Bts(Fs-tmt) Dwh - Ucf 2.x Advanced Olap Concepts v1.0 - Part 1

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Advanced OLAP ConceptsAdvanced OLAP ConceptsPart 1Part 1

<OLAP Concepts 201 Series>Renu Singh

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OLAP Overview• On-Line Analytical Processing is a decision support

software that allows the user to quickly analyze information that has been summarized into multidimensional views and hierarchies.

• There are three main features of OLAP system : Multidimensional Viewing – OLAP supports

multidimensional model which consists of facts and dimensions also called as Star Schema.

Calculation Intensive Capabilities – Due to data is stored in facts and dimensions tables, it enables users to analyze data without much calculations.

Time Series analysis – Enables users to analyze data across time.

• This Module will cover the details in four parts:– Part 1 – OLAP Modeling – Building Blocks– Part 2 – OLAP Types & Architectures – Part 3 – OLAP Reporting Styles – Part 4 – OLAP Issues & Optimization


OLAP Objectives

Upon completion of this module you will be able to:• Understand OLAP Building Blocks i.e. OLAP Modeling


OLAP Outline

Lesson 1 OLAP Modeling

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OLAP Modeling

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• Multidimensional modeling– Basis for OLAP application. It represents data

under the metaphor of a cube whose cell corresponds to event that occurred in business domain.

Building Blocks - Dimensional Fact modeling

DimensionDimension

DimensionMeasure



A simple representation of dimensional fact schema

Invoice Line

QuantityUnit priceNet amountVATTotal amountdiscount

yeardate month

week

month

Product

Type

brand

Category

state city

customer



Dimension AttributesProperty of dimensionExample: product dimension is described by type, category, brand

HierarchyDirected tree, rooted in a dimension. All nodes are dimension attributesExample:



Few other important items• Convergence - two attributes in a same dimension and same

hierarchy are connected by more than one alternate path

• Cross dimensional attribute – whose value is determined by two or more dimension attribute. Example: VAT- depends on both product category and state

• Operational/Multiple Arcs – Defines the association of attributes in dimensional model


• Shared Hierarchy – Helps to reduce redundancy of using large portion of hierarchies twice or more in same Fact schema

• Ragged (incomplete) Hierarchy – Relates the instances where values of one or more attributes are missing. It mostly occurs in geographic hierarchy

• Unbalanced Hierarchy – each level has a consistent meaning, but the branches have inconsistent depths

• Dynamic Hierarchy – Frequently changes hierarchy as time is a key factor



OLAP Cube (Data Cube)It is a data structure that allows faster analysis of data. It will

help user to analyze facts at multiple level of abstraction

A cube is a set of possible views defined over a list of dimensions, a base table and aggregated measures. Cube view can be denoted as CV[G], where G is a granularity

Example: A cube view that sums amounts sold at category Department can be defined as ………

Select Department, SUM(amount)from sales, T[Product, department]where sales.product = T[Product, department].productgroup by department

This view can be defined as CV[department]



OLAP Operations

Rollup and Drilldown operations:Rollup operations aggregates cube view to

a higher granularity. It is also called a summarization or consolidation. As an example, sales figure can be aggregated in agent level and then city level

Drilldown is a process where user travel from summarized to more detail level. As an example, sales in zonal level can be detailed to agent level



OLAP Operations Slice and Dice operations:

Slice is limiting the analysis of OLAP cube to a given attribute/property. It is a two dimensional view of a cube.

Dice limits analysis to subset of an attribute



Basic Features• Multidimensional conceptual view• Intuitive data manipulation• Accessibility• Client Server Architecture• Transparency• Multi-user support

Reporting Features• Flexible reporting• Uniform reporting performance• Automatic adjustment of Physical Level

Building Blocks – Codd Rules for OLAP


Dimensional Features• Generic dimensionality• Unlimited dimensions and aggregation levels• Unrestricted cross-dimensional operations

Building Blocks – Codd Rules for OLAP


Congratulations! You have now completed the module OLAP Building Blocks

You should now be able – Dimensional Modeling– Features of Dimensional Modeling– Codd Rules for OLAP

Module Summary

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Quiz

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1. Process to see low level of information from aggregated level isa) Drill Downb) Drill Upc) Drill Acrossd) Drill Through

Question 1

Answer: A


2. Lowest level of information stored in Dimensional model is called asa) Factb) Granularityc) Measured) None of the above

Question 2

Answer: B


3. Basis of OLAP system is <>a) Measureb) Factc) Dimensiond) Multi Dimensional Model

Question 3

Answer: D


4. _____ is a data structure that allows faster analysis of data. a) Measureb) OLAP Cubec) Dimensiond) None of the above

Question 4

Answer: B


5. Two attributes in a same dimension and same hierarchy are connected by more than one alternate patha) Convergenceb) Cross Dimensionc) Conformed Dimensiond) None of the above

Question 5

Answer: A

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References


References

Library:Tek-tips reporting solutions forumhttp://www.dmreview.com/http://www.dwinfocenter.org

Data warehouse and OLAP – Concepts, Architecture and Solutions by Robert Wrember & Christian Koncilia

The OLAP Report by Nigel Pendse and Richard Creeth

The OLAP Solutions – Building Multidimensional Information Systems Second Edition by Erik Thomsen, Wiley dreamtech India Pvt. Ltd. 2002 ISBN 81-265-0275-4

Publications Publications

Training ProgramsURL’s

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Thank You

Renu SinghTechnical Lead

[email protected]

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Bts(Fs-tmt) Dwh - Ucf 2.x Advanced Olap Concepts v1.0 - Part 1

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