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An overview of Data Warehousing and OLAP Technology. Presented By Manish Desai. Introduction What is data warehouse ? Explanation of definition Data warehouse Vs. Operational Database Data warehouse architecture Back end tools Conceptual model Database design Warehouse servers

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Presentation Transcript
  • What is data warehouse ?
  • Explanation of definition
  • Data warehouse Vs. Operational Database
  • Data warehouse architecture
  • Back end tools
  • Conceptual model
  • Database design
  • Warehouse servers
  • Index structures
  • Meta data
  • Conclusion
  • References
  • Essential elements of decision support
  • Enables The Knowledge Worker to make better and faster decisions
  • Used in many industries like:
    • Manufacturing (for order shipment)
    • Retail (for inventory management)
    • Financial Services (claims and risk analysis)
  • Every major database vendor offers product in this area
what is data warehouse
What is 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”
  • Typically maintained separately from operational databases
explanation of definition
Explanation of definition
  • Subject-Oriented:
    • Designed around subject such as customer, vendor, product and activity
    • Does not includes data that are not needed for Decision support system (DSS)
  • Integrated:
    • Most important feature
    • Consistent naming convention, measurement of variables and so forth
    • The data should be stored in single globally acceptable fashion
explanation continues
Explanation (continues…)
  • Time Varying:
    • All data in the warehouse should be accurate as of some moment in time
    • Data stored over a long time horizon (5 –10 years)
    • Key structure contains element of time (implicitly or explicitly)
    • Data once correctly recorded cant be updated
  • Non Volatile:
    • No Update of data allowed
    • only loading and access of data operations
  • Data sourcing,migration,cleanup tools
  • Meta data repository
  • Data marts
  • Data query, reporting, analysis and mining tools
  • Data warehouse administration and management
architecture continues
Architecture (continues…)
  • Distributed Data warehouse
    • Load balancing, scalability,higher availability
    • Meta data replicated and centrally administrated
    • Too expansive
  • Data marts
    • Departmental subset focused on selected subjects
    • example: marketing department includes customer, sales and product tabels
    • Has own repository and administration
    • May lead to complex integration problems if not designed properly
back end tools and utilities
Back end tools and Utilities
  • Data cleaning, loading, refreshing tools
  • Cleaning
    • Multiple source, possibility of errors
    • Example: replace string sex by gender
  • Loading
    • Building indices, sorting and making access paths
    • Large amount of data
      • Incremental loading
      • Only updated tuples are inserted ,Process hard to manage
  • Refresh
    • Propagating updates
    • When to refresh ?
    • Set by administrator depending on user needs and traffic
conceptual model and front end tools
Conceptual Model and front end tools
  • Multi dimensional view
    • Dimensions together uniquely determine the measure
    • Example: Sales can be represented as city,product, data
    • Each dimension is described by set of attribute
    • Example: product consist of
      • Category of product
      • Industry of product
      • Year of introduction
  • Front end tools
    • Multi dimensional spreadsheet
      • Supports Pivoting-reorientation
      • Roll_up - summarized data
      • Drill_down - go from high level to low level summary
database design
Database design
  • Two ways to represent Multi dimensional model
    • Star schema
      • Database consist of single fact table and single table for each dimension
      • Each tuples in fact table consist of pointer to each of dimension
    • Snowflake schema
      • Refinement over star schema
      • Dimensional hierarchy is explicitly represented by normalizing dimension tables
warehouse servers
Warehouse Servers
  • Specialized SQL servers
    • Provides advanced query language and query processing support for SQL queries over star and snowflake schemas
    • Example: Redbrick
    • Between relational back end and client front end tools
    • Extend traditional relational servers to support multidimensional queries
    • Example: Microstratergy
    • Multidimensional storage engine
    • Direct mapping
    • Example: Essbase from Arbor Inc.
index structures
Index structures
  • Bit map indices
    • Use single bit to indicate specific value of attribute
    • Example:

instead of storing eight characters to record “engineer” as skill of employee use single bit

id# Name Skill

1000 John 1

  • Join indices
    • Maintains the relationship between foreign key with its matching primary keys
meta data and warehouse management
Meta data and warehouse management
  • Its data about data
  • Used for building, maintain, managing and using data warehouse
  • Administrative meta data
    • Information about setting up and using warehouse
  • Business meta data
    • Business terms and definition
  • Operational meta data
    • Information collected during operation of warehouse
  • Data warehouse is the technology for the future.
  • data warehouse enables knowledge worker to make faster and better decisions
  • Inmon W. H.,Building the data warehouse
  • Kimball, R. The data warehouse toolkit.