Latest News

Wednesday, May 4, 2016

Bill Inmon Data Warehouse Architecture

Corporate Information Factory or Bill Inmon Data Warehouse Architecture is mostly based on EDW (Enterprise Data Warehouse ) concept.


Bill Inmon Data Warehouse Architecture


  1. Corporate Applications such as operational systems or transaction systems, that are used to support business. Transaction systems are used to collect data from business transactions such as sales, marketing, material managements…..etc and stored those data in various forms including relational data, hierarchical data or even spreadsheets. In Inmon’s architecture, transaction systems are also called source systems that provide data to the data warehouse.
  2. ETL Processes. To bring data from the transaction system, a process called ETL is used. ETL stands for extract, transform and load. ETL process consolidates data, transform it into a specific standard format and load it into a single repository called enterprise data warehouse, or EDW. ETL processes can run as a batch process periodically or a transaction-based for near real-time data. ETL process is referred as data integration or data services.
  3. Enterprise data warehouse is a central element in the Inmon’s data warehouse architecture. As Inmon’s data warehouse definition, enterprise data warehouse is an integrated repository of atomic data. Data in the enterprise data warehouse is captured at a very lowest level of detail. Data in the enterprise data warehouse is stored in relational database and uses third normal database design.
  4. Data marts are departmental views of information with subject oriented data. Data marts take data from enterprise data warehouse. Aggregations can take place when data brings from enterprise data warehouse to data marts. Data marts use dimensional design, therefore, the data in the data marts is ready for analysis. It is important to note that all the external applications or reporting tools or business intelligence tools query data from data marts instead of enterprise data warehouse directly.

  • Google+
  • Pinterest
« PREV
NEXT »

No comments

Post a Comment