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 learning. It serves as a central repository for large amounts of structured or semi-structured data. Data warehouses are designed to store and analyze large volumes of data, providing a foundation for better data quality and faster business insights. They enable organizations to run powerful analytics on huge volumes of historical data in ways that a standard database cannot. Data warehouses have been a part of business intelligence (BI) solutions for over three decades and have evolved with the emergence of new data types and hosting methods.
A typical data warehouse has a three-tier architecture:
– Bottom tier: This tier consists of a data warehouse server that collects, cleanses, and transforms data from multiple sources through a process known as Extract, Transform, and Load (ETL) or Extract, Load, and Transform (ELT).
– Middle tier: This tier consists of an OLAP (online analytical processing) server that enables fast query speeds. Different types of OLAP models can be used in this tier.
– Top tier: This tier is represented by a front-end user interface or reporting tool that enables end users to conduct ad-hoc data analysis on their business data.
OLAP (online analytical processing) is software for performing multidimensional analysis at high speeds on large volumes of data from a unified, centralized data store like a data warehouse. It is analytical in nature and is designed for multidimensional analysis of data in a data warehouse.