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.