MarkLogic Previews Semantic Database

Mike Vizard
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Data Lakes: 8 Enterprise Data Management Requirements

At the MarkLogic World 2016 User conference this week, MarkLogic previewed an update to its core database that makes it possible to semantically define specific sets of data within the database that can then be logically invoked by an application programming interface (API).

Joe Pasqua, executive vice president of products at MarkLogic, says MarkLogic 9 will provide support for Entity Services that can be used to model data in a way that provides persistent meaning across different fields of data. IT organizations can then invoke those data sets using an Optic API to launch queries in a way that makes it simpler and faster to execute joins across entities, perform aggregations, and project data in different views. Those queries will accomplish that goal using a new underlying index and distributed execution engine across a cluster.

In addition, MarkLogic revealed that the forthcoming release will feature faster SQL queries, enable end users to redact documents, provide better encryption, and offer tools that will make it simpler to manage instances of MarkLogic running on multiple clusters. The encryption capability will be further bolstered by including an implementation of the Key Management Interoperability Protocol (KMIP) developed by Cryptsoft.

MarkLogic Dashboard

MarkLogic9 is essentially applying an API construct that was originally applied to applications to much more granular sets of data. The result not only makes it possible for developers to mix and match data more easily, it opens up the possibility for power users to more easily combine data in ways that won’t always require assistance from a developer. The new release isn’t scheduled to be available until the end of this year.

MarkLogic9 offers the potential to turn data into much more of a strategic resource. The next big challenge will be getting end users and IT staffs alike to think in terms of sets of data rather than traditional rows and columns. Of course, once that happens, those data sets may very well take on a life of their own inside and out of the organization.

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