Data warehouse characteristics –Integrated The Data Warehouse is a centralized, consolidated database that integrates data retrieved from the entire organization. –Subject-Oriented The Data Warehouse data is arranged and optimized to provide answers to questions coming from diverse functional areas within a company. –Time Variant The Warehouse data represent the flow of data through time. It can even contain projected data. –Non-Volatile Once data enter the Data Warehouse, they are never removed. The Data Warehouse is always growing.
Twelve Rules That Define a Data Warehouse 1.The Data Warehouse and operational environments are separated. 2.The Data Warehouse data are integrated. 3.The Data Warehouse contains historical data over a long time horizon. 4.The Data Warehouse data are snapshot data captured at a given point in time. 5.The Data Warehouse data are subject-oriented. 6.The Data Warehouse data are mainly read-only with periodic batch updates from operational data. No online updates are allowed. 7.The Data Warehouse development life cycle differs from classical systems development. The Data Warehouse development is data driven; the classical approach is process driven. 8.The Data Warehouse contains data with several levels of detail; current detail data, old detail data, lightly summarized, and highly summarized data. 9.The Data Warehouse environment is characterized by read-only transactions to very large data sets. The operational environment is characterized by numerous update transactions to a few data entities at the time. 10.The Data Warehouse environment has a system that traces data resources, transformation, and storage. 11.The Data Warehouse’s metadata are a critical component of this environment. The metadata identify and define all data elements. The metadata provide the source, transformation, integration, storage, usage, relationships, and history of each data element. 12.The Data Warehouse contains a charge-back mechanism for resource usage that enforces optimal use of the data by end users.
End-user tools (BI tools) On-Line Analytical Processing (OLAP) is an advanced data analysis environment that supports decision making, business modeling, and operations research activities. Four Main Characteristics of OLAP –Use multidimensional data analysis techniques –Provide advanced database support –Provide easy-to-use end user interfaces –Support client/server architecture
Data in 3D or more….
3-D data in RDBMS
Multi-dimension data in RDBMS
Data Warehouse Implementation The Data Warehouse as an Active Decision Support Network –A Data Warehouse is a dynamic support framework. –Implementation of a Data Warehouse is part of a complete database-system-development infrastructure for company-wide decision support. –Its design and implementation must be examined in the light of the entire infrastructure.
Data Warehouse Implementation A Company-Wide Effort that Requires User Involvement and Commitment at All Levels –For a successful design and implementation, the designer must: Involve end users in the process. Secure end users’ commitment from the beginning. Create continuous end user feedback. Manage end user expectations. Establish procedures for conflict resolution.
Data Warehouse Implementation Satisfy the Trilogy: Data, Analysis, and Users –For a successful design and implementation, the designer must satisfy: Data integration and loading criteria. Data analysis capabilities with acceptable query performance. End user data analysis needs.
Data Warehouse Implementation Apply Database Design Procedures –Data Warehouse development is a company-wide effort and requires many resources: people, financial, and technical. The sheer and often mind-boggling quantity of decision support data is likely to require the latest hardware and software. It is also imperative to have very detailed procedures to orchestrate the flow of data from the operational databases to the Dare Warehouse. To implement and support the Data Warehouse architecture, we also need people with advanced database design, software integration, and management skills.
Data warehouse implementation road map
DSS vs Data mining DSS >> reactive Data mining >> Proactive
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