1. Presented By: Pedel Oppong-Abebrese,Pedel Oppong-Abebrese Michael Boadi, William Osei, Nana Amoa OforiMichael BoadiWilliam OseiNana Amoa Ofori DATA WAREHOUSING

2. Introduction Data warehouse provide access to data for complex analysis, knowledge, discovery and decision making. It is usually confused with the term database as vendors have capitalized on the popularity of the term.

3. Traditional database vs. Data warehouse Traditional database are transactional(ie relational, object- oriented or network). Data warehouse is mainly intended for decision making Thus they support high performance demands on an organization’s data

4. Types of Applications supported OLAP (Online Analytical Processing): analysis of complex data DSS(Decision support systems) : support decision making with facts and figures.

5. Characteristic of Data warehouse Should be a store of information from different sources It should be non-volatile Client server architecture Accessible Intuitive data manipulation Multi-user support

6. Data Modeling for Data Warehouses

7. Data modelling for data warehousing This takes advantage of inherent relationships in data to populate data in multidimensional matrices called cubes. Hypercubes = More than 3 dimensional If data makes use of dimensional formatting, query performance in multi dimensional matrices, it can be much better than in relational data model. Illustrations: A two dimensional | three dimensional |Pivoting | etc. NB: Tools exist for viewing data according to the user’s choice of dimensions.

8. Data modelling for data warehousing Multidimensional models avail themselves to hierarchical views known as Roll up and Drill down displays. Roll up: Moves up the hierarchy, grouping into larger units along a dimension. Eg: Individual products could be grouped into product categories. Illustration Drill down: Opposite of roll up. Breaks down the hierarchy. Eg: Regions => Sub regions | Products => product styles. Illustration

9. Multi Dimensional Schemas There exits two of such schema: Star schema: A fact table connected to a set of dimension tables. Illustrate Snowflake: A variation of the star schema with some dimensional hierarchy normalised into a set of smaller dimension tables. Illustrate NB: Some installations are normalising data ware houses up to third normal form to enhance access to the ware house. Fact constellation: Has multiple fact tables sharing a dimension table. Illustrate

10. Establishing A Data Warehouse

11. Steps for acquiring data for a data warehouse  Extract Data from sources.  Format for consistency.  Ensure validity of data (Data Cleaning).  Fit data into the chosen model for the warehouse (multidimensional model).  Load Data into the warehouse.

12. Typical Processes in a Data Warehouse  Storing data according to the data model  Creating and maintaining required data structures  Creating and maintaining appropriate access paths  Support updates  Refreshing data  Purging data

13. Other key design components  Usage projections  Modular Design.  Metadata repository  Technical metadata  Details of acquisition processing, storage structures, data descriptions, access to support functionality etc.  Business metadata  Relevant business rules and organizational details supporting the warehouse

14.  Architecture  Distributed architecture  Will house a single replicated metadata repository at each distribution site.  But issues with: replication, communication, consistency and partitioning.  Federated warehouse  Decentralized confederation of autonomous data warehouses  One metadata repository for each unit  Would usually be made up of smaller “data marts”

15. Typical Functionality of a Data Warehouse

16. Functionality Roll up Drill-down Pivot Slice & Dice Sorting Selection ROLAP/ MOLAP / HOLAP Parallel processing: SMP, MPP

18. PROBLEMS & OPEN ISSUES IN DATA WAREHOUSES

19. Main difficulties of Implementing data warehouses Construction: The design, construction and implementation of the warehouse should not be underestimated. The larger the enterprise, the more sophisticated its construction would be. Administration: Again, this is proportional to the size and complexity of the warehouse. Highly skilled personnel would function effectively in this area because the administration of the data warehouse has a wider scope and thus would require far broader skills. Quality control: Ensuring data meets all the standards prescribed by the organisation or administrator. Quality and consistency is a key issue that is significant to the database administrator. Usage projections should be estimated prior to construction to minimize the number of anomalies that may occur. The warehouse should support random changes without major redesign Design of mgt: Structuring mgt function and selection of a management team is crucial. Larger organisation (Lo) = Major task (Mt) Effective management would certainly be a team function, requiring a wide set of technical skills, careful coordination, and effective leadership.

20. Open Issues in Data Warehousing As data marts and warehouses proliferate, research activities in this area is likely to increase. Old problems would get new emphasis. Eg. Data cleaning, indexing etc Automation of certain aspects of this area that currently require significant manual interventions. Eg. Data acquisition Commercial software is already out. Focus is mainly on management of data warehouse and OLAP /DSS applications.

21. The End - Thank you.