vs Database

Well, we’ve covered the basics of each solution. Now it’s time to find out how they differ from each other. We decided to compare them according to the main parameters listed below.


There are several types of database processing, and OLTP is one of them. It’s responsible for transaction processing and comes to assistance whenever employees need up-to-date and accurate information to cope fast with daily business requests.

While OLTP focuses on performance and daily usage, OLAP associated with warehouses is responsible for data analysis and decision-making. The latter is typically used in a business analytic field, helping non-technical executives to get answers to their questions.


The beauty of the database is that it allows you to add, change, and delete data in minutes. This is especially useful when it comes to transaction processing. Moreover, this technology processes a huge amount of data, allowing a company to record and keep every purchase.

As for data warehouses, they boast of performing a few complex queries on large multi-dimensional datasets.

Concurrent Users

This aspect can affect your business in different ways. Databases are renowned for their ability to support hundreds (sometimes thousands) of concurrent users. Thus, a large number of people can interact with a single database without impacting its performance. However, only one of them can make changes to the stored data.

Data warehouses do not have this capability because they support only a limited number of users. Separated from front-end apps, their use implies writing and executing complex queries. Since this is associated with significant computational resources, few people can use the system at the same time.

Scroll to Top