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

Some of the benefits of a data warehouse are:

• Better business analytics: A data warehouse can store and analyze all the past data and records of the company, which can increase the understanding or analysis of data for the company¹.

• Faster queries: A data warehouse is designed to handle large queries, so it runs queries faster than the database¹.

• Improved data quality: A data warehouse collects, cleanses, and transforms data from multiple sources, so it does not interfere with or add data by itself. This ensures the quality of data and any issues can be resolved by the data warehouse team¹.

• Historical insight: A data warehouse stores all the historical data, which contains details about the business, so that one can analyze it at any time and extract insights from it¹.

A data warehouse is different from a database in several aspects:

• A database is based on operational or transactional processing, while a data warehouse is based on analytical processing¹.

• A database stores current and up-to-date data that is used for daily operations, while a data warehouse maintains historical data over time that is used for trend analysis, future predictions, and decision support¹.

• A database uses an ER model and the goal is ACID properties, while a data warehouse uses a dimensional model and the goal is OLAP operations¹.

A data warehouse typically has a three-tier architecture, which consists of:

• Bottom tier: 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.

• Middle tier: The middle tier consists of an Online Analytical Processing (OLAP) server, which performs multidimensional analysis of the data and provides access to end users².

• Top tier: The top tier consists of various tools and applications that present the data to the end users in a meaningful way, such as dashboards, reports, charts, graphs, etc².