Oracle zeroes in on Hadoop data with new analytics tool

Oracle Big Data Spatial and Graph will process data natively on Hadoop and in parallel using MapReduce or in-memory structures.

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The rise of Hadoop has created new oceans of data to explore, and Oracle has a new software product that's designed to help. Called Oracle Big Data Spatial and Graph, it brings new analytic capabilities to Hadoop and NoSQL.

Users of Oracle's database have long had access to spatial and graph analytics tools, which are used to uncover relationships and analyse data sets involving location. Aiming to tackle more diverse data sets and minimise the need for data movement, Oracle created the product to be able to process data natively on Hadoop and in parallel using MapReduce or in-memory structures.

There are two main components. One is a distributed property graph with more than 35 high-performance, parallel, in-memory analytic functions. The other is a collection of spatial-analysis functions and services to evaluate data based on how near or far something is, whether it falls within a boundary or region, or to process and visualise geospatial data and imagery.

Analysts can then discover relationships and connections among customers, organizations and assets, for example. And they can perform analyses that group results based on spatial relationships, such as filtering customer data based on how near one customer is to another.

Big Data Spatial and Graph can be deployed on Oracle's Big Data Appliance as well as other Hadoop and NoSQL systems.

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