Hybrid Knowledge Management Architectures

With the growing need for volumes of data required by ML and Knowledge Bases, copying/duplicating potentially Petabytes of data is a real problem. Working with data "in situ" is fast becoming the only viable pattern for enterprises. Additionally, heterogenous data silos are given for big enterprises and aren't going away. Finally, incorporating 3rd party taxonomies/ontologies/datasets for enrichment are yet another example of data sources that need to be incorporated and orchestrated. This suggests the need for a hybrid approach for Knowledge Management. With careful architecture, this is where RDF can really shine. Further, moving to the cloud offers real long-term advantages.