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An overview of Data Warehousingand OLAP Technology

Surajit Chaudhuri Umeshwar DayalMicrosoft Reserch, Redmond HP Labs, Palo Alto

Presented  by:- Krishma Dutta

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Outline

Introduction

Need of Data Warehousing and OLAP

Architecture of Data Warehousing

Front-Back End Tools

Database Design Methodology

Conclusion

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Data Warehousing- An Introduction

Defined in many different ways:

In simplest terms Data Warehouse can be defined

as collection of Data marts

A data warehouse is a “subject-oriented, integrated,time-variant, and nonvolatile collection of data in

support of management’s decision-making

 process.”— W. H. Inmon

A data warehousing is a collection of decision

support technologies, aimed at enabling the

knowledge worker to make better decisions

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Need of Data Warehousing and OLAP

DataWarehousing

OLAP

Decision support requires historical

data which operational Databases do

not typically maintain

Decision support requires

consolidation (aggregation,

summarization) of data

Unacceptable Performance while

execution of complex OLAP queries

Multidimensional data model is not

supported by DBMS

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Tiered Architecture

Extract

Transform

Load

Refresh

Data Sources

Operational

Databases

External

Sources 

Data

Warehouse

Data Marts 

Data Storage

Tier1:

Data Warehouse

Server 

Serve

OLAP Engine

OLAP Server 

Tier2:

OLAP

Server 

Analysis

Query/Reports

Data mining

Front-End Tools

Tier3:

Clients

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Front-Back End Tools

Front End Tools Rollup (Drill-up)

Drill-down (Roll-down)

Slice and dice

Back End Tools Data Cleaning

Load

Refresh

Bob Jamie Brit Todd

Household

Automobile

Kitchen

Office

Montreal

Toronto

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Conceptual Model

Total annual salesof TV in U.S.A.

Date

   C  o  u  n   t  r

  y sum

 sum

TV

PVR PC

1 2 3 4

U.S.A

Canada

Mexico

 sum

 

ALL

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Database Design Methodology

DatabaseDesigns

Star Schema Snowflake Schema

A fact tablein the

middle

connected to

a set of dimension

tables

A refinement of star 

schema where

hierarchy is

normalized into a setof smaller dimension

tables, forming a shape

similar to snowflake

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Star Schema

Star Schema

location_keystreet

city

province

country

location

Time_key

Item_key

Branch_key

Location_key

Units_sold

Dollars_sold

Avg_sales   M  e  a  s  u  r  e  s

B_key

B_name

B_type

Branch

I_key

I_name

I_brand

I_type

I_supplier_type

item

T_keyT_day

T_day_week 

T_month

T_quarter

T_year

Time

Sales Fact Table

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Snowflake Schema

Snowflake Schema

location_keystreet

city

Location

time_key

item_key

 branch_key

location_key

units_sold

dollars_sold

avg_sales   M  e  a  s  u  r  e  s

B_key

B_name

B_type

Branch

I_key

I_nameI_brand

I_type

I_supplier_type

Item

T_key

T_day

T_day_week 

T_month

T_quarter

T_year

Time

Sales Fact Table

S_keyS_type

Supplier

C_key

C_city

C_province

C_country

City

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Summary

Data warehouse

A subject-oriented, integrated, time-variant, and nonvolatile

collection of data in support of management’s decision-making

 process

Architecture of Date warehouseConsisting of Warehouse servers, front end and back end tools

OLAP operations: drilling, rolling, slicing, dicing and pivoting

Multi dimensional model of Data warehouse

Data cube

Star Schema

Snowflake Schema

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