When building a data warehouse, you often need to flatten the data when you move it from the Raw tables to the Stage tables. Flattening data can be a difficult concept for new warehouse designers to grasp, so here are two detailed examples.
If you're inexperienced with using SSIS, creating a data warehouse might seem like an impossible task—but it's not if you use some different strategies. For example, you can use a wizard to initially create an SSIS package that will move data from the source system into the data warehouse.
It is crucial to move away from data and analytics stored on individual desktop computers. Today’s solutions must promote holistic, collective intelligence. The strong, continued alliance between Microsoft and Pyramid Analytics helps make all this possible....More
To become a truly data-driven enterprise, many business leaders recognize that they must extend the capabilities of self-service business intelligence (BI) and analytics to more of their business users. Many BI tools tackle part of this need, but they don’t offer a complete enterprise solution....More
Data quality is really important. Why? Because it helps you save money while increasing profits because of accurate contact data. It’s important to understand what data quality is and why it is important....More