Analysis and research for a data warehouse system Data warehousing is a difficult system and must have the ability to provide quality data. An operational database is one used by organizations to perform day-to-day database tasks. They are designed to handle fast transaction processes with systematic updates. Speed ​​is important for operational databases. They are most commonly operated by office personnel and are on the order of megabytes of data to gigabytes. Database consistency checks and constraints are strictly enforced. They contain the latest technology necessary for organizational functions to function. A data warehouse is different in several ways. They are used by management to make decisions, follow trends and generate reports. They are usually used offline, have a minimum number of users, and are huge: gigabytes to terabytes. They contain decades of data, which is read-only, added but never updated. The data in the data warehouse is time-sensitive: each row in the warehouse is assigned a timestamp so that the data can be trended against time. The types of queries performed on data warehouses are difficult. These are decision support databases used to make strategic decisions about the organization. Companies have data warehouses to gain insights into the latest organizational data fads that strategically impact 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, since the operational data store contains data that is only true at the current time. For example… half of the sheet… the constraints contain data that shows the rows in the fact table. In the star schema design, dimension tables are demoralized to reduce the number of JOINs needed in fact table queries, while in the snowflake schema, dimension tables are normalized to reduce data duplication and allow for reuse of those tables with other fact tables. At the physical level, data warehouses tend to be heavily indexed and partitioned to store the most used data faster. There are also other options available. Data warehouses are generally designed with specific questions in mind, but as the data grows, the warehouse gains value because there are new questions that can be asked if only the organization is discerning enough to see them. These questions and their answers can lead to new opportunities to engineer competitive advantage.