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Data Warehouse Process


Source DB --> SSIS --> SSAS --> SSRS

  • Application Database (Source DB).

    • Definition of needs and requirements
    • Design the Data Warehouse
      • Construction of dimensions
      • Construction of fact tables
    • ETL
      • Extract relevant data.
      • Transform data to DWH format.
      • Load data into DWH.
    • Relational Data Warehouse Management.

      • DATA Analysis
      • Dimensions and cubes in an Analysis Services solution.

        • Data Presentation – Reports.

        3 comments:

        1. You shared us a very detailed information regarding the process of a warehouse making. It looks so effective for me that is why I copy, paste and save it for future reference. Expect me to share this to others too.

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        2. A Data Warehouse is a centralized repository that stores large volumes of historical data collected from multiple sources, such as databases, applications, and external systems. It is designed specifically for reporting, business intelligence, and data analysis rather than day-to-day transaction processing. Data in a data warehouse is integrated, cleaned, and organized to provide a consistent and reliable view of an organization's information, enabling better decision-making.

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        3. A data warehouse has four key characteristics: it is subject-oriented, integrated, time-variant, and non-volatile. Big Data Projects.It allows users to analyze historical trends, generate reports, and support strategic planning through tools such as Online Analytical Processing (OLAP) and data mining. By providing fast access to high-quality data, a data warehouse helps organizations improve business performance, identify patterns, and make informed decisions.

          ReplyDelete