Oracle Introduces Spatial and Graph Analytics for Hadoop and NoSQL

Bookmark and Share

Oracle is shipping a new big data product called Oracle Big Data Spatial and Graph. Spatial and graph analytics has been available as an option for Oracle Database for more than 10 years, and with this introduction the company is bringing spatial and graph analytics to Hadoop and NoSQL. 

Oracle Big Data Spatial and Graph includes two main components: a distributed property graph with over 35 parallel, in-memory analytic functions; and a range of spatial analysis functions and services to evaluate data based how near or far something is to one another, whether something falls within a boundary or region, or to process and visualize geospatial map data and imagery. With the spatial capabilities, users can take data with any location information, enrich it, and use it to harmonize their data.  Users can also group results based on spatial relationships, in addition to applying spatial services to cleanse, filter, normalize and process geospatial data sets. This allows analysts can discover relationships and connections among customers, organizations and assets.  

The Oracle Big Data Spatial and Graph technology was announced in an Oracle blog.

Oracle Big Data Spatial and Graph also provides an in-memory graph analysis engine. Because graph analysis is generally very time consuming because the computation routinely involves touching most of the nodes in the graph, the data access pattern is non-sequential (random) and can be time consuming. The property graph support in Oracle Big Data Spatial and Graph addresses this performance challenge by loading a graph into memory to carry out fast graph analytics. The graph engine comes pre-integrated with more than35 built-in (parallel) social network analysis (SNA) algorithms that address common graph problems. For scalability and performance, the in-memory, parallel analysis engine leverages the underlying persistent storage layer to provide efficient access and filtering of graph (or sub-graphs) into memory, resulting in fast analysis.

Oracle has made this capability available to Hadoop and NoSQL in order to support different kinds of datasets and the different workloads. Imagery and sensor data can be analyzed to support a variety of business benefits. Distributed sensors are generating vast amounts of raster imagery data in raw data formats that require large-scale geoprocessing for cleansing and preparation. Hadoop environments are well suited to storing and processing these high data volumes quickly, in parallel across MapReduce  nodes.

According to Oracle, the Big Data Spatial and Graph technology is not only for existing customers, but will also benefit users that require spatial or graph analytics on Hadoop, even if they don’t have any other Oracle software.