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Summary Of: Data warehouse
This classic definition of the data warehouse focuses on data storage... Some of the benefits that a data warehouse provides are as follows... A data warehouse provides a common data model for all data of interest regardless of the data... Information in the data warehouse is under the control of data warehouse users so that... One possible simple conceptualization of a data warehouse architecture consists of the following interconnected layers... advantage of a dimensional approach is that the data warehouse is easier for the user to understand and to use... the retrieval of data from the data warehouse tends to operate very quickly... loading the data warehouse with data from different operational systems is complicated... It is difficult to modify the data warehouse structure if the organization adopting the dimensional approach changes the way in which it does... the data in the data warehouse are stored following... Another important decision in designing a data warehouse is which data to conform and how to conform the data... much of the work in implementing a data warehouse is devoted to making similar meaning data consistent when they are stored in the data... Maintaining tight management over the data warehouse bus architecture is fundamental to maintaining the integrity of the data warehouse... has defined a data warehouse as a centralized repository for the entire enterprise... in which the data warehouse is designed using a normalized enterprise data model... In the Inmon vision the data warehouse is at the center of the... The data in the data warehouse is organized so that all the data elements relating to the same real... The changes to the data in the data warehouse are tracked and recorded so that reports can be produced showing changes over time... Data in the data warehouse is never over... The data warehouse contains data from most or all of an organization... data marts against the data stored in the data warehouse is a relatively simple task... front cost for implementing a data warehouse using the top... down data warehouse design that both methodologies have benefits and risks... data warehouse data are often stored multiple times... Data warehouse data are gathered from the operational systems and held in the data warehouse even after... The following general stages of use of the data warehouse can be distinguished... operational systems on a regular basis and the data warehouse data is stored in a data structure designed to facilitate reporting... The data warehouse is usually not static... Another prediction is that data warehouse performance will continue to be improved by use of... Encyclodia Page On: Data warehouse
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| citations | verification | reliable references | challenged | business intelligence tools | extract, transform, and load | metadata | transaction processing | operational systems | customer relationship management | ERP | Business intelligence | extract, transform, load | data structure | extract, transform, load | Ralph Kimball | data marts | business processes | data marts | Bill Inmon | data integrity | database normalization | entity-relationship model | Codd | data normalization | Relational databases | denormalised | dimension-based model | decision support | legacy systems | data marts | Teradata | Bill Inmon | Ralph Kimball | disruptive | Service Oriented Architecture | Software as a Service | in memory | Visualization | data warehouse appliances | Thomas Davenport | analytics | Business Intelligence | Business intelligence tools | Data integration | Data mart | Data mining | Data mining agent | Data warehouse appliance | Database Management System (DBMS) | Decision support | Executive Information System (EIS) | Extract, transform, and load (ETL) | Master Data Management (MDM) | On Line Analytical Processing (OLAP) | Online transaction processing (OLTP) | Operational Data Store (ODS) | Screen scraping | Snowflake schema | Star schema | July 7 | 1998 | ISBN 978-972-618-479-9 | ISBN 0-471-20024-7 | July 6 | 2007 | ISBN 1-422-10332-3 | v | Data Warehousing | Dimensions | Dimension table | Dimensional modeling | Operational data store | Database | Surrogate key | Slowly changing dimension | Snowflake schema | Star schema | Extract, transform, load | Extraction | Transformation | Loading | Business Objects | Cognos | Datastage | Informatica | SAS System | v | d | Database management systems | Database models | Database normalization | Database storage | Distributed DBMS | Referential integrity | Relational algebra | Relational calculus | Relational database | Relational DBMS | Relational model | Object-relational database | Transaction processing | Database | ACID | CRUD | Null | Candidate key | Foreign key | Primary key | Superkey | Surrogate key | Trigger | View | Table | Cursor | Log | Transaction | Index | Stored procedure | Partition | SQL | Select | Insert | Update | Merge | Delete | Join | Union | Create | Drop | Begin work | Commit | Rollback | Truncate | Alter | XSQL | Relational | Flat file | Deductive | Dimensional | Hierarchical | Network | Document-oriented | Object-oriented | Object-relational | Temporal | XML data stores | Triple stores | Concurrency control | Data dictionary | JDBC | ODBC | Query language | Query optimizer | Query plan | Object-oriented | comparison | Relational | comparison | Document-oriented | Categories | Database management systems | Business intelligence | Data management | Data warehousing | Information technology management | Articles needing additional references from February 2008 | |
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