1. An overview of Data Warehousing and OLAP Technology Presented By Manish Desai

2. 2 Introduction What is data warehouse ? Explanation of definition Data warehouse Vs. Operational Database Data warehouse architecture Back end tools Conceptual model Database design Warehouse servers Index structures Meta data Conclusion References

3. 3 Introduction Essential elements of decision support Enables The Knowledge Worker to make better and faster decisions Used in many industries like: –Manufacturing (for order shipment) –Retail (for inventory management) –Financial Services (claims and risk analysis) Every major database vendor offers product in this area

4. 4 What is Data Warehouse ? A data warehouse is a subject-oriented, integrated, time-varying, non-volatile collection of data that is used primarily in organizational decision making Typically maintained separately from operational databases

5. 5 Explanation of definition Subject-Oriented: –Designed around subject such as customer, vendor, product and activity –Does not includes data that are not needed for Decision support system (DSS) Integrated: –Most important feature –Consistent naming convention, measurement of variables and so forth –The data should be stored in single globally acceptable fashion

6. 6 Explanation (continues…) Time Varying: –All data in the warehouse should be accurate as of some moment in time –Data stored over a long time horizon (5 –10 years) –Key structure contains element of time (implicitly or explicitly) –Data once correctly recorded cant be updated Non Volatile: –No Update of data allowed – only loading and access of data operations

7. 7 Data Warehouse Vs. Operational Database Data WarehouseOperational Database userKnowledge workerClerk, IT professional FunctionDecision supportDay to day operations DataHistorical,summarized, multidimensional, integrated Current, up-to-date, detailed Unit of workComplex queryShort, simple transaction metricQuery throughout, response Transaction throughput

8. 8 Architecture Data sourcing,migration,cleanup tools Meta data repository Data marts Data query, reporting, analysis and mining tools Data warehouse administration and management

9. 9 Architecture (continues…) Distributed Data warehouse –Load balancing, scalability,higher availability –Meta data replicated and centrally administrated –Too expansive Data marts –Departmental subset focused on selected subjects –example: marketing department includes customer, sales and product tabels –Has own repository and administration –May lead to complex integration problems if not designed properly

10. 10 Back end tools and Utilities Data cleaning, loading, refreshing tools Cleaning –Multiple source, possibility of errors –Example: replace string sex by gender Loading –Building indices, sorting and making access paths –Large amount of data Incremental loading Only updated tuples are inserted,Process hard to manage Refresh –Propagating updates –When to refresh ? –Set by administrator depending on user needs and traffic

11. 11 Conceptual Model and front end tools Multi dimensional view –Dimensions together uniquely determine the measure –Example: Sales can be represented as city,product, data –Each dimension is described by set of attribute –Example: product consist of Category of product Industry of product Year of introduction Front end tools –Multi dimensional spreadsheet Supports Pivoting-reorientation Roll_up - summarized data Drill_down - go from high level to low level summary

12. 12 Database design Two ways to represent Multi dimensional model –Star schema Database consist of single fact table and single table for each dimension Each tuples in fact table consist of pointer to each of dimension –Snowflake schema Refinement over star schema Dimensional hierarchy is explicitly represented by normalizing dimension tables

13. 13 Warehouse Servers Specialized SQL servers –Provides advanced query language and query processing support for SQL queries over star and snowflake schemas –Example: Redbrick ROLAP –Between relational back end and client front end tools –Extend traditional relational servers to support multidimensional queries –Example: Microstratergy MOLAP –Multidimensional storage engine –Direct mapping –Example: Essbase from Arbor Inc.

14. 14 Index structures Bit map indices –Use single bit to indicate specific value of attribute –Example: instead of storing eight characters to record engineer as skill of employee use single bit id# Name Skill 1000 John 1 Join indices –Maintains the relationship between foreign key with its matching primary keys

15. 15 Meta data and warehouse management Its data about data Used for building, maintain, managing and using data warehouse Administrative meta data –Information about setting up and using warehouse Business meta data –Business terms and definition Operational meta data –Information collected during operation of warehouse

16. 16 Conclusion Data warehouse is the technology for the future. data warehouse enables knowledge worker to make faster and better decisions

17. 17 References Inmon W. H.,Building the data warehouse www.olapcouncil.org www.pwp.starnetinc.com www.arborsoft.com Kimball, R. The data warehouse toolkit.