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Building a data warehouse in SQL server is a complex process that requires a deep understanding of SQL Server and data warehousing best practices. It involves several steps, including planning, designing, and implementing the database schema, as well as loading and transforming the data. Here are the high-level steps you can follow to build a data warehouse in SQL Server: ·          Identify the business requirements : Before you start building a data warehouse, you need to identify the business requirements, such as what data is needed, who will use the data, and how the data will be analysed. ·         Plan the data warehouse schema: Once you have identified the business requirements, you need to plan the data warehouse schema. This involves designing the tables, relationships, and indexes that will be used to store the data. ·         Create the database: After plann...

  The decision between a database and a data warehouse will depend on your specific needs and use case. Deciding between a database and a data warehouse largely depends on the specific needs of your organization and the type of data you're working with. Some of the factors for consideration are: Ø  Data Volume : When you have large volume of data (e.g., terabytes or more), then better to go for a data warehouse as data warehouses are optimized for handling large amounts of data. Databases are designed to handle smaller amounts of data. Ø  Data Complexity : If you are working with complex data that requires advanced querying and analysis, then, a data warehouse will be a better choice. Data warehouses can handle complex queries and data modelling. Databases may not be equipped to handle the same level of complexity. Ø  Data Integration : If you need to integrate data from multiple sources, a data warehouse may be the better choice. Data warehouses are designed to hand...

Evaluating the analytical results on live systems involves testing the validity and accuracy of the results obtained from the analysis.  This process can involve the following steps:   Verification of Data: The first step is to ensure that the data used in the analysis is accurate and up-to-date. This includes checking for any errors in the data collection process and making sure that the data is relevant to the system being evaluated. Comparison with Actual Results : The next step is to compare the analytical results with actual results obtained from the live system. This will help to determine if the analysis is producing accurate results and if any discrepancies exist, the cause of these discrepancies can be investigated. Sensitivity Analysis: It is also important to perform a sensitivity analysis to determine how changes in the inputs used in the analysis will affect the results. This helps to identify any areas where the analysis may be vulnerable to changes in the live ...