Analysis and Research for a Data Warehouse SystemData warehousing is a challenging system and must have the ability to provide quality data. An operational database is a database used by organizations to perform their daily database activities. They are designed to handle fast transaction processes with systematic updates. Velocity is important for operational databases. They are most commonly operated by office staff and are in the range of megabytes of data to gigabytes. Consistency checks and database constraints are rigidly enforced. They contain the latest technologies necessary for the operation of organizational functions. A data warehouse is different in several ways. They are used by management to make decisions, track trends and write reports. They are usually used offline, have few users and are huge: gigabytes to terabytes. They contain decades of data, which is read-only and supplemented but never updated. The data in the data warehouse is time sensitive: each row in the warehouse is time-stamped so that data can be trended over time. The types of queries run against data warehouses are difficult. These are decision support databases that are used to make strategic decisions about the organization. Companies have data warehouses to gain knowledge about the latest trends in organizational data that strategically affect the business. This type of analysis and reporting is called OLAP: online analytical processing. Management uses OLAP tools on the data warehouse to run reports and make decisions. This would be impossible to do with an operational data store, because the operational data store contains data that is only true at the current time. For example...... middle of paper ......ey constraints, contain data that shows the rows of the fact table. In star schema design, dimension tables are demoralized to reduce the number of JOINs needed in fact table queries, while in snowflake schema, dimension tables are normalized to reduce duplication data and allow the reuse of these tables with other fact tables. At the physical level, data warehouses tend to be heavily indexed and partitioned to store the most used data more quickly. There are also other options available. Data warehouses are usually designed with specific questions in mind, but as the data grows, the warehouse becomes more valuable because new questions can be asked if only the organization is insightful enough to see them. These questions and their answers can lead to new opportunities for designing competitive advantage.