A data warehouse is a system that aggregates data from multiple sources into a single, central, consistent data store to support data analysis, data mining, artificial intelligence, and machine learning². A data warehouse enables an organization to run powerful analytics on huge volumes of historical data in ways that a standard database cannot².

A data warehouse is separate from a database management system (DBMS), which stores data in the form of tables, uses ER model, and the goal is ACID properties¹. A data warehouse stores a huge amount of data, which is typically collected from multiple heterogeneous sources like files, DBMS, etc. The goal is to produce statistical results that may help in decision making¹.

Some of the benefits of a data warehouse are¹:

• Better business analytics: Data warehouse plays an important role in every business to store and analyze all the past data and records of the company, which can further increase the understanding or analysis of data to the company.

• Faster queries: Data warehouse is designed to handle large queries that’s why it runs queries faster than the database.

• Improved data quality: In the data warehouse, the data you gathered from different sources is being stored and analyzed. It does not interfere with or add data by itself, so your quality of data is maintained. And if you get any issue regarding data quality, then the data warehouse team will solve this.

• Historical insight: The warehouse stores all your historical data which contains details about the business so that one can analyze it at any time and extract insights from it.

A data warehouse generally has a three-tier architecture, which consists of a bottom tier, a middle tier, and a top tier².

• The bottom tier consists of a data warehouse server, usually a relational database system, which collects, cleanses, and transforms data from multiple data sources through a process known as Extract, Transform, and Load (ETL) or a process known as Extract, Load, and Transform (ELT)².

• The middle tier consists of an OLAP (online analytical processing) server which enables fast query processing and analysis of multidimensional data using dimensions and measures².

• The top tier consists of the end-user tools such as dashboards, reports, charts, graphs, etc., that present the analytical results to the users².

Data warehousing can be applied anywhere where we have a huge amount of data and we want to see statistical results that help in decision making¹. Some examples of applications of data warehousing are:

• Social media websites: The social networking websites like Facebook, Twitter, Linkedin, etc. are based on analyzing large datasets. These sites gather data related to members, groups, locations, etc., and store it in a single central repository. Being a large amount of data they use Data Warehousing techniques for storing and analyzing their user’s activities like posts, comments, likes etc.¹

• Banking and finance: The banking and finance sector uses data warehousing for various purposes such as risk management, fraud detection, customer analysis, credit card transactions, etc. Data warehousing helps them to integrate data from different sources such as branches, ATMs, online banking, etc., and perform complex queries and analytics on them¹.

• Retail and e-commerce: The retail and e-commerce industry uses data warehousing for analyzing customer behavior, preferences, trends, sales patterns, inventory management, etc. Data warehousing helps them to optimize their marketing strategies, pricing policies, product recommendations, etc., based on the insights derived from the data¹.