Introduction to Data Warehouse 

The term "Data Warehouse" was first introduced by Bill Inmon in 1990. Inmon defined a data warehouse as a subject-oriented, integrated, time-variant, and non-volatile collection of data, designed to assist analysts in making informed business decisions. Unlike operational databases, which are frequently updated due to daily transactions, a data warehouse stores historical data that remains unchanged. This makes it ideal for analyzing trends, patterns, and historical performance.

For instance, if a business executive wants to review past feedback on a product, supplier, or customer, an operational database may not provide the necessary data due to constant updates. A data warehouse, however, offers a generalized and consolidated view of data across multiple dimensions, making it easier to analyze. Additionally, it includes Online Analytical Processing (OLAP) tools, which allow users to interact with data in a multidimensional space, leading to data generalization and data mining. By integrating functions like association, clustering, classification, and prediction with OLAP, data warehouses have become essential platforms for advanced data analysis and interactive mining of knowledge across various levels of abstraction.

Understanding a Data Warehouse

1. Separate from Operational Databases

A data warehouse is a specialized database that is kept distinct from an organization's operational database, allowing for dedicated analytical processing.

2. No Frequent Updates

Unlike operational databases, data warehouses are not frequently updated. They primarily store static, consolidated data for analysis.

3. Consolidated Historical Data

Data warehouses contain historical data from multiple sources, enabling organizations to perform deep analysis and gain insights into business trends and performance.

4. Strategic Decision-Making

A data warehouse empowers executives by helping them organize, understand, and utilize data effectively, supporting informed strategic decisions.

5. Integration of Diverse Systems

Data warehouses facilitate the integration of data from various application systems, ensuring a unified, comprehensive view of information.

6.Historical Data Analysis

With a consolidated data warehouse, businesses can easily perform historical data analysis to uncover patterns and make future predictions.

Key Features of a Data Warehouse

1. Subject-Oriented

A data warehouse is subject-oriented because it organizes information around key subjects such as products, customers, suppliers, sales, and revenue. Unlike operational databases that focus on daily transactions, a data warehouse is designed to support data modeling and analysis for better decision-making.

2. Integrated

Data in a data warehouse is integrated from various heterogeneous sources, such as relational databases, flat files, and other storage systems. This data integration ensures a unified view, allowing for more comprehensive and effective data analysis.

3. Time-Variant

The data stored in a data warehouse is associated with specific time periods, making it easier to analyze historical trends. This time-variant nature allows organizations to review and assess data from a historical perspective, which is essential for long-term analysis and planning.

4. Non-Volatile

A data warehouse is non-volatile, meaning that once data is stored, it remains unchanged even as new data is added. This separation from the operational database ensures that frequent updates in the operational system do not affect the historical data stored in the warehouse, preserving data integrity for analysis and reporting.

Conclusion

In conclusion, a data warehouse is a powerful tool for organizations to store and analyze historical data. Its key features—subject orientation, data integration, time variance, and non-volatility—enable businesses to make informed, strategic decisions by providing a unified and comprehensive view of their data over time. This makes the data warehouse an essential platform for effective decision-making and long-term planning.