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Inmon & kimball method

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Data Warehouse and Business Intelligence

Text of Inmon & kimball method

  • 1. DATA WAREHOUSE KIMBALL OR INMON Understanding different approach

2. Understand the basics of 2 approaches Enterprise Data Warehouse Dimensional Modelling and Design Understand the Similarities and Differences 3. In 1990 Inmon wrote a book Building the Data Warehouse Inmon defines architecture for collection of disparate sources into detailed, time variant data store( The top down approach) In 1996 Kimball wrote The Data Warehouse Toolkit Kimball updates book and defines multiple databases called data-marts that are organized by business processes, but useData bus architecture (The bottom-up approach) 4. A Data warehouse is a collection of Enterprise wide data across line of businessand subject areas Data is integrated using a massive database Provides complete organizational view of the information needed to run the business A Data mart provides departmental view of information specific and subject oriented Build multiple data-marts using dimensional architecture Provides Fact based information integrated with multiple dimensions 5. Data WarehouseData MartsScope Application independent Centralized or Enterprise Planned Specific application Decentralized by group Organic but may be plannedData Historical, detailed, summary Some de-normalization Some history, detailed, summary High de-normalizationSubjects Multiple subjects Single central subject areaSource Many internal and external sources Few internal and external sourcesPros & Cons Flexible Data oriented Long life Single complex structure Restrictive Project oriented Short life Multiple simple structures that may form a complex structure 6. Bill Inmon: A data warehouse is a subject-oriented and the data in the database isorganized with data elements relating and linking together. Time-variant: The changes to the data in the database are tracked and recorded showing changes over time; Non-volatile: Data in the database is never over-written or deleted once committed, the data is static, read-only, but retained for future Database: The database contains data from all operational applications, and that this data is made consistent the data warehouse should be designed from the top-down to include all corporate data. In this methodology, data marts are created only after the complete data warehouse has been created. 7. Ralph Kimball: A proponent of the dimensional modelling and approach tobuilding data warehouse through data marts. The data warehouse is nothing more than the union of all the data-marts, Kimball indicates a bottom-up approach for data warehousing Individual data marts are created providing views into the organizational data in chunks Eventually an Enterprise Data warehouse is create by combining the data marts together using Bus architecture. 8. INMONKIMBALLThe warehouse is a part of Corporate information factory consists of all Data bases.Fact and Dimensions using Dimensional modellingDefines database environment as Operational: Day to day operations Atomic: Transaction captured Departmental: Focused Individual: Ad-hocMetrics or facts and Dimension with attributesERD refines entities, attributes and relationshipsBus architectureData Items sets and Data sets by department Physical modelling to optimize performance by de-normalizingDoes not adhere to normalization theorySubject-Oriented, Integrated, Non-Volatile Time-Variant, Top-Down, Enterprise Data Model Characterizes Data marts as AggregatesBusiness-Process-Oriented, Bottom-Up , Dimensional Model, Integration Achieved via Conformed Dimensions, Star Model 9. DB2Data warehouseDW DWORACLESTAGING AREA Flat files CUBE SAPUSER ACCESS 10. DB2Data warehouseDWORACLESTAGING AREA Flat filesSAPDWCUBEUSER ACCESS 11. REQUIREMENTSINMONKIMBALLOrganization requirementsStrategicTacticalData IntegrationEnterpriseDepartmentalStructureNon metric data, meets multiple varied information needsBusiness metrics , KPIs, ScorecardsScalabilityChange of Scope and requirementsLimited scope and volatile needs 12. REQUIREMENTSINMONKIMBALLData StabilitySource systems changes frequently Stable source systemsStaff requirementLargeSmallDeliverySlow and LongQuick turnaroundCostLow upfront costHigh expenditure