Data Warehousing (DWH)

A Data Warehouse (DWH) is a system that stores and organizes large amounts of data from multiple sources in one centralized place. It helps businesses combine, clean, and structure their data, making it ready for analysis and decision-making. Unlike operational systems used for daily tasks, a DWH is designed specifically for analysing data and identifying trends over time.

A DWH integrates data from various sources, such as files, databases, and applications, into a single system. It cleans and structures raw data, ensuring consistency and reliability for analysis. By storing historical data, it allows businesses to track changes and trends, while its optimization for handling large queries enables faster and more complex analyses.

A DWH is essential because it provides a clear and unified view of data, making it easier to access and use information from different systems. It reduces the workload on operational systems by handling analytical tasks separately while clean and organized data supports better decision-making. Additionally, it simplifies reporting and compliance by storing data in an organized way for audits and regulatory requirements.

Maintaining a DWH ensures businesses always have accurate and up-to-date information. A well-maintained DWH can scale with business growth, handling larger data volumes without losing efficiency. By managing the analytical load separately, it saves time and resources, improving overall productivity.

We now understand the concept of a data warehouse (DWH) and its role as a centralized system for storing and analysing data. In the next section, we will explore the Inmon and Kimball methodologies. We will also look at their differences and how businesses can apply them to design effective and efficient data warehouses.