Inmon & kimball method

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Data Warehouse and Business Intelligence

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Inmon & kimball method

  1. 1. DATA WAREHOUSE KIMBALL OR INMON Understanding different approach
  2. 2. Understand the basics of 2 approaches Enterprise Data Warehouse Dimensional Modelling and Design Understand the Similarities and Differences
  3. 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 use Data bus architecture (The bottom-up approach)
  4. 4.  A Data warehouse is a collection of Enterprise wide data across line of business and 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. 5. Data Warehouse Data Marts Scope • Application independent • Centralized or Enterprise • Planned • Specific application • Decentralized by group • Organic but may be planned Data • Historical, detailed, summary • Some de-normalization • Some history, detailed, summary • High de-normalization Subjects • Multiple subjects • Single central subject area Source • Many internal and external sources • Few internal and external sources Pros & Cons • • • • Flexible Data oriented Long life Single complex structure • • • • • Restrictive Project oriented Short life Multiple simple structures that may form a complex structure
  6. 6.  Bill Inmon: A data warehouse is a subject-oriented and the data in the database is organized 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. 7.  Ralph Kimball: A proponent of the dimensional modelling and approach to building 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. 8. INMON KIMBALL The warehouse is a part of Corporate information factory consists of all Data bases. Fact and Dimensions using Dimensional modelling Defines database environment as Operational: Day to day operations Atomic: Transaction captured Departmental: Focused Individual: Ad-hoc Metrics or facts and Dimension with attributes ERD refines entities, attributes and relationships Bus architecture Data Items sets and Data sets by department Physical modelling to optimize performance by de-normalizing Does not adhere to normalization theory Subject-Oriented, Integrated, Non-Volatile Time-Variant, Top-Down, Enterprise Data Model Characterizes Data marts as Aggregates Business-Process-Oriented, Bottom-Up , Dimensional Model, Integration Achieved via Conformed Dimensions, Star Model
  9. 9. DB2 Data warehouse DW DW ORACLE STAGING AREA Flat files CUBE SAP USER ACCESS
  10. 10. DB2 Data warehouse DW ORACLE STAGING AREA Flat files SAP DW CUBE USER ACCESS
  11. 11. REQUIREMENTS INMON KIMBALL Organization requirements Strategic Tactical Data Integration Enterprise Departmental Structure Non metric data, meets multiple varied information needs Business metrics , KPI’s, Scorecards Scalability Change of Scope and requirements Limited scope and volatile needs
  12. 12. REQUIREMENTS INMON KIMBALL Data Stability Source systems changes frequently Stable source systems Staff requirement Large Small Delivery Slow and Long Quick turnaround Cost Low upfront cost High expenditure

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