OLAP Services

Business Intelligence Solutions

Agenda

Definition of OLAP Types of OLAPDefinition of CubeDefinition of DMR Differences between Cube and DMRDesign Philosophy for building an efficient cubeExamplesQuestions

OLAP

Definition of OLAP

On-Line Analytical Processing is multi dimensional analysis of data stored in a database.

Why do we need it

EasyMulti-dimensional presentation for business information and analysisEasy to use

FastOLAP Technology is very fast – 98% of reports in less than 1-3

seconds Powerful

Calculated Columns enables calculations that are difficult using relational

technology

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

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)

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.

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.

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.

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

Cube Interface

Levels

Measures

Dimension

Data source

Sign onCube

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

DMR in Framework

Dimension

Levels

Hierarchy

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

Analysis Studio for Cube and DMR

Granite Cube Granite OLAP

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

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

Cube Optimization Interface

Questions

Q & A?