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What is a Data Warehouse?

By October 26, 2022April 26th, 2023Analytics Glossary, Data Engineering, Data Management4 mins read
Data Warehouse


A data warehouse is one of the key components of any analytics solution. It is used to store and analyze data from various sources, including other databases and transactional systems in a structured format. The information stored in a data warehouse can be accessed by any business user who has appropriate permissions.

Data Warehouse Definition

A data warehouse (DW) is a digital storage system that connects and harmonizes large amounts of data from many different sources. Its purpose is to feed business intelligence (BI), reporting, and analytics, and support regulatory requirements – so companies can turn their data into insight and make smart, data-driven decisions. Data warehouses store current and historical data in one place and act as the single source of truth for an organization. Data warehouses are especially beneficial to organizations because they provide a centralized location for all of an organization’s data which can then be used to support various business needs such as BI, reporting, analytics, and compliance. A key point to note here is that data warehouses can handle only structured data.

The Need for Modern Data Warehousing

A data warehouse is one of the key components of any analytics solution. It’s a central repository for data from multiple sources and can be used to store historical data and make it available for analysis. A good way to understand this is by thinking about your own life: if you have a car, there are probably many different tools that help keep it in good condition. You might have an air filter, oil change reminders on your calendar (like we did!), and even some instructions on how to change your own oil in case something goes wrong with yours! These all help keep your car in optimal condition so that it lasts longer than expected. However, when someone has trouble understanding exactly what needs fixing or replacing because they don’t know what happened last time they took care of their vehicle themselves—or even worse yet haven’t been able to figure out why they’re having issues again—they may call upon someone else who specializes in working on cars more frequently than most people do; maybe even one who knows more than just basic maintenance procedures.”

Data Warehouse vs Database

The difference between a data warehouse and a database is that the former is designed to handle large amounts of data, whereas the latter only handles small quantities. A typical OLAP cube contains billions of rows and hundreds of columns, whereas, in a traditional relational database, you’ll find not more than a few million records. A data warehouse can hold many different types of information from various sources: financial reports from various organizations; customer profiles from marketing departments; product sales statistics from multiple departments within your company.

Parameter Data Warehouse Database
Processing type OLAP OLTP
Operations Complex analytical queries CRUD transactions
Data Source Multiple sources Usually a single source
Data Timeline Historical Data Daily to Monthly
Data Volume Mid to Large Low to Mid

Data Warehouse schema

The data warehouse schema is used to define the data that will be stored in the data warehouse, which is then populated with operational and historical data.

The definition of a data warehouse schema is different from that of an overall database or operational system’s (OLTP) model. In fact, it can even be thought of as being far more abstract than either one: while OLTP models are usually defined using relational algebraic languages like SQL or PL/SQL, DW schemas are defined using XML documents only because they’re designed to describe things like transactions and business rules without implementing them on top of an existing set (as opposed to relational algebraic modeling).

Data Warehouse Real-Life Use Cases

A data warehouse is a repository of data, which you can use to make business-related decisions. It’s a critical tool in the analytics process, but many different ways exist.

  • Business Intelligence: Data warehouses are used to provide business intelligence (BI) reports that show trends and patterns in your organization’s performance. Managers and executives use BI so they can make informed decisions about what actions need to be taken next.
  • Data Science: Similar to BI, data science uses analytics tools like machine learning algorithms and predictive models in order to predict future outcomes based on past behaviors or events within an organization’s current environment at any given time period such as day/month/year, etc


A data warehouse is the central repository of all the data that is captured by your analytics solution. It stores not only relational tables but also any other types of structured or unstructured data (such as text files), which can be queried to extract information from it. It may also contain some indexing capabilities, allowing for fast lookups in any particular column or group of columns in a database table.

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