Analysis and Research for a data warehouse system

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Analysis and Research for a data warehouse system
Data warehousing is a difficult system and has to have the capability deliver quality data. An operational database is one which is used by organizations to run its day to day database activities. They are designed to handle rapid transaction processes with systematically updates. Velocity is important to operational databases. They are most commonly operated by office staff, and are on the order of megabytes of data to gigabytes. Database consistency checks and constraints are rigidly enforced. They contain the latest technology necessary to operate organizational functions.
A data warehouse is different in several ways. They are used by management for making decisions, following trends, and pulling reports. They are typically used offline, have minimal users and are enormous: gigabytes to terabytes. They contain decades of data, which are read only, and added to but never updated. The data in the data warehouse is time sensitive - each row in the warehouse is time stamped so that trending of data versus time can be done. The kinds of queries that are run against data warehouses are difficult. These are decisions support databases that are used to make strategic decisions about the organization.
Businesses have data warehouses in place to attain knowledge about latest fads in organization data that affect the business strategically. This type of analysis and reporting is called OLAP: on line analytical processing. Management uses OLAP tools on data warehouse to run reports and make determinations. This would be impossible to do with an operational data store, since operational data store contains data that is only true at the current time. For exam...

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...ey constraints, contain data which shows the rows in the fact table. In the star schema design, the dimension tables are demoralized to reduce the number of JOINs necessary in queries on the fact table, while in the snowflake schema the dimension tables are normalized to reduce data duplication and allow reuse of those tables with other fact tables.
At a physical level, data warehouses tend to be heavily indexed and partitioned to put the most used data on faster storage. There are other options available as well. Data warehouses are typically designed with specific questions in mind, but as data grows, the warehouse gains value because there are new questions that can be asked if only the organization is perceptive enough to see them. Those questions and their answers can lead to new opportunities for designing a competitive advantage.
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