Although many different techniques and technologies for big data appliances can increase scalable performance, the ways that certain applications are mapped to a typical Hadoop-style stack might limit scalability due to memory access latency or network bandwidth. Yet the promise of big data must go beyond increased scalability for known problems.
Big data analytics systems must adapt different techniques to solve a variety of challenges. These approaches should use creative algorithms to exploit data analytics methods that differ from the conventional batch-oriented reporting and analysis. One alternative is graph analytics, which uses an abstraction called a graph model that is intended to simplify the rapid absorption of data from many sources and linking of related information together in a logical manner. The graph analytics alternative can model both structured and unstructured data from various sources yet allows you to tightly couple the meaning of entity relationships as part of the representation of the relationship.
In this Webinar, we will discuss graph analytics, how it can be used to effectively embed the semantics of relationships among different entities within the structure, and provide an ability to both invoke traditional-style queries (to answer typical “search” queries modeled after known patterns) as well as enable more sophisticated, undirected analyses.
You will learn:
Individual, Student, & Team memberships available.