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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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