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Data Warehousing

Data Warehousing. Lecture-3 Introduction and Background. Introduction and Background. What is a Data Warehouse ?. It is a blend of many technologies, the basic concept being: Take all data from different operational systems. If necessary, add relevant data from industry.

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Data Warehousing

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  1. Data Warehousing Lecture-3 Introduction and Background

  2. Introduction and Background

  3. What is a Data Warehouse ? • It is a blend of many technologies, the basic concept being: • Take all data from different operational systems. • If necessary, add relevant data from industry. • Transform all data and bring into a uniform format. • Integrate all data as a single entity.

  4. What is a Data Warehouse ? (Cont…) • It is a blend of many technologies, the basic concept being: • Store data in a format supporting easy access for decision support. • Create performance enhancing indices. • Implement performance enhancement joins. • Run ad-hoc queries with low selectivity.

  5. How is it Different? ?  • Fundamentally different Business user needs info Answers result in more questions User requests IT people Business user may get answers IT people do system analysis and design IT people send reports to business user IT people create reports

  6. 100% 0% Operational DWH How is it Different? • Different patterns of hardware utilization Bus Service vs. Train

  7. How is it Different? • Don’t do data entry into a DWH, OLTP or ERP are the source systems. • OLTP systems don’t keep history, cant get balance statement more than a year old. • DWH keep historical data, even of bygone customers. Why? • In the context of bank, want to know why the customer left? • What were the events that led to his/her leaving? Why? • Customer retention. • Combines operational and historical data.

  8. How much history? • Depends on: • Industry. • Cost of storing historical data. • Economic value of historical data.

  9. How much history? • Industries and history • Telecomm calls are much much more as compared to bank transactions- 18 months. • Retailers interested in analyzing yearly seasonal patterns- 65 weeks. • Insurance companies want to do actuary analysis, use the historical data in order to predict risk- 7 years.

  10. How much history? Economic value of data Vs. Storage cost Data Warehouse a complete repository of data?

  11. How is it Different? • Usually (but not always) periodic or batch updates rather than real-time. • The boundary is blurring for active data warehousing. • For an ATM, if update not in real-time, then lot of real trouble. • DWH is for strategic decision making based on historical data. Wont hurt if transactions of last one hour/day are absent.

  12. How is it Different? • Rate of update depends on: • volume of data, • nature of business, • cost of keeping historical data, • benefit of keeping historical data.

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