Data warehouse vs. database — key differences explained

Data warehouse vs database

As we become increasingly reliant on digital technologies for everything in business and daily life, humans are creating unfathomable quantities of data. By 2025, data volume is projected to exceed an extraordinary 180 zettabytes. All this information comes in from a variety of different inputs, with the average company managing as many as 400 sources. But where and how that data is stored is often overlooked by consumers.

Enter databases and data warehouses. While the two terms sound similar, they are not interchangeable. In fact, many companies need both to conduct business successfully and take advantage of all the information they’ve amassed. To this end, it’s important to understand the differences between these two terms and how to adopt them to support data management and analysis.

This article outlines the key differences between data warehouses and databases and how they are often used with respect to business analytics. We’ll explore the following topics:

Quick overview — the difference between a data warehouse and a database

To put it broadly, databases store information needed to run specific applications while data warehouses primarily store historical data from across systems to be analyzed.

But the key differences stretch far beyond this to include the following:

With a clearer understanding of how these two terms differ from one another, let’s take a more in-depth look at each.

What is a data warehouse?

A data warehouse is a digital depository where large quantities of historical data are held from across different systems for analysts to conduct robust processing and reporting. The advantage of using a data warehouse is that users can review content from across data sources to develop new insights, detect patterns, and make decisions that can guide the business forward.

Data warehouses are usually updated on a regularly scheduled basis, meaning that information may not reflect real-time results depending on when the last upload or update was conducted. Data warehouses can also allow analysts to work with disparate information that may not have the same formatting or conditions applied to them from their original sources.

Companies have more flexibility than ever before with where and how they establish their data warehouses. Options include both on-premise and cloud-based solutions, with the right choice depending on an organization’s needs regarding security, scalability, cost, and access.

A use case for leveraging a data warehouse can include running reports on customer data to better understand behaviors and develop new personalized marketing initiatives based on those needs.

Databases are repositories for data storage while data warehouses are used for active analysis of the information they hold.

What is a database?

A database is a collection of information that is electronically stored for quick use in a specific application. Databases systematically collect data points that are organized for more immediate reference or manipulation by a wide range of users.

The information contained in a single database usually reflects a specific use or business task needed to be performed. For example, a database can contain information specific to customer profiles to aid customer service representatives when taking inbound calls. Content relevant to internal human resources processes, on the other hand, would likely be contained in an entirely different database, accessed by a completely different application and team.

It’s important to note that databases come in many shapes and sizes depending on the technical needs of the applications they serve. While they may use different schemas or query language to structure their content, the end-user experience is typically the same — fast, on-demand access to specific information.

A use case for using a database can include an application for collecting customer data from marketing forms on a website. These fields are mapped to fields in the database that hold the information for team members to use and reference during their daily work.

Data warehouse vs. database

Seeing the differences between a database and a data warehouse can help business leaders understand the use of each for their own systems. Many companies already use databases to power the bulk of their computing and data management, even if those databases come integrated and ready to go with the applications purchased for use, such as cloud-based customer relationship management (CRM) systems.

However, there are a number of benefits for companies looking to better understand their business by analyzing data across platforms. This is where data warehouses shine. Consider how difficult it can be to run a search for information when the data is housed in different application databases. It can take quite a while to run the same search repeatedly, often across data that is organized differently, and then compile that disparate data into a single output that can then be processed and reviewed.

Data warehouses simplify this experience for business analysts, helping them draw from large amounts of data with complex queries without much of the sweat equity that can come with it.

To better understand the differences between a data warehouse versus a database, review the information compiled in the comparison chart below.

Data warehouse
Data recording — databases allow users to enter information into separate fields for reference and use in an application.
Data analysis — data warehouses allow users to work with the data in real time to run reports and draw business conclusions.
Processing method
Online transaction processing (OLTP) — manages aggregated data for transactional use.
Online analytical processing (OLTP) — manages stored data for analytical queries and review.
Data type
Real-time data leverages a live feed for end-user reference for performing daily activities.
Historical data leverages tools analysts use for longer form reports and business intelligence.
Available 24/7 unless unforeseen circumstances intervene.
Available on demand or intermittently depending on requests.
Real-time input and storage based on user interaction.
Scheduled updates at set intervals that can cause gaps in data, depending on when reports are run.
Concurrent and unlimited based on application access and licensing.
Asynchronous and limited to a few users depending on business need for data access.
Limited based on single application requirements or licensing.
Scalable depending on data needs across applications and systems.
Simple requests based largely on keywords to return basic records relevant to the application and assumed user need.
Complex requests based on designed queries formed by business analysts on demand.
Limited to a database per application relationship.
Unlimited support across databases and applications.

As shown in the above comparison, databases and data warehouses have their own unique advantages and applications. They can also have some downsides that business analysts need to be aware of.

Get started with analytics

Companies need meaningful and dynamic ways to store, access, and process the information they collect about everything from product performance to customer preferences. This means that both databases and data warehouses can be beneficial to supporting and optimizing business operations when used properly.

Databases can help reduce the time spent managing and sorting through detailed information that can otherwise be organized and stored for easy access via dedicated applications. This also helps keep content consistent across users working on the same platforms and makes it faster for everyone to work with content, whether in marketing, human resources, or customer service.

Data warehouses take this information a step further, allowing business analysts to pool together information from various systems and study it from a variety of angles. This can lead to more actionable insights and help direct better decision-making for leaders looking for a more holistic approach to understanding their business, industry, or customers.

When you’re ready to explore your data management processes, start by reviewing the analytics tools you already have in place. Do these give you the key insights you feel you need to make informed business decisions? What gaps exist that implementing a data warehouse or taking better advantage of your existing databases could address?

Adobe can help

Adobe Analytics can bring together different data sources to help business leaders experience a complete picture of their ecosystem. Reports can be personalized with real-time review that is actionable and relevant to your business concerns. Explore data from any stage in the customer journey and leverage predictive insights based on the Adobe Sensei AI engine.

Take the next step with your data analytics and learn how Adobe Analytics can boost your business. Learn more or request a personalized demo today.