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Data Warehousing. COT4810 4/3/2008. Data Warehousing. Overview Purpose History Methods Design Tools Other aspects of DW. The Purpose of Data Warehousing. Atomic data has a value now Sometimes the value needs to be realized through analytics Reporting Data mining.

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data warehousing

Data Warehousing



data warehousing2
Data Warehousing
  • Overview
    • Purpose
    • History
  • Methods
    • Design
    • Tools
  • Other aspects of DW
the purpose of data warehousing
The Purpose of Data Warehousing
  • Atomic data has a value now
  • Sometimes the value needs to be realized through analytics
    • Reporting
    • Data mining
the purpose of data warehousing4
The Purpose of Data Warehousing
  • Warehouses enable Decision Support
  • Turn static atomic data into valuable information
  • Used to support a variety of other Business Intelligence (BI) platforms
the history of data warehousing
The History of Data Warehousing
  • Concept developed by IBM Researchers in 1980’s
  • Really takes off in early-mid 1990s
    • 1991, Bill Inmon
    • 1996, Ralph Kimball
what is a data warehouse
What is a Data Warehouse?
  • Bill Inmon’s definition:

“A data warehouse is a subject oriented, integrated, non-volatile, time variant collection of data [to support management decisions].”

what is a data warehouse7
What is a Data Warehouse?
  • Subject oriented
    • Data is arranged by subject area instead of application
  • Integrated
    • Data is collected and consistently stored from multiple sources
what is a data warehouse8
What is a Data Warehouse?
  • Non-volatile
    • Data is static
      • It doesn’t matter when the question is asked
  • Time variant
    • Allows for analysis of data over time
oltp and olap
  • Online Transaction Processing (OLTP)
  • Online Analytical Processing (OLAP)
oltp and olap11
  • They work well together…
    • OLAP supports offloaded OLTP data
      • No longer needs to accumulate in the application database (or on stored media)
    • Less data, less index maintenance
    • Allows OLTP to perform at peak transaction efficiency
      • Smaller OLTP database = better performance
data marts
Data Marts
  • A Data Mart hosts a specific subset of data
  • Multiple Data Marts can compose what BI/DSS views as the Data Warehouse
inside the warehouse
Inside the Warehouse
  • Data is captured from source from via an ETL process
    • Extract source data
    • Transform source data
    • Load into Warehouse
  • Typically data is organized into Fact and Dimension tables
fact tables
Fact Tables
  • Numerical data representing a specific activity
    • Details about the sale of a product
      • How many
      • How much
      • When
      • What store
  • Fact tables exist at the center of the Star Schema
dimension tables
Dimension Tables
  • Characteristics of the facts that offer a business perspective
    • Details about when a product was sold
      • Was it a weekday or weekend
      • Was it a holiday
      • First of the month, last of the month
star schema rolap
Star Schema (ROLAP)

cubes molap
Cubes (MOLAP)
  • Takes multiple dimensions and optimizes a view

other aspects of the data warehouse
Other Aspects of the Data Warehouse
  • Data Mining
  • User Interface design
  • Requirements for Business Intelligence predictions or forecasts
  • Data “Webhouse”
  • Why did Data Warehousing get a boost in the 1990s?
  • What are the steps in the ETL process?
  • Kimball, Ralph. The Data Warehouse Toolkit. Wiley Publishing. 2nd Edition. April, 2002.
  • Inmon, W. H. Building the Data Warehouse. Wiley Publishing. 4th Edition. October, 2005.
  • Dave Browning and J. Mundy. “Data Warehouse Design Considerations.” Microsoft SQL Server Technical Articles. December, 2001.