What is Data Warehousing?

By |2020-12-30T07:46:31+00:00December 30th, 2020|

In today’s business environment, it is essential for an organization to have reliable reporting and analysis of their 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.

In this blog, we’ll discuss 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 of Data Warehouse: 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.

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

Often people confuse between a data warehouse and a database as they both share some similarities. The main difference 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.

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 Features

The main characteristics of a data warehouse are:

  • Subject Oriented: A data warehouse provides information catered to a specific subject instead of 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.

Example of Data Warehouse in Different Industries

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.

Retain chain

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.


In the healthcare sector, a data warehouse is used to forecast outcomes, generate treatment reports, share data with insurance providers, etc.

Types of Data Warehouse

There are three main types of a data warehouse (DWH):

1- Enterprise Data Warehouse

It serves as a central or key database that facilitates decision-making throughout the enterprise.

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.

3- Data Mart

It is a subset of a data warehouse that is intended to support a particular department, region, or business unit.

Advantages: Why Does A Business Needs 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 Tool 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-user access tools for data warehouses solve these issues by creating a single database of homogeneous data that can be easily accessed 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. This is the underlying framework of end-user access tools for data warehouses that ensures the quality and usability of the data. Astera Centerprise brings together high-performance data warehousing extract, transform, and load (ETL) features in a unified and intuitive package. It offers a number of features and optimizations to support data warehouse loading, including a high-performance slowly changing dimensions (SCD) component, lookup caching, robust parallel-processing engine, and optimized database writes. Click here to learn more about data warehousing and the optimized Centerprise, a high-performance data warehousing solution. You can also view our webinar video, Working with the High-volume Data Warehouse in Centerprise on Astera.TV.