What Is a Data Warehouse? Definition, Concepts, and Benefits

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Where do you store a petabyte of data for business intelligence? A data warehouse, that’s where.

[Featured image] A data engineer works on maintaining a data warehouse. He's standing at his workstation in front of a large computer monitor.

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 companies make better, more informed decisions by applying data analytics to large volumes of information. 

In this article, you’ll learn more about what data warehouses are, their benefits, and how they’re used in the real world. You’ll also learn how data warehouses differ from other similar concepts, explore common warehousing tools, and find relevant courses that can help you start exploring a career in data today. 

Note: See bottom of article for complete acronym glossary.

What is a data warehouse? 

A data warehouse, or 'enterprise data warehouse' (EDW), is a central repository system where businesses store valuable information, such as customer and sales data, for analytics and reporting purposes. 

Used to develop insights and guide decision-making via business intelligence (BI), data warehouses often contain 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 centralising data within a non-volatile, standardised 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 summarised data that can sometimes be large petabytes [1]. 

Data warehouse benefits

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

  • Provide a stable, centralised 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 warehouse examples

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

Here are some of the most common real-world examples of data warehouses being used today:

Health care 

In recent decades, the healthcare 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 healthcare data [2]. 


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


Retailers—whether online or in-person—are always concerned about how much product they buy, sell, and stock. 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 lake vs data warehouse vs database

Many terms in data analytics sound alike, such as data warehouse, data lake, and database. But despite their similarities, each refers to meaningfully different concepts.

A database is any collection of data stored electronically in tables. In business, databases are often used for online transaction processing (OLTP), which captures and records detailed information in real-time, such as sales transactions, and then stores it for later reference. 

A data warehouse, meanwhile, is a centralised repository and information system used to develop insights and guide decision-making through business intelligence. It stores summarised data from multiple sources, such as databases, and employs online analytical processing (OLAP) to analyse it. 

Finally, a data lake is a large repository designed to capture and store structured, semi-structured, and unstructured raw data. This data can be used for machine learning or AI in its raw state and data analytics, advanced analytics, or databases and data warehouses after processing. 

Data warehouse concepts

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

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, in which the data is supplied to the warehouse 

  • Middle tier: Also called the application tier, in which 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 analyse 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 Big Query

  • Snowflake data warehouse

Work with data warehouses 

Data warehouses are powerful tools used by businesses every day. Start your journey toward working with data warehouses today by taking a flexible online course like the IBM Data Warehouse Engineer Professional Certificate, which can help you develop job-ready skills for an entry-level role in data warehousing.

Additionally, IBM’s BI Foundations with SQL, ETL and Data Warehousing Specialisation will help you prepare for BI analytics success by developing hands-on skills for building data pipelines, warehouses, reports, and dashboards.

*Glossary of Acronyms

AI: Artificial Intelligence

ATM: Automated Teller Machine

BI: Business Intelligence

EDW: Enterprise Data Warehouse

ETL: Extracted, Transformed, and Loaded

OLAP: Online Analytical Processing

OLTP: Online Transaction Processing

ROI: Return on Investment

SQL: Structured Query Language

SSOT: Single Source of Truth


Article sources


IBM. “OLAP, https://www.ibm.com/cloud/learn/olap.” Accessed June 7, 2024.

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