Data warehouse is data management and data analysis
Goal: is to integrate enterprise wide corporate data into a single reository from which users can easily run queries
The major benefit of data warehousing are high returns on investment.
Increased productivity of corporate decision-makers
Underestimation of resources for data loading
Hidden problems with source systems
Required data not captured
Increased end-user demands
High demand for resources
Complexity of integration
Operational data sources - - > for the DW is supplied from mainframe operational data held in first generation hierarchical and network databases, departmental data held in proprietary file systems, private data held on workstaions and private serves and external systems such as the Internet, commercially available DB, or DB assoicated with and organizationâ€˜s suppliers or customers.
Operational datastore(ODS) - - > is a repository of current and integrated operational data used for analysis. It is often structured and supplied with data in the same way as the data warehouse, but may in fact simply act as a staging area for data to be moved into the warehouse.
query manager - - > also called backend component, it performs all the operations associated with the management of user queries. The operations performed by this component include directing queries to the appropriate tables and scheduling the execution of queries
end-user access tools - - > can be categorized into five main groups: data reporting and query tools, application development tools, executive information system (EIS) tools, online analytical processing (OLAP) tools, and data mining tools.
Inflow- The processes associated with the extraction, cleansing, and loading of the data from the source systems into the data warehouse.
upflow- The process associated with adding value to the data in the warehouse through summarizing, packaging , packaging, and distribution of the data.
downflow- The processes associated with archiving and backing-up of data in the warehouse.
Tools and Technologies
The critical steps in the construction of a data warehouse:
after the critical steps, loading the results into target system can be carried out either by separate products, or by a single, categories:
database data replication tools
dynamic transformation engines
For the various types of meta-data and the day-to-day operations of the data warehouse, the administration and management tools must be capable of supporting those tasks:
Monitoring data loading from multiple sources
Data quality and integrity checks
Managing and updating meta-data
Monitoring database performance to ensure efficient query response times and resource utilization
Auditing data warehouse usage to provide user chargeback information
Replicating, subsetting, and distributing data
Maintaining effient data storage management
Archiving and backing-up data
Implementing recovery following failure