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Business Intelligence and Decision Support Systems (9 th Ed., Prentice Hall)

Business Intelligence and Decision Support Systems (9 th Ed., Prentice Hall). Chapter 8: Data Warehousing. Learning Objectives. Understand the basic definitions and concepts of data warehouses

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Business Intelligence and Decision Support Systems (9 th Ed., Prentice Hall)

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  1. Business Intelligence and Decision Support Systems(9th Ed., Prentice Hall) Chapter 8: Data Warehousing

  2. Learning Objectives • Understand the basic definitions and concepts of data warehouses • Learn different types of data warehousing architectures; their comparative advantages and disadvantages • Describe the processes used in developing and managing data warehouses • Explain data warehousing operations • Explain the role of data warehouses in decision support

  3. Learning Objectives • Explain data integration and the extraction, transformation, and load (ETL) processes • Describe real-time (a.k.a. right-time and/or active) data warehousing • Understand data warehouse administration and security issues

  4. 8.1. Opening Vignette: “DirecTV Thrives with Active Data Warehousing” • Company background • Problem description • Proposed solution • Results • Answer & discuss the case questions.

  5. Opening Vignette:

  6. Main Data Warehousing (DW) Topics • DW definitions • Characteristics of DW • Data Marts • ODS, EDW, Metadata • DW Framework • DW Architecture & ETL Process • DW Development • DW Issues

  7. 8.2. Data Warehouse Defined • A physicalrepository where relational data are specially organized to provide enterprise-wide, cleansed data in a standardized format • “The data warehouse is a collection of integrated, subject-oriented databases design to support DSS functions, where each unit of data is non-volatile and relevant to some moment in time”

  8. Data Warehouse Defined

  9. Characteristics of DW • Subject oriented • Integrated • Time-variant (time series) • Nonvolatile • Summarized • Not normalized • Metadata • Web based, relational/multi-dimensional • Client/server • Real-time and/or right-time (active)

  10. Characteristics of DW • Subject oriented • Integrated • Time-variant (time series) • Nonvolatile • Summarized • Not normalized • Metadata • Web based, relational/multi-dimensional • Client/server • Real-time and/or right-time (active)

  11. Characteristics of DW • Subject oriented • Integrated • Time-variant (time series) • Nonvolatile • Summarized • Not normalized • Metadata • Web based, relational/multi-dimensional • Client/server • Real-time and/or right-time (active)

  12. Characteristics of DW • Subject oriented • Integrated • Time-variant (time series) • Nonvolatile • Summarized • Not normalized • Metadata • Web based, relational/multi-dimensional • Client/server • Real-time and/or right-time (active)

  13. Characteristics of DW • Subject oriented • Integrated • Time-variant (time series) • Nonvolatile • Summarized • Not normalized • Metadata • Web based, relational/multi-dimensional • Client/server • Real-time and/or right-time (active)

  14. Characteristics of DW • Subject oriented • Integrated • Time-variant (time series) • Nonvolatile • Summarized • Not normalized • Metadata • Web based, relational/multi-dimensional • Client/server • Real-time and/or right-time (active)

  15. Characteristics of DW • Subject oriented • Integrated • Time-variant (time series) • Nonvolatile • Summarized • Not normalized • Metadata • Web based, relational/multi-dimensional • Client/server • Real-time and/or right-time (active)

  16. Characteristics of DW • Subject oriented • Integrated • Time-variant (time series) • Nonvolatile • Summarized • Not normalized • Metadata • Web based, relational/multi-dimensional • Client/server • Real-time and/or right-time (active)

  17. Characteristics of DW • Subject oriented • Integrated • Time-variant (time series) • Nonvolatile • Summarized • Not normalized • Metadata • Web based, relational/multi-dimensional • Client/server • Real-time and/or right-time (active)

  18. Characteristics of DW • Subject oriented • Integrated • Time-variant (time series) • Nonvolatile • Summarized • Not normalized • Metadata • Web based, relational/multi-dimensional • Client/server • Real-time and/or right-time (active)

  19. Characteristics of DW • Subject oriented • Integrated • Time-variant (time series) • Nonvolatile • Summarized • Not normalized • Metadata • Web based, relational/multi-dimensional • Client/server • Real-time and/or right-time (active)

  20. Data warehousing: Process

  21. Data Mart A departmental data warehouse that stores only relevant data • Dependent data mart A subset that is created directly from a data warehouse • Independent data mart A small data warehouse designed for a strategic business unit or a department

  22. Data Mart

  23. Dependent/Independant data mart

  24. Data Warehousing Definitions • Operational data stores (ODS) A type of database often used as an interim area for a data warehouse • Operational marts An operational data mart. • Enterprise data warehouse (EDW) A data warehouse for the enterprise. • Metadata Data about data. In a data warehouse, metadata describe the contents of a data warehouse and the manner of its acquisition and use

  25. 8.3. Data warehousing process overview

  26. A Conceptual Framework for DW

  27. 8.4. Generic DW Architectures • Three-tier architecture • Data acquisition software (back-end) • The data warehouse that contains the data & software • Client (front-end) software that allows users to access and analyze data from the warehouse • Two-tier architecture First 2 tiers in three-tier architecture is combined into one … sometime there is only one tier?

  28. Generic DWArchitectures 3-tier architecture 1-tier Architecture ? 2-tier architecture

  29. DW Architecture Considerations • Issues to consider when deciding which architecture to use: • Which database management system (DBMS) should be used? • Will parallel processing and/or partitioning be used? • Will data migration tools be used to load the data warehouse? • What tools will be used to support data retrieval and analysis?

  30. A Web-based DW Architecture

  31. Alternative DW Architectures

  32. Alternative DW Architectures

  33. Which Architecture is the Best? • Bill Inmon versus Ralph Kimball • Enterprise DW versus Data Marts approach Empirical study by Ariyachandra and Watson (2006)

  34. Information interdependence between organizational units Upper management’s information needs Urgency of need for a data warehouse Nature of end-user tasks Constraints on resources Strategic view of the data warehouse prior to implementation Compatibility with existing systems Perceived ability of the in-house IT staff Technical issues Social/political factors Data Warehousing Architectures Ten factors that potentially affect the architecture selection decision:

  35. Enterprise Data Warehouse(by Teradata Corporation)

  36. Teradata Corporation

  37. 8.5. Data Integration and the Extraction, Transformation, and Load (ETL) Process • Data integration Integration that comprises three major processes: data access, data federation, and change capture. • Enterprise application integration (EAI) A technology thatprovides a vehicle for pushing data from source systems into a data warehouse • Enterprise information integration (EII) An evolving tool space that promises real-time data integration from a variety of sources • Service-oriented architecture (SOA) A new way of integrating information systems

  38. Data Integration and the Extraction, Transformation, and Load (ETL) Process Extraction, transformation, and load (ETL) process

  39. ETL • Issues affecting the purchase of and ETL tool • Data transformation tools are expensive • Data transformation tools may have a long learning curve • Important criteria in selecting an ETL tool • Ability to read from and write to an unlimited number of data sources/architectures • Automatic capturing and delivery of metadata • A history of conforming to open standards • An easy-to-use interface for the developer and the functional user

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