A data warehouse architecture is a method of defining the overall architecture of data communication processing and presentation that exist for end-clients computing within the enterprise. Each data warehouse is different, but all are characterized by standard vital components.
Production applications such as payroll accounts payable product purchasing and inventory control are designed for online transaction processing (OLTP). Such applications gather detailed data from day to day operations.
Data Warehouse applications are designed to support the user ad-hoc data requirements, an activity recently dubbed online analytical processing (OLAP). These include applications such as forecasting, profiling, summary reporting, and trend analysis.
Production databases are updated continuously by either by hand or via OLTP applications. In contrast, a warehouse database is updated from operational systems periodically, usually during off-hours. As OLTP data accumulates in production databases, it is regularly extracted, filtered, and then loaded into a dedicated warehouse server that is accessible to users. As the warehouse is populated, it must be restructured tables de-normalized, data cleansed of errors and redundancies and new fields and keys added to reflect the needs to the user for sorting, combining, and summarizing data.
Data warehouses and their architectures very depending upon the elements of an organization's situation.
Three common architectures are:
- Data Warehouse Architecture: Basic
- Data Warehouse Architecture: With Staging Area
- Data Warehouse Architecture: With Staging Area and Data Marts
Data Warehouse Architecture: Basic
An operational system is a method used in data warehousing to refer to a system that is used to process the day-to-day transactions of an organization.
A Flat file system is a system of files in which transactional data is stored, and every file in the system must have a different name.
A set of data that defines and gives information about other data.
Meta Data used in Data Warehouse for a variety of purpose, including:
Meta Data summarizes necessary information about data, which can make finding and work with particular instances of data more accessible. For example, author, data build, and data changed, and file size are examples of very basic document metadata.
Metadata is used to direct a query to the most appropriate data source.
Lightly and highly summarized data
The area of the data warehouse saves all the predefined lightly and highly summarized (aggregated) data generated by the warehouse manager.
The goals of the summarized information are to speed up query performance. The summarized record is updated continuously as new information is loaded into the warehouse.
End-User access Tools
The principal purpose of a data warehouse is to provide information to the business managers for strategic decision-making. These customers interact with the warehouse using end-client access tools.
The examples of some of the end-user access tools can be:
- Reporting and Query Tools
- Application Development Tools
- Executive Information Systems Tools
- Online Analytical Processing Tools
- Data Mining Tools