Data Warehouse vs. Database: The Differences Explained

Written by Coursera • Updated on

Data warehouses and databases serve different purposes in an organization. Those differences are explained in this guide.

[Featured image] Database administrator in a yellow sweater looks at information on a computer screen

Every business and organization needs to store, access, and analyze data. Data warehouses store massive amounts of historical data for analysis, while databases keep a record of all sales orders or patient records, which can be used for other business purposes. Both are necessary to get things done.

In this article, we’ll take you through the differences between a data warehouse and a database. By the end, you will know whether an organization needs one or the other, or both, and several use cases and careers that work with these data-driven tools every day. 

Difference between a data warehouse and a database

While both data warehouses and databases act as data storage, there is one key difference between them. A data warehouse enables companies to track analytics for business intelligence, while a database is simply a collection of data in one place. Databases’ main purpose is to store data securely and allow users to access it easily.

For example, a health care system can have a database to track patient records. That same health care system might also need a data warehouse to store the entire system’s data on operations, marketing, and finance in one place.

Data warehouse vs. database: what’s the difference?

In a nutshell, these are the key differences between a data warehouse and a database. Databases 

Data warehouseDatabase
DatabaseOLAP (online analytical processing)OLTP (online transactional processing)
Type of collectionSubject-orientedApplication-oriented
QueryComplex analytical queriesSimple transaction queries

What is a data warehouse?

A data warehouse is a large repository of data accumulated from various sources within an organization that is used to make smart, data-driven decisions. Data science professionals can use queries in a data warehouse to gather the information needed for specific problems. 

Data warehouses are critical for organizations to understand things like profitability because they can pull data from several departments into one dashboard.

Data warehouse use cases

A data warehouse can have many applications. Here are some examples of data warehouses in practice:

  • Health care data analysis: A data warehouse can carry information that guides analysis for understanding how often and why cancer patients over age 25 tend to receive chemotherapy as opposed to radiation treatment. 

  • Company-wide performance evaluations: Companies can use data warehouses to evaluate company-wide team performance. Dashboards and reports can be created based on customer value and usage patterns to evaluate marketing, sales, and customer service teams.

Read more: Data Lake vs. Data Warehouse: What’s the Difference?

Related careers

While there may be many title variations for the following job roles, they are the team members who would encounter data warehouses in an organization.

  • Data warehouse analyst: A data warehouse analyst researches and evaluates data from data warehouse to make recommendations on improving data storage and reporting, as well as other business decisions. 

  • Business intelligence analyst: A business intelligence analyst uses data marts or warehouses to develop company- or department-wide insights by building reports, dashboards, and visualizations using tools like Python, SQL, and Tableau.

  • Data warehouse engineer: A data warehouse engineer builds and manages data warehouse strategies. They might be responsible for setting project scopes, choosing the right software tools, and leading strategic solutions.

What is a database?

A database stores data for one particular function of your business. They can record real-time information on daily transactions, such as products sold in an e-commerce business or patient records in an Electronic Health Record (EHR).

Databases can process many simple queries quickly. They can handle “big data,” but can be as small as an Excel spreadsheet of sales orders for a small business. A big data database can convert structured and unstructured data into formats that analytics tools can use.

Relational vs non-relational database

Relational databases, also called SQL databases, store data in rows and columns like an Excel spreadsheet. Non-relational databases use one of the four storage models (document, key-value stores, graph, and column) for more flexible storage.


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Database use cases

Databases can be used in an almost infinite number of ways. Here are just a couple so you can better grasp what a database is used for.

  • Electronic health record (EHR): In health care, patient information can be inputted into an EHR during their first visit, and updated during subsequent visits. Typically, this information stays secure and confidential on the platform. It updates the time and date of the appointment along with any other relevant symptoms and diagnoses, and can be accessed by clinicians at any time.

  • Tracking customer preferences: Online streaming services such as Netflix and Spotify use databases to track the TV shows and songs that are offered, as well as your viewing and listening preferences. This information is stored on NoSQL databases for flexible scalability.

Related careers

  • Database administrator: A database administrator makes sure a database can run efficiently. They might create or organize systems to store data as varied as financial information, product specifications, and customer orders. Database administrators make sure this data is available to those authorized to access it.

  • Database architect: Database architects design and build databases. They create the standard for operating, programming, and securing a database, so that data analysts, data scientists, and engineers can access it easily and efficiently.

  • Data analyst: Data analysts gather, clean, and study data sets to help solve an organization’s problems. 

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Written by Coursera • Updated on

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