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Data Warehouse (Implement a Data Warehouse with SQL Server (Design and…

- - - - Data acquisition
      - Data storage
      - Information delivery
    - - Factors
        
        Primarily, it includes the integrated data that is the centerpiece.
        
        Everything that is needed to prepare the data and store it.
        
        On the other hand, all the means for delivering information to user.
        
        The rules, procedures, and functions that enable the data warehouse to work and fulfill the business requirements.
      - General purpose
        
        The architecture provides the overall framework for developing and deploying the data warehouse.
        
        It is a comprehensive blueprint.
        
        The architecture defines the standards, measurements, general design, and support techniques.
    - - 1. Different Objectives and Scope
        
        Defining the scope
        
        You consider the group of users, the range of functions, the data repository, and the output screens and reports
        
        Factors to consider
        
        First, must consider the number and extent of the data sources.
        +How many legacy systems that data are extracted from? +What are the external sources? +Are you planning to include departmental files, spreadsheets, and private databases?
        
        What about including the archived data?
        
        Scope of the architecture may again be measured in terms of the data transformations and integration functions.
        
        Data granularity and data volumes are also important considerations.
      - 2. Data content
        
        The “read-only” data in the data warehouse sits in the middle as the primary component in the architecture.
        
        Before data is brought into your data warehouse and stored as read-only data, a number of functions must be performed.
        
        Further, the data warehouse architecture must support the storing of data grouped by business subjects, not grouped by applications as in the case of operational systems.
        
        When we mention historical data stored in the data warehouse, we are talking about very high data volumes.
        
        Most companies opt to keep data going back 10 years in the data warehouse.
        
        Some companies want to keep even more, if the data is available. This is another reason why the data warehouse architecture must support high data volumes.
      - 3. Complex Analysis and Quick Response
        
        The data warehouse architecture must support complex analysis
        
        Most of the online information retrieval during a session by a user is interactive analysis.
        
        User usually starts with a query at a high level, reviews the result set, initiates the next query looking at the data in a slightly different way, and so on. It is a long session
        
        Therefore, the data warehouse architecture must support variations for providing analysis
        
        Users must be able to drill down, roll up, slice and dice data, and play with “what-if” scenarios.
        
        Users must have the capability to review the result sets in different output options.
        
        Users are no longer content with textual result sets or results displayed in tabular formats. Every result set in tabular format must be translated into graphical charts.
        
        The data warehouse architecture must make it easy to make strategic decisions quickly
      - 4. Flexible and Dynamic
        
        All business requirements up front
        
        Nevertheless, the missing parts of the requirements show up after your users begin to use the data warehouse. What is the implication of this ?
        You have to make sure your data warehouse architecture is flexible enough to accommodate additional requirements as and when they surface.
        
        Additional missed requirements is need
        
        Business conditions themselves change.
        
        In fact, they keep on changing. Changing business conditions call for additional business requirements to be included in the data warehouse.
        
        If the data warehouse architecture is designed to be flexible and dynamic, then the data warehouse can cater to the supplemental requirements as and when they arise.
      - 5. Metadata-driven
        
        As the data moves from the source systems to the end-users as useful (it is strategic information), the metadata surrounds the entire movement.
        
        In an operational system, there is no component that is equivalent to metadata in a data warehouse.
        
        In your data warehouse architecture, the metadata component interleaves with and connects the other components.
  - - - Shared Disk.
      - Mass Transmission
      - Real-time Connection
      - Manual Methods