Database vs Data Warehouse: Difference Between Database vs Data Warehouse [2023]

Data lies at the core of any software application or computer program. It is essential for web developers, especially those working on the back-end, to be familiar with database technologies. These systems store, organize, and process data for users to intuitively find and extract relevant information.

They come in all shapes and sizes, making it challenging for beginners to make a decision. If you are venturing into web development, it is critical to understand the difference between database and data warehouse. Having a sound knowledge of the available options helps you select the right tools and techniques to address your specific needs. 

Before we get into the database vs. data warehouse discussion, let us first describe these technologies’ purpose in implementing web development projects. 

What is a Database?

Any collection of data that represents related elements of the real-world can be termed as a database. It forms a critical building block of the application and is organized for specific tasks, such as storage, accessibility, and retrieval. Typically, the structured information is stored electronically in a computer and controlled by a database management system (DBMS)

Why Use A Database?

When it comes to database vs data warehouse, here are some reasons why you should use a database. 

  • A database provides access to and security over data.
  • It provides a range of methods for storing and retrieving data.
  • A database effectively manages the demands of various applications using the same data.
  • A database enables concurrent data access so that only one person at a time can view the same data.

Examples Of Some Popular Databases

  • MongoDB
  • Oracle
  • MySQL
  • Redis
  • Microsoft SQL Server
  • PostgreSQL
  • IBM Db2
  • Elasticsearch
  • SQLite
  • Microsoft Access

What is a Data Warehouse?

A warehouse is a type of database that introduces analytics into data usage in an organization. It integrates copies of historical and commutative data from disparate sources and makes it available for analysis and reporting processes. Therefore, data warehouses enable better decision-making through research, evaluation, and forecasting.

Why Use A Data Warehouse?

When evaluating a database and data warehouse, consider these reasons for choosing a data warehouse.

  • Business users can access crucial data from various sources conveniently through data warehouses.
  • It offers consistent data on different cross-functional operations.
  • Helps you combine data from many sources to reduce the production system’s strain.
  • TAT (total turnaround time) for analysis and reporting can be decreased using a data warehouse.
  • Data warehouses save users time by enabling them to access crucial data from various sources in one location rather than retrieving it from each source individually. The cloud’s data is very simple to access.
  • Users and stakeholders can underestimate the calibre of the data in the source systems. Reports from a data warehouse are more accurate.

Examples Of Some Popular Data Warehouses

  • Google BigQuery
  • Amazon Redshift
  • Microsoft Azure Synapse
  • IBM Db2 Warehouse
  • Oracle Autonomous Data Warehouse
  • Snowflake
  • Teradata Vantage

Database vs. Data Warehouse

Here is an overview of the main difference between database and data warehouse.

Database Data Warehouse
Online Transaction Processing (OLTP) method Online Analytical Processing (OLTP) method
Deletes, inserts and updates several short online transactions quickly. Rapidly analyzes massive volumes of data and provides different viewpoints.
Simple transactional queries. It helps carry out fundamental operations for your venture. Complex queries for in-depth analysis. Data warehouse simplifies to analyze ventures.
The data in the database is already updated. It stores historical data and current data. There is a possibility that the data is out of date.
Limited to a single data source. All data sources from all business functions.
The data in databases is available in real time. The data in the data warehouses are refreshed from source systems as needed.
For designing, ER modelling technique is used. The data modelling approach is used for the designing of data warehouses.
Tables and joins of a database are complex. Tables and joins are simple in a data warehouse as they are denormalized.
Flexible or rigid schema varies with the type of database. Fixed and pre-defined schema definition for ingest.
Some applications are banking, telecommunications, airline, retail chain, insurance, and health. Some applications are HR management, sales and production, banking, airlines, finance, telecommunication and manufacturing.

The primary difference between database and data warehouse is that the former is designed to record data while the latter assists in analyzing it. In a database, data collection is more application-oriented, whereas a data warehouse contains subject-based information. As for data processing, Online Transactional Processing or the OLTP system processes requests in a database. On the contrary, Online Analytical Processing or the OLAP category of tools dominates data warehouses. 

Furthermore, the two database technologies differ in their coding and development aspects. While Entity-Relationship models are used to create a database, data modeling techniques are prominently applied to design a data warehouse. Moreover, database tables and joins are complicated to implement as they normalized, unlike in data warehouses.

The two data collections also vary in terms of query and storage types. Simple transactional queries are used in the database, but the data warehouse analytics requires complex queries. Finally, the database system’s information is more detailed than the summarized data in the warehouse. 

Learn about: Top 30 Data Warehouse Interview Questions & Answers

Pros and cons of using database


  • A digital database eliminates redundancy and allows for multiple views. 
  • It follows the ACID compliance, which stands for Atomicity, Consistency, Isolation, Durability.
  • It facilitates program-data independence, thus retaining the integrity of the data.
  • It enables concurrent data sharing and multi-user transaction processing.
  • DBMS can balance the requirements of several applications with the same set of data.

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  • The implementation costs of a DBMS software and hardware can be high, especially for startups.
  • You may have to train the staff in using the DBMS as people from non-technical backgrounds may not be familiar with it.
  • Although field calculations and grouping operations can be performed in a DBMS, it has limited capability of handling complex calculations.
  • The proposed database solution may not be compatible with the existing systems of the organization.
  • There is a possibility of the owners losing the information stored in the database. So, security and privacy issues have to be taken into account. 

Pros and cons of data warehouse


  • Warehousing offers a fast way of provisioning thematic information to decision-makers.
  • Warehousing brings down the total turnaround time or TAT for analysis and reporting purposes.
  • It collates useful information from different sources in one place, helping users in executing cross-functional activities
  • It reduces operational inefficiencies and enhances the quality of customer relationship management systems.
  • It contributes to improved performance by separating the transactional databases from analytics processing. 
  • Warehousing provides non-volatility to the data as it does not get erased upon entering new information.


  • Adding new data sources in a data warehouse can be a cumbersome, time-consuming task.
  • Warehousing is a high-maintenance setup, requiring significant effort in extracting, loading, and cleaning data.
  • The average user may take a while to comprehend how to use a data warehouse. End-users have to be trained in data mining and other techniques.
  • Data warehousing is an evolving subject area, and its scope is continuously expanding to incorporate new workplace environments.

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Factors Influencing the Final Choice

Beginners in the field of web development can find it tricky to pick the right solution. And in such situations, knowing about each alternative’s features and pros and cons can prove immensely beneficial. To decide who wins the database vs. data warehouse debate, we should also look at the use cases for each option. We have summarized some examples for you below. 


Banking and financial institutions use DBMS to organize customer information and account related activities (such as deposits, payments, loans, credit card use, and so on). Data warehouses are typically used to manage on-the-desk resources. IT teams in the banking sector can take care of their day-to-day operations more efficiently and provide better customer service with warehousing. 


Insurance is a data-heavy industry capable of leveraging business intelligence. Data warehousing approaches help in identifying consumption patterns and keeping a tab on customer trends and market movements. 


Data warehousing opportunities in healthcare entail strategic decision-making, which involves predicting outcomes and creating treatment reports. With the advancements in big data and machine learning, warehousing can also help forecast diseases or particular ailments in patients. 


DBMS helps airline companies keep a record of booking and reservations, assisting in schedule management. As for other operations route analysis, crew assignment, frequent flyer discount schemes, etc., data warehousing is the ideal choice. 

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Supply chain management in manufacturing has revolutionized with the utilization of databases. A DBMS can be a boon for many activities, from tracking production and inventory status to logistics management items. On the other hand, a data warehouse is a valuable asset in situations where the enterprise wants to conduct advanced analytics or apply optimization techniques.

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A simple customer database includes the name, address, contact information, email of individuals who have purchased from you. Conversely, a data warehouse is an integrated and centralized solution that can offer you a peek into customers’ buying behavior. You can use such insights to determine things like promotion mix and pricing policies. 


A database consists of details like call records, monthly bills, current balance, etc. By contrast, warehousing compiles information from multiple sources, allowing telecom companies to make better sales and distribution decisions. 


DBMS helps in systemizing record-keeping for HR departments and educational institutions. Organizations use it to manage data related to employees’ salaries and deductions and also to generate payslips. University administrations maintain a database of the student registration details, course enrolments, results, fees, etc. 

Also Read: DBMS vs. RDBMS: Difference Between DBMS & RDBMS

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Wrapping up

From the above applications, you would have observed that there is no one-size-fits-all or cure-all answer. Thus, it is best to evaluate what works best for you. Now that you have learned the difference between database and data warehouse, you would be in a position to make an informed choice. 

If you are curious to learn about data science, check out IIIT-B & upGrad’s Executive PG Program in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.

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