Data Warehouse: Definition, Uses, and Examples

Key takeaways

• By definition, data warehouses are central repositories of a business’s data, and they typically provide authorized, relevant employees with access to that data.

• A data warehouse typically provides a single source of truth, centralizing data from external and internal sources, that the business can use to analyze and make decisions or gain insights.

• When comparing a data lake versus a data warehouse, you’ll find the two are similar, with a data warehouse acting as a centralized information system you can use to store summarized data for business intelligence purposes, and a data lake providing a large repository for capturing and storing raw data.

Data warehouse design may include a three-tier structure, where the bottom tier houses the data, the middle tier hosts the online analytical processing (OLAP) server, and the top tier provides end users with access to extract and analyze the data. Learn more about what a data warehouse is, its benefits, its architecture, and its uses, along with details and common tools.

If you want to begin learning how to work with data warehouses professionally, enroll in the IBM Data Warehouse Engineer Professional Certificate. In around four months, you’ll have an opportunity to gain practical experience in creating data pipelines, working with relational database systems, designing and populating data warehouses, and using business intelligence tools to analyze the data. After completing this beginner-level, nine-course series, you'll earn a career credential you can add to your resume and professional profile.

Note: See bottom of article for a complete acronym glossary

What is a data warehouse?

A data warehouse, or “enterprise data warehouse” (EDW), is a central repository system in which businesses store valuable information, such as customer and sales data, for analytics and reporting purposes. Data warehouses store and process large amounts of data from various sources within a business. An integral component of business intelligence (BI), data warehouses help businesses make better, more informed decisions by applying data analytics to large volumes of information.

Used to develop insights and guide decision-making via BI, data warehouses often contain a combination of both current and historical data that has been extracted, transformed, and loaded (ETL) from several sources, including internal and external databases.

Typically, a data warehouse acts as a business’s single source of truth (SSOT) by centralizing data within a non-volatile and standardized system accessible to relevant employees. Designed to facilitate online analytical processing (OLAP) and used for quick and efficient multidimensional data analysis, data warehouses contain large stores of summarized data that can sometimes be many petabytes large.

Data warehouse examples

As data becomes more integral to the services that power our world, so too do warehouses capable of housing and analyzing large volumes of data. Whether you’ve realized it or not, you likely use many of these services every day.

What is an example of a data warehouse?

Imagine a giant digital warehouse storing all your shopping information from various stores you frequent. That's a simplified analogy for a data warehouse. In the real world, your favorite store's data warehouse might combine sales transactions, customer loyalty program details, and inventory data to understand buying habits and optimize stock management.

A data warehouse is a large central storage unit for current and historical data collected from various sources. Unlike databases used for daily operations, data warehouses hold historical information, often from multiple departments or even companies. This allows data analysts to examine trends over time, identify patterns, and gain insights that can inform better business decisions.

Some of the most common real-world examples of data warehouses being used today include the following:

1. Health care

In recent decades, the health care industry has increasingly turned to data analytics to improve patient care, efficiently manage operations, and reach business goals. As a result, data scientists, data analysts, and health informatics professionals rely on data warehouses to store and process large amounts of relevant health care data.

2. Banking

Open up a banking statement and you’ll likely see a long list of transactions: ATM withdrawals, purchases, bill payments, and on and on. While the list of transactions might be long for a single individual, they’re much longer for the many millions of customers who rely on banking services every day. Rather than sitting on this wealth of data, banks use data warehouses to store and analyze this data to develop actionable insights and improve their service offerings.

3. Retail

Retailers, whether online or in-person, are concerned about how much product they’re buying, selling, and stocking. Today, data warehouses allow retailers to store large amounts of transactional and customer information to help them improve their decision-making when purchasing inventory and marketing products to their target market.

Data warehouse benefits

Data warehouses provide many benefits to businesses. Some of the most common benefits include:

• Provide a stable, centralized repository for large amounts of historical data

• Improve business processes and decision-making with actionable insights

• Increase a business’s overall return on investment (ROI)

• Improve data quality

• Enhance BI performance and capabilities by drawing on multiple sources

• Provide access to historical data business-wide

• Use AI and machine learning to improve business analytics

Data lake vs. data warehouse vs. database

Certain terms and concepts help you make sense of the field and perform your job responsibilities, should you pursue a profession in data analytics. Take a look at three common terms: data warehouse, data lake, and database. At a glance, here's what each means:

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

Data warehouse design and concepts

Whether you’re looking to start a career in business intelligence or data analytics, more generally, you should have a strong grasp of key data warehouse concepts and terms. Some of the most common to know include the following:

Data warehouse architecture

The exact architecture of a data warehouse will vary from one to another. Data warehouses can be one-, two-, or three-tier structures. Perhaps the most common, however, is the three-tier architectural structure, which looks as follows:

• Bottom tier: Also called the data tier, where the data is supplied to the warehouse

• Middle tier: Also called the application tier, where an OLAP server processes the data

• Top tier: Also called the presentation tier, which is designed for end-users with particular tools and application programming interfaces (APIs) used for data extraction and analysis

Cloud data warehouse

Traditionally, data warehouses were housed in servers within a business’s physical location. Today, though, more and more data warehouses use cloud storage to house and analyze large volumes of data. Some of the most common cloud data warehouse software include:

• Microsoft Azure data warehouses, particularly Azure Synapse Analytics and Azure SQL database

• AWS’ data warehouse, Amazon Redshift

• Google Cloud’s data warehouse, Google BigQuery

• Snowflake AI Data Cloud

Data warehouse acronym glossary

Stay in the loop on career trends and tips

Keep up with emerging technologies and in-demand skills, and get professional insights and tips with a subscription to Career Chat, our weekly LinkedIn newsletter. Then, check out some of our other free data analytics resources to continue learning.

• Watch on YouTube: Data Warehouse vs. Database: Which One Do You Need?

• Hear from a fellow learner: Meet the Learner in Turkey Who Wants to Advance in Data Science

• Find your path: Data Engineering Learning Roadmap: From Beginner to Expert

You can also continue learning about data warehouses, analytics, and more with Coursera Plus. You’ll get access to more than 10,000 programs to help advance your skills and career.