The analytics prowess of Anzo Graph Query Engine (AGQE), an enormously parallel in-memory querying graph database of semantic data, is almost immeasurable. The scale, scope, and speed of data elements that big data encompasses are boundless.
Together, AGQE and Big Data are a match made for one another. AGQE was expressly designed to conquer the Volume, Velocity, and Variety concerns that make big data analytics equally formidable and invaluable to the enterprise. By leveraging fundamental semantic technologies, it facilitates instantaneous query results of big data to enable analytic action in near-real time.
Volume and Velocity
The scale and speed of AGQE is tailored for big data’s two most immediate challenges. Its in-memory, parallel processing is central to the rapidity in which it parses through semantic statements in seconds. Similarly, the scalable nature of smart data lakes powered by semantic technologies—which are responsible for AGQE’s simultaneous querying capabilities instead of analyzing data linearly—enable it to account for billions of triples in moments. The relationship between volume and velocity is complementary; AGQE’s celerity allows it to process greater amounts of data in shorter time, accentuating the scalable semantic architecture of smart data lakes. The relationship between these two characteristics of AGQE is an integral aspect of detecting fraud in real-time or conducting clinical trials in life science.
AGQE’s semantic framework is essential to its success with the variety of big data sources, structures and data types. The engine relies on a standards-based environment facilitated by an expanding semantic model (supplemented by vocabularies and taxonomies) that inherently links any sort of data. Moreover, that model provides a granular degree of insight into the relationships between data, which AGQE exploits by delivering query results at the aforementioned speeds. Thus, users are provisioned with a contextualization of relationships between data to enhance these real-time results, gleaning patterns and points of relevance that might otherwise elude them.
Big Enterprise Data
Anzo Graph Query Engine accounts for each of the dynamics of big data that have made its analysis mandatory additions to data-driven processes across verticals. Its revolutionary relevance to big data is underscored by the fact that it actually betters those processes, drastically improving the query speed, scale, and scope of other mechanisms. In this regard, AGQE explicitly denotes the still-recent convergence of technologies that enable real-time querying of all enterprise data—which was previously impossible.
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