Data Warehousing Overview

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a presentation about brief introduction to the concept of data warehouse

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Data Warehousing Overview

  1. 1. data warehousing overviewGood decisions by effectively managing data<br />Presented to : Eng. ShahinazAzab<br /> Presented by : Ahmed Gamal Mohammed <br />SWE2 – ITInc .<br />Intake 30 <br />2009/2010<br />
  2. 2. Outline<br />Forethought<br />What is Data Warehousing ? <br />Architecture of Data Warehousing <br />Data warehousing methodologies<br />Advantages of using Data Warehousing<br />Disadvantages of using Data Warehousing<br />Conclusion <br />2<br />
  3. 3. Forethought<br />“Today every company is an information company but not all are prepared to deal with it.“<br />Mark Lahr – 3M Corp<br />"The CEO will always get good data, but the challenge is making it available to the masses. That’s the challenge, how do you democratize decision-making?" <br />Eric Berg, chief administrative officer and former CIO-Goodyear.<br />3<br />
  4. 4. What is date warehouse ?<br />“collection of data that is used primarily in organizational decision making.”<br />-- W.H. Inmon, credited with initially using the term Data Warehouse, 1992<br />4<br />
  5. 5. Also means `<br />A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management's decision making process.<br />It is :- <br />Subject-Oriented.<br />Integrated.<br />Time-Variant.<br />Non-volatile.<br />5<br />
  6. 6. Architecture of Data Warehousing <br />Operational database layer<br />The source data for the data warehouse <br />Data access layer<br />The interface between the operational and informational access layer<br />Metadata layer<br />The data directory - This is usually more detailed than an operational system data directory. <br />Informational access layer<br />The data accessed for reporting and analyzing and the tools for reporting and analyzing data <br />6<br />
  7. 7. Typical DW Architecture<br />Data Store<br />Data Access<br />Data Sources<br />ETL<br />Presentation<br />Dashboards<br />The Data <br />Warehouse<br />System A<br />Prompted Views<br />System B<br />Scorecards<br />System C<br />Extract<br />Transform<br />Load<br />Business Model<br />Ad-Hoc Reporting<br />System D<br />Self Serve<br />7<br />
  8. 8. Data warehousing methodologies<br />Bottom-up design<br />Ralph Kimball, a well-known author on data warehousing, is a proponent of an approach to data warehouse design<br />In this approach<br />data marts are first created to provide reporting and analytical capabilities for specific business processes.<br />Top-down design<br />Bill Inmon, is one of the leading proponents of the top-down approach to data warehouse design.<br />In this approach <br /> data warehouse is designed using a normalized enterprise data model. "Atomic" data, that is, data at the lowest level of detail, are stored in the data warehouse.<br />8<br />
  9. 9. Advantagesof using Data warehousing<br />Prior to loading data into the data warehouse, inconsistencies are identified and resolved. This greatly simplifies reporting and analysis.<br />Because they are separate from operational systems, data warehouses provide retrieval of data without slowing down operational systems.<br />9<br />
  10. 10. Disadvantages of using data warehousing <br />Data warehouses are not the optimal environment for unstructured data.<br />Because data must be extracted, transformed and loaded into the warehouse, there is an element of latency in data warehouse data.<br />10<br />
  11. 11. Conclusion <br />11<br />Implementing a Data Warehouse is not a project, but a long term commitment to implement continuously improving business intelligence practices…<br />
  12. 12. 12<br />Thank you for your listening <br />At the end<br />

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