DATA WAREHOUSING

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DATA WAREHOUSING
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Legacy System

• Systems that were developed in the early years of
  business processing
• Rich source of historical data, but its
  difficult to retrieve, because of non-standard
  features
• This is why we need data warehouse

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Problems with Legacy System

• Access data from a legacy system may be difficult
  for several reasons
• Developed for a different hardware or software
  platform
• Use a different data model
• Use a different DBMS
• Use a different data definitions
• Use a different data format
• All these make difficulty in integration and
  sharing data

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Data Definitions Problems

• Homonyms use different field names to store the
  same data in the different database
• Synonyms - use the same field names to store
  different data in the different database
• Domain integrity domain for the same field may
  be different
• Business rules may be different in different
  database
• Referential integrity may be problems linking
  related records from different databases
• Concurrency control when multiple users access
  a database that design for single user

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Data Warehouse Concepts

• Technique of extracting and filtering data from
  diverse database and use this data to build a new
  database
• Stores information extracted from historical,
  operational and external databases
• The primary purpose to provide information for
  management decision making

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Database vs data warehouse
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Data Warehouse Architecture

• Operational database / external database layer
• Information access layer
• Data access layer
• Metadata layer
• Process management layer
• Application messaging layer
• Physical layer
• Data staging layer

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Data Warehouse Implementation

• Data includes operational, historical and
  external data
• Extraction and transformation extract and
  transform data in different table
• Data warehouse storage store the extracted and
  transformed data in different table
• Historical data used for forecasting purposes
• Reports, statistics, data analysis and
  presentation output from data warehouse to make
  a decision

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Data Warehouse Benefits and Risks

• Benefits
• Reduces reporting cost
• Reduces data consolidation and integration cost
• Increase efficiency and decision making
  capabilities

• Risks
• House the wrong data
• Expensive to build and maintain
• Require organizational changes

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Online Analytical Processing

• Support data modeling and multidimensional data
  analysis
• Share the characteristics
• Provide user-friendly interface
• Use multidimensional data analysis technique
• Provide advanced database support
• Support client/server architecture

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Online Analytical Processing

• Can be classified
• Relational Online Analytical Processing use
  RDBMS
• Multidimensional Online Analytical Processing
  extension of RDBMS

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Data Mining

• Data mining is a decision support tools that
  enables a user to access directly large amount of
  data and analyzes the data
• Data mining is the set of activities used to find
  new, hidden, or unexpected patterns in data

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Data Mining Technique

• Data mining process has four phases
• Data preparation main data sets to be used are
  identified and cleaned
• Data analysis and classification identify
  common data characteristic or pattern
• Knowledge acquisition develop a model resemble
  target data
• Prediction used to predict future behaviour and
  forecast business outcomes

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Data Mining Tools

• Data mining tools today has this following
  characteristics
• Data preparation facilities
• Selection of data mining operations
• Product scalability and performance
• Facilities for visualization of results

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