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Data Warehouse

  •  A Data Warehouse is a subject-oriented integrated time-varying non-volatile COLLECTION OF DATA THAT is used PRIMARILY IN ORGANIZATIONAL DECISION MAKING.
- Bill Inmon,Building the Data Warehouse 1996

  • A process of transforming data into information and making it available to users in a timely enough manner to make a difference
              • Forrester Research April 1996
  • Data Warehouse Architecture
 
 
  • OLTP vs Data Warehouse
 

                    OLTP

            Data Warehouse

  • Application Oriented

  • Used to Run Business

  • Detailed Data

  • Current Up to Date

  • Isolated Data

  • Repetitive Access

  • User Specific

  • Performance Sensitive

  • Less Records accessed at a time

  • Read/Update Access

  • No Data Redundancy (Normalization)


  • Subject Oriented

  • Used to Analyze Business

  • Summarized and Refined

  • Snapshot Data

  • Integrated Data

  • Ad-hoc Access

  • Business User

  • Performance Relaxed

  • Large Volumes at a time

  • Mostly Read(Batch Update)

  • Redundancy Present (De normalized)



  • ETL (Extraction ,Transformation and Loading) is a process by which data is integrated and transformed from the operational systems into the Data Warehouse environment

Operational Data Challenges

  • Data from Heterogeneous sources
  • Format Differences
  • Data Variations 
    • Across Locations same code represents different customers
    • Across period of time a product code have been re-used.

Data Transformation

  • Conversions of Data - Data Type change / Standardized to common units (currency/measurements)
  • Classification -Changing continuous values to discrete ranges (temperature to temperature ranges)
  • Splitting of Fields
  • Merging
  • Aggregations 
  • Derivations(Percentages,Ratios,Indicators)
  • Four Classes - Structure ,Format,Conversions,Classifications

Guiding Principles

  • Single Version of Truth
  • Integration of Data
  • Non Redundant Data
  • Established Standards
  • Methodology specific to data warehousing
  • Business Oriented data model with metadata
  • Detailed atomic data
  • Uniform data meanings
  • Time-dimensioned data with years of history
  • Adhoc Access
  • Scaled for growth of data,users and speed
  • Any Questions ,any data, at any time

ETL Methodologies

  • Kimball /Star Schema - the Right way to do it , takes longer to develop , use less space
  • Inmon / 3rd Normal Form - The wrong way ,keep the same structure as source , puts lot of work on Business Analysts.

Kimball vs Inmon

  • Ralph Kimball approch stressed the importance of data marts , which are repositories of data belonging to particular lines of Business .The data warehouse is simply a combination of different data marts that facilitates reporting and analysis. the Kimball data warehouse uses a "bottom-up" approch.
  • Bill Inmon regarded the data warehouse as the centralized repository for all enterprise data. In this approch , an organization first creates a normalized data warehouse model.Dimensional data marts are then created based on the subjects , it uses "top-down" approch.

 

 

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