Today we have decided to focus a bit more on the "Why" angle of Big Data, in an attempt to outline the output of your efforts. More specifically, we'll stop at BigData Analytics, their essence, purpose and variety, that fuel predictive business decisions.

Let's hinge towards IBM Data Scientists for a deeper dive into the BigData concepts. Hopefully, this syrup will enrich your BASICs appetite whilst delivering a better understanding of those "under-the-hood" processes.

Last time we promised a buffet of data-related topics, so here we are, keeping our promises (a default powered by our teams' environment). Advancing from abstract notions such as data drivenness, we thought a fair path towards the main course would be an introduction to BigData.

Peeling this BASICs digital onion, we've left some details out (particularly in our last article). As stated, data-centric applications deserve their series, a journey we're kicking off today. Before talking about BigData, strategies and mechanics, let's filter the messy data-centric vs data-driven dilemma.

Based on our refactoring experiences nearly 99% of the time, most common high-load changes can be resolved by implementing microservices an efficient data paradigms.