Consolidating data warehouse tables

Notwithstanding the many approaches to implementing decision support systems in practice, there are two basic types of data warehouses: enterprise data warehouses and data marts.Each have their proponents, as well as their respective strengths and weaknesses.These processes need to be automated so that they can be performed on an ongoing basis: extracting, transforming, and moving the source data as often as needed to meet the business requirements of the data warehouse.In the operational environment, data is current valued and accurate as of the moment of access.Making this data available to a wide audience of business users is one of the most significant challenges for today's information technology professionals.In response, many organizations choose to build a data warehouse to unlock the information in their operational systems and understand real-world business problems.Coupled with third-party products that can be integrated using the Microsoft Data Warehousing Framework, customers have a large selection of interoperable, best-of-breed products from which to choose for their data warehousing needs.

While there are many types of data warehouses, based on different design methodologies and philosophical approaches, they all have these common traits: The data warehousing process is inherently complex, and as a result has been historically costly and time-consuming.This is invariably an ongoing process, not a one-time solution, and requires a different approach from that required in the development of transaction-oriented systems. Simply put, this means that the data warehouse is focused on a business concept (for example, sales) rather than a business process (for example, issuing invoices), and contains all the relevant information on the concept gathered from multiple processing systems.A data warehouse is a collection of data in support of management's decision-making process that is subject-oriented; integrated; time-variant; and nonvolatile (see W. This information is collected and represented at consistent periods of time and is not changing rapidly.An enterprise warehouse contains both detailed point-in-time data and summarized information and can range from 50 gigabytes to more than one terabyte in total data size.Enterprise data warehouses can be very expensive and time-consuming to build and manage.

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