1. OLAP Services Business Intelligence Solutions

2. Agenda Definition of OLAP Types of OLAP Definition of Cube Definition of DMR Differences between Cube and DMR Design Philosophy for building an efficient cube Examples Questions

3. OLAP Definition of OLAP  On-Line Analytical Processing is multi dimensional analysis of data stored in a database. Why do we need it  Easy Multi-dimensional presentation for business information and analysis Easy to use  Fast OLAP Technology is very fast – 98% of reports in less than 1-3 seconds  Powerful Calculated Columns enables calculations that are difficult using relational technology

4. OLAP OLAP applications provide the following features:  Offer high-performance access to pre-summarized data (in the form of cubes)  Give users the power to retrieve answers to multidimensional business questions quickly and easily  Provide slice-and-dice views of multiple relationships in large quantities of pre- summarized data

5. Types Of OLAP Types of OLAP In the OLAP world, there are mainly three different types to physical representation of Data Warehouse data  Multidimensional Online Analytical Process (MOLAP)  Relational Online Analytical Process (ROLAP)  Hybrid Online Analytical Process (HOLAP)

6. MOLAP Multidimensional Online Analytical Process (MOLAP):  This is the traditional mode in OLAP analysis.  In MOLAP data is stored in form of multidimensional cubes and not in relational databases.  It provides excellent query performance and the cubes are built for fast data retrieval.  All calculations are pre-generated when the cube is created and can be easily applied while querying data.  The disadvantages of this model are that it can handle only a limited amount of data.

7. ROLAP Relational Online Analytical Process (ROLAP) :  The underlying data in this model is stored in relational databases.  Since the data is stored in relational databases this model gives the appearance of traditional OLAP’s slicing and dicing and drill down functionality.  The advantages of this model is it can handle a large amount of data and can leverage all the functionalities of the relational database.

8. HOLAP Hybrid Online Analytical Process (HOLAP)  HOLAP technology tries to combine the strengths of the MOLAP and ROLAP.  For summary type information HOLAP leverages cube technology and for drilling down into details it uses the ROLAP model.

9. Definition of cube High Level definition of Cube  A cube is a set of data that is organized and structured in a hierarchical, multidimensional arrangement Why do we need it  Rollup or sum the data to higher levels  The models are defined by dimension structures and measures which can be easily customized  Time periods are handled in a specific way which makes data delivery easy  High flexibility and portability

10. Cube Interface Levels Measures Dimension Data source Sign on Cube

11. Definition of DMR What is DMR  DMR stands for Dimensionally Modeled Relation, a Cognos modeling technique allowing to present relational data sources as OLAP cubes.  DMR processes relational data on the fly and presents it back to end users in a hierarchical view, allowing them to navigate from summary to more detailed levels of data in a visual format  All OLAP-style queries, roll-ups\drill-downs are then transformed into appropriate sql (group by's, aggregations) by Cognos Server Why do we need it  Analysis studio is available.  Real time Analysis

12. DMR in Framework Dimension Levels Hierarchy

13. Differences Between OLAP and DMR Advantages of OLAP Cube  Easy to create  Fast Performance  Limited Data  Data is up to last build  Pre Aggregated Disadvantages of DMR  Adds complexity  Requires local processing, potentially moving large amounts of data from the database to the BI server for final processing  Complex to create  5-20% more time based on data size and query complexity

14. Analysis Studio for Cube and DMR Granite Cube Granite OLAP

15. Philosophy for building a cube 1. Analyze requirements 2. Access your source data 3. Identify measures and Dimensions 4. Specify Time dimension 5. Identify Hierarchies 6. Create Model

16. Optimization of Cubes Shorten processing Time in Transformer  Use multiple queries to reduce the size of each source file  Optimizing querying  Incremental updates (Only add new data) Shorten access time in Power Play  Reduce the number of categories Auto _Partition (Divides large power cube into set of small pre summarized cubes)  Maximize data consolidation by adding a sort step before records are written to the cube  Improve the performance of queries since already summarized

17. Cube Optimization Interface

18. Questions Q & A?