Data Warehouse vs Database: 9 Important Differences

Preetipadma Khandavilli • Last Modified: January 20th, 2023

Data Warehouse vs Database | Hevo Data

Data is being leveraged by companies to optimize almost all business processes today. However, traditional databases do not cater to the varying needs of Data Analysis as it requires access to Big Data for visualization and reporting. Dated databases provide superior performance while handling small transactional data at speed. But do not support analytical workflows, which are crucial for organizations to stay ahead in the competitive world.

This is where Data Warehouses become prominent among data-driven organizations for streamlining Data Analysis. As a result, companies are implementing different types of data storing infrastructure — Databases and Data Warehouses — for varying needs. Although both are used for storing data, there are several differences in functionality when comparing data warehouse vs database. In this primer, you shall learn what each is and the key differences. 

Table of Contents


  • Working knowledge of OLAP.
  • Familiarity with OLAP systems.

What is Database?

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A database is an organized collection of related data that makes logical sense. It allows easier search, retrieval, manipulation, and analysis of data. Databases generally consist of information arranged in rows and columns of tables, which help in easily reading and writing during transactions.

A database normally follows the ACID (Atomicity, Consistency, Isolation, and Durability) compliance model that helps it to avoid duplicate processing and other errors. This also ensures reliability and higher integrity after updating information.

Today, there are numerous database software that is multi-layered, use different query languages, and support various storage formats like XML. Some of the common databases used every day are SQL and NoSQL databases. Popular databases include Oracle, MySQL, PostgreSQL, Apache Cassandra, and MongoDB.

The different types of databases are as follows:

  • Relational databases: These are some of the most popular and widely used forms of databases. They use SQL commands to write, write, and query information into the database. Here, the data is organized in tables where each row is marked by a unique identifier called a key. While columns in the table hold an attribute of data, every row in the table is a record, and each record represents a value for an attribute. 
  • Non-Relational Database: Also known as NoSQL databases. They have become more popular in recent years owing to their flexibility and adaptability to changing schemas. Non-relational databases can be either key-value, document-based, or graph-based for handling unstructured data.

For applications that require scalable, high-performance, and highly functional databases, NoSQL is the most suitable option.

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What is Data Warehouse?

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A Data Warehouse is a repository that stores historical and commutative data from single or multiple sources. It centralizes and consolidates large amounts of data from disparate sources to facilitate Data Analysis, Data Mining, Artificial Intelligence, and Machine Learning.

Another definition of a Data Warehouse refers to an advanced form of database designed for query and analysis instead of transaction processing. While Data Warehouses mainly store structured data, today’s modern Data Warehouse supports semi-structured data for insights delivery.

Companies that offer Data Warehouse services are Amazon Redshift, Google BigQuery, Oracle, and IBM.

The different types of Data Warehouses are as follows:

Broadly, two types of Data Warehouses have made the cut in the IT industry today. Some of these are:

  • On-Premise Data Warehouse: This relies upon on-premise IT resources such as servers and engineering bandwidth to deliver Data Warehouse functions. 
  • Cloud Data Warehouse: This is explicitly built to run in the cloud. This type of Data Warehouse is offered to customers as a managed service.

Cloud Data Warehouses save the cost needed for the initial setup of an On-Premise Data Warehouse. But, the latter offers better security and privacy as it does not send sensitive user data over the internet. However, the cloud Data Warehouse has effortless scalability, ease of accessibility, and fewer maintenance costs, compared to the on-premise Data Warehouse.

Data Warehousing Model
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Apart from this you also have Data Marts, a subdivision of a Data Warehouse designed to focus on a single functional need of an organization or business. In other words, a Data Mart holds data for specific analytical workflows. For instance, an accounting department can use a Dart Mart built specifically for them, or a marketing company can use a Marketing Dart Mart. Data Marts offers enhanced data integrity and security.

A Summarised Comparison of Data Warehouse vs Database

While these two data storage elements may seem similar, they offer very different capabilities. Here is a brief breakdown of the differences:

Data WarehouseDatabase
Designed to analyze dataDesigned to record data
Stores summarized dataStores detailed data
Uses Online Analytical Processing OLAPUses Online Transactional Processing OLTP
Allows users to analyze business dataPerforms fundamental business operations and transactions
Data must be refreshed when neededData is available in real-time
Subject-oriented data collectionApplication-oriented data collection
Draws data from a range of other applicationsLimited to a single application

The Key Differences Between Databases and Data Warehouses

Here are the key differences between Data Warehouses and Databases:

Data Warehouse vs Database: Sourcing

The key difference between a database and a Data Warehouse is that a database works more efficiently when information is transferred from a single source. But a Data Warehouse must extract information from multiple sources to provide a consolidated and comprehensive Data Analysis for enhanced decision-making. 

Data Warehouse vs Database: Processing Types

Databases employ OLTP (Online Transactional Processing) to delete, insert, replace and update large numbers of short online transactions quickly. In contrast, Data Warehouses use OLAP (Online Analytical Processing) to support analyses of a colossal amount of data rapidly.

By optimizing transactional speed, OLTP allows quick response to user requests. However, OLAP systems are relatively slow as it allows analysts to query big data for multiple objectives like analysis, reporting, and more.

Data Warehouse vs Database: Design

Databases use Entity-Relationship (ER) model-based design. This model usually requires a smaller space. In contrast, Data Warehouses use data modeling techniques. One such design is the Dimensional Model Design. Dimensional modeling is a data modeling technique that uses dimensions, attributes, and facts to store data in a Data Warehouse. This improves the efficiency of queries by reducing the number of tables and relations between them.

Data Warehouse vs Database: Nature of Data

Given their respective nature, a database stores current data while a Data Warehouse stores both current and historical data. This historical data can be either months or years old and proves handy for an in-depth analysis. Data Warehouses help summarize data and make it ready for Data Analysis. 

Data Warehouse vs Database: Normalization

Historical data can lead to redundancy, which is a no-go for normalization. Databases use a static schema that results in high data normalization. This helps users avoid data redundancy and organize data as per predetermined attributes.

On the other hand, Data Warehouses run on denormalized or partially denormalized schemas to optimize query performance. Denormalized schema is used for faster analytical response time. For instance, Star schema consists of one fact table, which can be joined to several denormalized dimension tables. 

Data Warehouse vs Database: Scope of Use

The use of a database is often restricted to a single application, as a result, it can process one request at a time. This is because databases are an assortment of application-specific data, unlike a Data Warehouse that houses several categories of data. 

In comparison, Data Warehouses accommodate data for any given number of applications ranging from machine learning to reporting. In simpler words, Data Warehouse usage can involve multiple applications. This is because it possesses subject-based information.  

Data Warehouse vs Database: Accessibility

Databases are generally designed to handle read-write operations for single-point transactions. Therefore, to extract data in a database system, users must call high volumes of small read functions for efficient transaction processing. This can sometimes result in a lengthy and excruciating procedure. 

A Data Warehouse is designed in a manner that separates it from front-end applications. It receives lesser I/O requests and delivers more data throughput compared to that of a database. As a result, Data Warehouses provide organizations with rapid access to data retrieval for analysis. 

Data Warehouse vs Database: Ease of Analysis

Due to the tabular organization of databases, carrying out analytical queries will be challenging. In comparison, Data Warehouses allow easy and endless possibilities for performing analytical queries through data transformation techniques. As Data Warehouses use fewer tables and a simpler structure, the total turnaround time (TAT) for analysis and reporting is reduced significantly.

Data Warehouse vs Database: Downtime

The transactional nature of databases demands it to be available and accessible at all times, i.e., 99.9% of uptime as per SLA. But, all databases require regular data backups to function. So, a downtime emergency would result in an expensive affair, lawsuits, and hamper business operations.

This is not the case for a Data Warehouse primarily because a Data Warehouse is extensively used for back-end analysis. Data Warehouses often have downtime built in that gets scheduled when the necessity arrives to accommodate periodic uploads of new data. 

Data Warehouse vs Database Summary
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Can You Use a Database as a Data Warehouse?

Having gone through an in-depth comparison between Data Warehouse vs Database, you might think of a question like “Is it possible to use a database as a Data Warehouse?” 

You know that databases store information in a structured format of rows and columns that can be easily accessed and managed. But the main purpose of using databases is to process and store daily transactional data that your company generates or receives from outside sources like customers, suppliers, etc. Databases use OnLine Transactional Processing (OLTP) to insert, replace, and update data that can be queried very fast. However, when you want to process and analyze large volumes, Data Warehouses take advantage over databases by leveraging OnLine Analytical Processing (OLAP) which can analyze volumes of data swiftly. 

The processing technology, OLAP, used by Data Warehouses is specifically designed to accelerate data processing and data analysis requirements of a business, which is a significant 1000 times faster in comparison to processing in database OLTP systems. Moreover, databases normalize data, which is good for storage, but bad for querying. To query data fast, you need denormalized data that can be accessed easily.

With a burgeoning adoption of Cloud-based CRMs, Project Management Tools, Customer Support Apps, and Business Intelligence, Cloud-based Data Warehouse offerings have become cheap and offer you to pay only for used storage and computing resources (depending on the data warehouse you choose). With such a wide variety of options to choose from like Google BigQuery, Snowflake, Amazon Redshift, and Firebolt, with the added benefit of their fast processing capabilities and thriving user base, there is hardly any reason why you wouldn’t want to use a Data Warehouse.

Choosing to use a Cloud-based Data Warehouse will drastically improve your data processing and analytics needs by providing a Single Source of Truth (SSOT) to all your employees, especially data scientists and engineers. Using ETL tools like Hevo Data, you can simplify Data Warehouse ETL by merging all your data sources seamlessly with our 100+ built-in connectors (with over 40+ Free Source Connectors) and you can set up Data Pipelines in as easy as 3 simple steps.


When comparing data warehouse vs database, both are effective data storages that deal with large amounts of data. Either offers numerous yet distinct benefits and is extremely useful in business. In today’s data-driven economy, one cannot undermine their importance. However, this resourcefulness depends on the purposes of a business organization.

For example, if the primary objective is to store customer data for a movie theater, a database will be an ideal option. Suppose the main purpose is to pull together information from multiple disparate sources and use the collective data for carrying out analysis and reporting. In that case, a Data Warehouse will be a perfect choice, along with databases.

Extracting complex data from a diverse set of data sources to carry out an insightful analysis can be a challenging task and this is where Hevo saves the day! Hevo offers a faster way to move data from Databases, and SaaS applications into your Data Warehouse to be visualized in a BI tool. Hevo Data is fully automated and hence does not require you to code.

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