Manage your Data Warehousing Challenges with Advanced Data Analytics

The Cortana Analytics Suite (CAS) is made up of different components in Azure, allowing users to custom build an analytical application to suit a wide range of analytics scenarios such as real-time recommendations, customer churn forecasting, fraud detection, and predictive maintenance just to name a few. In this post, we'll look at four problems with traditional data warehouses and show how the new Azure SQL Data Warehouse (part of the CAS) overcomes them and makes analytics available to organizations of all sizes. When developing a new data warehouse, one of the first steps is sizing and commissioning hardware requirements. However, sizing a data warehouse for both storage and processing can be difficult as you only know your present source data needs and therefore have to predict the rest. Also, purchasing and configuring hardware can be cost prohibitive.