12/6/05 The Data Warehouse from William H. Inmon, Building the Data Warehouse (4 th ed)

12/6/05 Data Warehouse = architecture (not a technology) architecture (not a technology) example of Decision Support System

12/6/05 Data Placement  DSS - Decision Support Systems (analytical function)  OLTP – Online Transactional Processing (operational function)  Archival data – cheaper/slower storage

12/6/05 OLTP DSS  primitive data  operational  day-to-day  clerical function  non-redundant  non-integrated  run repetitively  derived data  analytical  historical  managerial function  redundant  data integrated  run heuristically

12/6/05 A Definition: “A data warehouse is a subject- oriented, integrated, non-volatile, and time-variant collection of data in support of management’s decisions.” (a sophisticated series of snapshots…)

12/6/05 Design Decisions  Granularity - level of detail or summarization of the units of data in the data warehouse (more detail = lower level of granularity)  Partitioning – breakup of data into separate physical units that can be handled independently

12/6/05

Major Components  Design of Data Warehouse itself  Interface from operational systems -role of extract (ETL) software [Extract/Transform/Load] -element of time (compound keys) -data purging

12/6/05 Indirect Use of Data Warehouse Data  An analysis program periodically spins off a file to the operational environment that includes specific summarized data  Airline commission example  Retail personalization example  Credit scoring example

12/6/05 Data Warehouse Requirements  Manage large amounts of data  Manage data on diverse media  Easily index and monitor  Interface with varying technologies  Store and access data in parallel  Metadata control (by “user”)  Contextual information (vs content)  Efficiently use indexes  Support compound keys