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.

Azure Machine Learning is a fully managed predictive analytics service that you can use to create predictive models against your data in SQL Data Warehouse, and then publish as ready-to-consume web services. You can learn the basics of predictive analytics and machine learning by reading Introduction to Machine Learning on Azure. You can then learn how to create, train, score and test a machine learning model using the Create experiment tutorial. We will read data from Product table in the AdventureWorksDW database. Start a new experiment by clicking NEW at the bottom of the Machine Learning Studio window, select EXPERIMENT, and then select Blank Experiment.

We need the following manual steps to complete the data factory.

Microsoft today announced the general availability of the Azure SQL Data Warehouse, an elastic data warehouse as a service with enterprise-class features. It is a fully managed DW as a Service that you can provision in minutes and scale up to 60 times larger in seconds. With Azure SQL Data Warehouse, storage and compute scale independently. You can dynamically deploy, grow, shrink, and even pause compute, taking advantage of best-in-class price/performance. Also, SQL Data Warehouse uses the power and familiarity of T-SQL to let you easily integrate query results across relational data in your data warehouse and non-relational data in Azure blob storage.