What is Data Warehousing? Concepts, Features, and Examples

By |2021-07-19T10:00:03+00:00November 6th, 2020|

In today’s business environment, it is essential for an organization to have reliable reporting and analysis of their ever-increasing data. Businesses need their data to be consolidated and integrated for different levels of aggregation, from customer service to partner integration to top-level executive business decisions. This is where data warehousing comes in as it makes reporting and analysis easier. This rise in data in turn increases the use of data warehouses in business.

In this blog, we’ll discuss different data warehouse concepts. The blog begins by explaining what is data warehousing, the use of data warehouses in different industries, data warehousing features, and types of data warehouses. Lastly, we’ll explain an effective, easy-to-use data warehouse tool.

Definition: What is a Data Warehouse?

The data warehouse (DWH) is defined as a repository of an organization’s electronically stored data extracted from operational systems and made available for ad-hoc queries and scheduled reporting. While the process of data warehousing simply entails constructing and using the data warehouse. Data stored in the data warehouse is different from data found in the operational environment in that it is organized in such a way where relevant data is clustered together to facilitate reporting for day-to-day operations and analysis. This then determines the trends over time and creates plans based on that information. Hence, reinforcing the importance of the use of warehouses in business.


Difference Between Data Warehouse and Database: Data Warehouse Vs. Database

Often people confuse between data warehouse vs. database as they both share some similarities. So, what differentiates data warehouse vs database then? The main difference between a data warehouse and a database is made obvious when an enterprise needs to perform analytics on an extensive data set. However, a data warehouse is equipped to handle a large data set, but a database is not.

Approaches of Combining Heterogeneous Databases

In order to integrate different databases, there are two popular approaches:

  • Query-driven Approach: A query-driven approach is a traditional approach that is used to create integrators and wrappers on top of different dissimilar or heterogeneous databases.
  • Update-driven Approach: An update-driven approach to integrating database is an alternative to the query-driven approach, and is more frequently used today. In this approach, the data from different heterogeneous sources is combined or integrated beforehand and stored in a data warehouse. Later, this data can be accessed for querying and analysis.

Data Warehouse Architecture

A data warehouse architecture uses dimensional models to identify the best technique for extracting meaningful information from raw data and translating it into an easy-to-understand structure. However, there are three main types of data warehouse architecture that should be taken into account when designing a business-level data warehouse.

  1. Single-tier Architecture
  2. Two-tier Architecture
  3. Three-tier Architecture

Data Warehouse Features Explained

The main data warehouse features are:

  • Subject Oriented: A data warehouse provides information catered to a specific subject instead of the whole organization’s ongoing operations. Examples of subjects include product information, sales data, customer and supplier details, etc.
  • Integrated:  A data warehouse is developed by combining data from multiple heterogeneous sources, such as flat files and relational databases, which consequently improves data analysis.
  • Time-Variant:  The data in a data warehouse gives information from a certain historical point of time therefore the information in a data warehouse is categorized with a particular time frame.
  • Non-volatile: Non-volatile refers to the prior data that is not omitted when newer data is added to it. A data warehouse is separate from an operational database, which means that any regular changes in the operational database are not seen in the data warehouse.

Examples of Data Warehousing in Various Industries

Data warehousing has become a vital part of performing business intelligence across several industries. Here are some examples of how data warehousing is used across the board. Let’s look at various industries that consider data warehouse an essential part of their day-to-day operations.

  • Investment and Insurance sector

In the investment and insurance sector, data warehouses are primarily used to analyze customer and market trends and other data patterns. Forex and stock markets are two major sub-sectors where data warehouses play a crucial role because a single point difference can lead to massive losses across the board. In these sectors, data warehouses are usually shared and focus on real-time data streaming.

  • Retail chains

In the retail sector, data warehouses are majorly used for distribution and marketing to enable tracking of items, examining pricing policies, keeping track of promotional deals, and analyzing customer buying trends. Retail chains usually incorporate enterprise data warehouse system for business intelligence and forecasting needs.

  • Healthcare

In the healthcare sector, a data warehouse is used to forecast outcomes, generate treatment reports, share data with insurance providers, research labs, and other medical units. Enterprise data warehouses are the backbone of healthcare systems because the latest, up-to-date treatment information is crucial for saving lives.

Types of Data Warehouse

There are three main types of data warehouse (DWH). Each has its specific role in data management operations.

Data Warehouse Concept and Types (Source: Educba)

1- Enterprise Data Warehouse

Enterprise data warehouse (EDW) serves as a central or key database to facilitate decision-making throughout the enterprise. Major benefits of having an EDW include access to cross-organizational information, the ability to run complex queries, and the enablement of enriched, far-sighted insights for data-driven decisions and early risk assessment.

2- ODS(Operational Data Store)

In ODS, the data warehouse refreshes in real-time. Therefore, it is often used for routine enterprise activities, such as storing records of the employees. It is used as a source for providing data to the enterprise data warehouse.

3- Data Mart

It is a subset of a data warehouse that is intended to support a particular department, region, or business unit. Consider this: You have multiple departments including sales, marketing, product development, etc. Each department will have a central repository where it stores data. This repository is called a data mart. The data from the data mart is stored in the ODS on a daily/weekly (or as configured) basis. The ODS acts as a staging area for data integration. It then sends the data to the EDW, where it is stored and used for BI purposes.

Advantages: Why Businesses Need Data Warehouse?

A lot of business users wonder why is data warehousing important? Data warehousing offers several benefits to the end-users, which include:

  • Improved end-user access to a wide variety of enterprise data
  • Increased data consistency
  • Additional documentation of the data
  • Potentially lower computing costs and increased productivity
  • Providing a place to combine related data from separate sources
  • Creation of a computing infrastructure that can support changes in computer systems and business structures
  • Empowering end-users to perform ad-hoc queries or reports without impacting the performance of the operational systems

Data Warehousing Tools and Techniques

The data infrastructure of most organizations is a collection of heterogeneous systems. For example, an organization might have one system that handles customer-relationships, human resources, sales, production, finance, partners, etc. These systems are often poorly or not at all integrated and simple questions can be very hard to answer, even though the information is available “somewhere” within the disparate data systems.  End-users ‘ access to data warehouse tools can solve these issues by creating a single database of homogeneous data that can be easily retrieved and manipulated.

Needless to say, the software tools used for extracting and transforming the data into a homogeneous format that can be loaded into the data warehouse are also key components of a data warehousing system.

Enterprise Data Warehousing Automation Tool by Astera Software

Astera data warehouse builder expedites the process of developing a data warehouse from scratch, helps integrate data from multiple data marts and databases, automates modeling the schema structure, and delivers a high-performance data warehouse through a unified and intuitive platform.

Astera Data Warehouse Builder also makes data warehouse automation easier. ADWB is a metadata-driven data warehousing automation tool with a rich data modeler. The reverse-engineer functionality allows creating databases in few clicks, without writing any codes. Similarly,  schemas can easily be created from scratch with the easy to use drag-and-drop option. The images below briefly depict how the Astera data warehouse builder works.

Reverse-engineering feature in Astera DWB


Dataflow to populate dimension table in ADWB


Once the schema is built and data is populated, the data model can be forward engineered just as easily to the business’ database.

Learn more about how to build your data warehouse from scratch with Astera Data Warehouse Builder, a high-performance data warehousing solution.

If you want to discuss your use case or wish to see a live demo of the product, let us know and our experts will reach out to you.