Data Warehouse Technologies*
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An Introduction to Data Warehousing
Presented by
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In the Beginning, life was simple…
Data

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But…
Data








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Our information needs…




















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Kept growing. (The Spider web)
SOURCE: William H. Inmon












































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Purpose
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Briefing Contents

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So What Is a Data Warehouse?
Definition: A data warehouse is the data repository of an enterprise. It is generally used for research and decision support.
By comparison: an OLTP (on-line transaction processor) or operational system is used to deal with the everyday running of one aspect of an enterprise.
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Why Do We Need Data Warehouses?
Consolidation of information resources
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What Is a Data Warehouse Used for?
Knowledge discovery
Medical research
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Goals
Structure
Size
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Comparison Chart of Database Types
Data warehouse
Operational system
Subject oriented
Transaction oriented
Small (MB up to several GB)
Historic data
Current data
Batch updates
Continuous updates
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Design Differences
Star Schema
Data Warehouse
Operational System
ER Diagram
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Supporting a Complete Solution
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Data Warehouses, Data Marts, and Operational Data Stores
Data Warehouse – The queryable source of data in the enterprise. It is comprised of the union of all of its constituent data marts.
Data Mart – A logical subset of the complete data warehouse. Often viewed as a restriction of the data warehouse to a single business process or to a group of related business processes targeted toward a particular business group.
Operational Data Store (ODS) – A point of integration for operational systems that developed independent of each other. Since an ODS supports day to day operations, it needs to be continually updated.
SOURCE: Ralph Kimball
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Briefing Contents

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Building a Data Warehouse
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Stage 1: Analysis
Dimensional analysis
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Stage 2: Design
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Dimensional Modeling
Fact Table – The primary table in a dimensional model that is meant to contain measurements of the business.
Dimension Table – One of a set of companion tables to a fact table. Most dimension tables contain many textual attributes that are the basis for constraining and grouping within data warehouse queries.
SOURCE: Ralph Kimball
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Stage 3: Import Data
Identify data sources
Extract the needed data from existing systems to a data staging area
Transform and Clean the data
Resolve data type conflicts
Remove, correct, or flag bad data
Conform Dimensions
Analysis
Design
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Importing Data Into the Warehouse
Operational Systems
(source systems)
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Stage 4: Install Front-end Tools
Reporting tools
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Stage 5: Test and Deploy
Usability tests
Software installation
User training
Analysis
Design
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Special Concerns
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Briefing Contents

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Goals of the STORET Central Warehouse
Improved performance and faster data retrieval
Ability to produce larger reports
Ability to provide more data query options
Streamlined application navigation
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Old Web Application Flow
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Central Warehouse Application Flow
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Web Application Demo
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STORET Central Warehouse – Potential Future Enhancements
More query functionality
Additional report types
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Data Warehouse Components
SOURCE: Ralph Kimball
End User Data Access
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Data Warehouse Components – Detailed
Storage: flat file (fastest); RDBMS; other Processing: clean; prune; combine; remove duplicates; household; standardize; conform dimensions; store awaiting replication; archive; export to data marts No user query services
Data Mart #1: OLAP (ROLAP and/or MOLAP) query services; dimensional; subject oriented; locally implemented; user group driven; may store atomic data; may be frequently refreshed; conforms to DW Bus
Data Mart #2
Data Mart #3
End User Applications
feed
feed
feed
feed
End User Data Access
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Briefing Contents
Data Warehouse Concepts