Cambridge Semantics, a provider of graph-based Smart Data discovery and exploratory analytic solutions, announced the release of an enhanced product version of its Anzo Smart Data Lake solution with the ability to combine, manage, and analyze all of an enterprise’s data in one place.
The Anzo Smart Data Lake for Enterprise enables companies to take advantage of “enterprise knowledge graphs” of unlimited size, similar to Facebook or LinkedIn, but targeted specifically to an organization's information assets. Featuring an Enterprise Smart Data Catalog containing all of an organization’s data -- structured, unstructured, internal or external -- the enterprise knowledge graph is presented as trillions of interlinked facts made available in any combination, on-demand to approved users.
Built upon the suite of Anzo products, including the Anzo Smart Data Platform, Anzo Graph Query Engine, Anzo Unstructured, and Anzo Smart Data Manager, additional features of the Anzo Smart Data Lake for Enterprise include:
- Elimination of time-consuming data integration and provisioning issues of custom extraction queries and data views previously faced by IT departments;
- Cloud-delivered, interactive self-service analytics and data-on-demand extracts driven by business-user level understanding of the rich, context-laden data without the need to learn a query language;
- A governance and provenance model that provides a full “chain of custody” for all data managed, preventing an organization from creating an overwhelming and unusable “data swamp”;
- A hybrid cloud computing model that supports on-site private clouds as well as IaaS providers such as Amazon Web Services and Google Compute Engine;
- A seamless integrated multi-repository system that allows companies to add and remove fully encrypted data-set repositories as needed to co-located data with the cheapest available cloud compute;
- Built on W3C Open Data Standards platform, preventing vendor lock-in;
- Is usually delivered incrementally as an overlay technology, above existing enterprise IT systems.