The new data-driven industrial revolution highlights the need for big data technologies to unlock the potential in various application domains. To this end, BigDataStack delivers a complete high-performant stack of technologies addressing the emerging needs of data operations and applications. The stack is based on a frontrunner infrastructure management system that drives decisions according to data aspects thus being fully scalable, runtime adaptable and performant for big data operations and data-intensive applications.
BigDataStack promotes automation and quality and ensures that the provided data are meaningful, of value and fit-for-purpose through its Data as a Service offering that addresses the complete data path with approaches for data cleaning, modelling, semantic interoperability, and distributed storage.
BigDataStack introduces a pioneering technique for seamless analytics which analyses data in a holistic fashion across multiple data stores and locations, handling analytics on both data in flight and at rest. Complemented with an innovative CEP running in federated environments for real-time cross-stream processing, predictive algorithms and process mining, BigDataStack offers a complete suite for big data analytics.
BigDataStack holistic solution incorporates approaches for data-focused application analysis and dimensioning, and process modelling towards increased performance, agility and efficiency. A toolkit allowing the specification of analytics tasks in a declarative way, their integration in the data path, as well as an adaptive visualization environment, realize BigDataStack’s vision of openness and extensibility.
With an emphasis on standardisation and open source contributions targeting high impact, BigDataStack will enable data operations and data-intensive applications to take full advantage of the developed technologies, exhibiting their applicability through three commercial use cases from the maritime, market and financing domains.
Innnovation Radar's analysis of this innovation is based on data collected on 18/07/2019.