Big Data and Cloud Computing

Applications can pull from and work with data from , multiple sources as long as the app design incorporates these five fundamental data management and mapping techniques. If you don't account for and carefully manage data sources during application design, there's a real risk that the application will fail to meet performance, resilience and elasticity expectations. This risk is especially true in analytics applications that draw from multiple data sources. However, there are five ways to address the problem of multiple data sources in an application architecture: Know what data you need to combine, use data visualization, add data blending tools, create abstracted virtual database services and determine where to host data sources. Let's look at what each of these tasks entails and why they make a difference in application data management. 1. Know what data to combine The first thing to understand is what you should combine, both in terms of the data sources and ...

This long article has the following sections: Why do we need to model our data? Why do we need dimensional models? Data Modelling vs Dimensional Modelling So why do some people claim that dimensional modelling is dead? The Data Warehouse is dead Confusion The Schema on Read Misunderstanding Denormalization revisited. The physical aspects of the model. Taking de-normalization to its full conclusion Data distribution on a distributed relational database (MPP) Data Distribution on Hadoop Dimensional Models on Hadoop Hadoop and Slowly Changing Dimensions Storage evolution on Hadoop The verdict. Are dimensional models and star schemas obsolete? Complementary Reading on Dimensional Modelling in the Era of Big Data The purpose of this article is threefold (1) Show that we will always need a data model (either done by humans or machines) (2) Show that physical modelling is not the same as logical modelling. In fact it is very different and depends on the underlying techn...