Conventional wisdom states that you should build a data warehouse (DW) and then populate data marts from it. This data architecture is called a hub and spoke. The central premise of the hub and spoke is that the heavy-duty data integration activities are completed as you move the data from source systems into the DW. Once these data integration tasks are done, you can propagate or "franchise" the data from the DW into the data marts. Data franchising takes the DW data and packages it via filtering, aggregation and transformations into data usable by business intelligence (BI) tools and understandable to business users (see Figure 1). This approach works extremely well, but lately I am concerned because it appears this approach is being abandoned. I think this is happening because people have forgotten its fundamental aspects and why it works better than the alternatives. Due to advances in databases, storage, network and server capabilities, it is often more effective to deploy the hub and spoke all on the same database or instance rather than store the data mart "spokes" on separate servers and databases.
Figure 1: Hub and Spoke
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