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picture1_Business Ppt Templates 73917 | 1dwdm Ppt By Dshankaragowda B


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File: Business Ppt Templates 73917 | 1dwdm Ppt By Dshankaragowda B
introduction data warehousing a data warehousing is a technique for collecting and managing data from varied sources to provide meaningful business insights it is a blend of technologies and components ...

icon picture PPTX Filetype Power Point PPTX | Posted on 01 Sep 2022 | 3 years ago
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    Introduction Data Warehousing 
   A data Warehousing is a technique for collecting and managing 
   data from varied sources to provide meaningful business insights. 
   It  is  a  blend of technologies and components which allows the 
   strategic use of data. It is electronic storage of a large amount of 
   information  by  a  business  which  is  designed  for  query  and 
   analysis  instead  of  transaction  processing.  It  is  a  process  of 
   transforming  data  into  information  and  making  it  available  to 
   users in a timely manner to make a difference.
      Data Warehousing Modeling
   Data warehouse modeling includes:
   Top Down/Requirements Driven Approach
   Fact Tables and Dimension Tables
   Multidimensional Model/Star Schema
   Support Roll Up, Drill Down, and Pivot Analysis
   Time Phased/Temporal Data
   Operational Logical and Physical Data Models
   Normalization and Denormalization
   Model Granularity: Level of Detail 
   OLAP
   Online analytical processing(OLAP) is an approach to answer multi-dimensional analytical 
   queries swiftly in computing. OLAP is part of the broader category of business intelligence., 
   which also encompasses relational databases, report writing and data mining.
   Advantages:
   OLAP is a platform for all types of business includes planning, 
   budgeting, reporting and Analysis.
   Information and calculations are consistent in an OLAP cube. 
   This is a crucial benefit .
   Disadvantages:
   OLAP requires organizing data into a star schema. These 
   schemas are complicated to implement and administer.
   Transactional data cannot be accessed with OLAP system. 
   The main characteristics of OLAP are as follows:
   • Multidimensional conceptual view: OLAP systems let business users 
   have a dimensional and logical view of the data in the data warehouse. It 
   helps in carrying slice and dice operations.
   • Multi-User Support: Since the OLAP techniques are shared, the OLAP 
   operation  should  provide  normal  database  operations,  containing 
   retrieval, update, adequacy control, integrity, and security.
   • Accessibility: OLAP acts as a mediator between data warehouses and 
   front-end. The OLAP operations should be sitting between data sources 
   (e.g., data warehouses) and an OLAP front-end.
   • Storing  OLAP  results: OLAP  results  are  kept  separate  from  data 
   sources.
   • Uniform  documenting  performance: Increasing  the  number  of 
   dimensions  or  database  size  should  not  significantly  degrade  the 
   reporting performance of the OLAP system.
  • OLAP provides for distinguishing between zero values and 
   missing values so that aggregates are computed correctly.
  • OLAP system should ignore all missing values and compute 
   correct aggregate values.
  • OLAP facilitate interactive query and complex analysis for 
   the users.
  • OLAP allows users to drill down for greater details or roll up 
   for aggregations of metrics along a single business 
   dimension or across multiple dimension.
  • OLAP provides the ability to perform intricate calculations 
   and comparisons.
  • OLAP presents results in a number of meaningful ways, 
   including charts and graphs.
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...Introduction data warehousing a is technique for collecting and managing from varied sources to provide meaningful business insights it blend of technologies components which allows the strategic use electronic storage large amount information by designed query analysis instead transaction processing process transforming into making available users in timely manner make difference modeling warehouse includes top down requirements driven approach fact tables dimension multidimensional model star schema support roll up drill pivot time phased temporal operational logical physical models normalization denormalization granularity level detail olap online analytical an answer multi dimensional queries swiftly computing part broader category intelligence also encompasses relational databases report writing mining advantages platform all types planning budgeting reporting calculations are consistent cube this crucial benefit disadvantages requires organizing these schemas complicated implemen...

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