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Data Warehouses and OLAP Data Management

Data Warehouses and OLAP Data Management. Dennis Volemi D61/70384/2009 Judy Mwangoe D61/73260/2009 Jeremy Ndirangu D61/75216/2009. Scope. Introduction Define Data Warehouses Define OLAP and Differentiate with OLTP Benefits of Data warehouses.

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Data Warehouses and OLAP Data Management

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  1. Data Warehouses and OLAPData Management Dennis Volemi D61/70384/2009 Judy Mwangoe D61/73260/2009 Jeremy Ndirangu D61/75216/2009

  2. Scope • Introduction • Define Data Warehouses • Define OLAP and Differentiate with OLTP • Benefits of Data warehouses. • Disadvantages of Data Warehouses • Typical Data Warehouse Architecture • Leading Industry Data warehouses and OLAP • Future Trends in Data warehousing • Open Discussion

  3. <Insert Picture Here> Introduction

  4. Definitions of Data Warehouses • Collection of diverse data and decision support technologies • subject oriented, aimed at executive and decision makers, often a copy of operational data, with value-added data (e.g., summaries, history), integrated and time-varying • Collection of tools • gathering data; cleansing, integrating; querying, reporting, analysis; data mining; monitoring, administering warehouse

  5. OLAP vs OLTP • OLAP – Online Analytical Processing • Describes processing at warehouse, Typically, the data warehouse is maintained separately from the organization’s operational databases. There are many reasons for doing this. The data warehouse supports on-line analytical processing (OLAP). • OLTP – Online Transaction Processing • OLTP applications typically automate clerical data processing tasks such as order entry and banking transactions that are the bread-and-butter day-to-day operations of an organization.

  6. <Insert Picture Here> Why Data Warehousing

  7. Benefits of data warehouses • A data warehouse provides a common data model for all data of interest regardless of the data's source. This makes it easier to report and analyze information than it would be if multiple data models were used to retrieve information such as sales invoices, order receipts, general ledger charges, etc. • Prior to loading data into the data warehouse, inconsistencies are identified and resolved. This greatly simplifies reporting and analysis. • Information in the data warehouse is under the control of data warehouse users so that, even if the source system data is purged over time, the information in the warehouse can be stored safely for extended periods of time.

  8. Benefits of Data Warehouses contd. • Because they are separate from operational systems, data warehouses provide retrieval of data without slowing down operational systems. • Data warehouses can work in conjunction with and, hence, enhance the value of operational business applications, i.e. ERP’s, CRM’s , Billing etc • Data warehouses facilitate decision support system applications such as trend reports (e.g., the items with the most sales in a particular area within the last two years), exception reports, and reports that show actual performance versus goals. Data Wareshouses and OLAP

  9. Disadvantages of Data warehouses • Data warehouses are not the optimal environment for unstructured data. • Because data must be extracted, transformed and loaded into the warehouse, there is an element of latency in data warehouse data. • Over their life, data warehouses can have high costs. • Data warehouses can get outdated relatively quickly. There is a cost of delivering suboptimal information to the organization. • There is often a fine line between data warehouses and operational systems. Duplicate, expensive functionality may be developed. Or, functionality may be developed in the data warehouse that, in retrospect, should have been developed in the operational systems. Data Wareshouses and OLAP

  10. Typical Architecture of a Data Warehouse • Extracting data from multiple operational databases and • external sources. • Integration (cleaning, transforming and loading the data). • Presentment in multidimensional views of data to a variety of front end tools: query tools, report writers, analysis tools, and data mining tools. • Finally, there is a repository for storing and managing metadata, and tools for monitoring and administering • the warehousing system.

  11. Typical Architecture of a Data Warehouse Source: An Overview of DataWarehousing and OLAPTechnology by Surajit Chaudhuri& Umeshwar Dayal

  12. Leading Industry Data Warehouses • Oracle – Oracle BI product Suite • IBM – Cognos • SAP – Business Objects • Microsoft – BI ( Share-point, Office 2010 and SQL Server 2008 Release 2)

  13. Future Trends in Data Warehousing • Data warehouse in the cloud (Embracing SaaS) • Open Source growth and influence. • Use of Data Warehouses outside Business Units e.g. Social Marketing, in Government etc

  14. Open Discussion Oracle Communications

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