One of the goals of BigDataStack is to facilitate scalable data storage through a distributed storage layer. This would enable storage across different resources, while supporting data migration for application components and re-allocation of data services across the infrastructure.
We are at an age where a single jet engine creates up to one terabyte (1,000,000,000,000) of data within a single transatlantic flight. Each one of us is like one of those engines, giving off ‘data exhaust’ as we operate in our daily lives. But these aren’t just inconsequential. In fact, big data is a worldwide market with more than estimated $203 billion worth of value by 2020.
We are happy and proud to announce that our paper entitled "BigDataStack: A holistic data-driven stack for big data applications and operations", submitted to the IEEE BigData Congress 2018, affiliated with the 2018 IEEE World Congress on Services (IEEE SERVICES 2018), has been accepted for publication.
A datacentric paradigm helps create a 360° view of the customer and provide personalized services.
The diffusion of online banking, social media, banking operations in branches and ATMs create a multi-channel scenario. BigDataStack allows this through facilitating intelligent banking powered by data analytics.
A vessel has to complete its route within a time-frame. When a part of the main engine fails unexpectedly, the ship risks staying off-hire. This can be very damaging to a shipping company, as chartering revenues decrease, while replacing a spare part immediately increases cost. Thus, identification of potential failure allows timely ordering, or even replacement of spare parts before failure.
BigDataStack will deliver a complete pioneering stack, based on a frontrunner infrastructure management system that drives decisions according to data aspects, thus being fully scalable, runtime adaptable and high-performant to address the emerging needs of big data operations and data-intensive applications.