Oracle Big Data Spatial and Graph - technical tips, best practices, and news from the product team

  • May 14, 2015

Announcing Oracle Big Data Spatial and Graph

Jean Ihm
Product Manager, Oracle Spatial and Graph

We have just shipped a new big data product.  Oracle Big Data Spatial and Graph offers a set of analytic services and data models that support Big Data workloads on Apache Hadoop and NoSQL database technologies.  For over a decade, Oracle has offered leading spatial and graph analytic technology for the Oracle Database.  Oracle is now applying this expertise to work with social network data and to exploit Big Data architectures.  

This post provides product feature highlights.  You can get more detail at the OTN website here.

Oracle Big Data Spatial and Graph includes two main components:

  • A distributed property graph database with 35 built-in graph analytics to
    • discover graph patterns in big data, such as communities and influencers within a social graph
    • generate recommendations based on interests, profiles, and past behaviors
  • A wide range of spatial analysis functions and services to
    • evaluate data based on how near or far something is to one another, or whether something falls within a boundary or region
    • process and visualize geospatial map data and imagery

Our objective is to provide the spatial and graph capabilities that are best suited to the use cases, data sets, and workloads found in big data environments.  Oracle Big Data Spatial and Graph can be deployed on Oracle Big Data Appliance, as well as other supported Hadoop and NoSQL systems on commodity hardware.  

Property Graph Data Management and Analysis

The property graph feature of Oracle Big Data Spatial and Graph facilitates big data discovery and dynamic schema evolution with real-world modeling and proven in-memory parallel analytics. Property graphs are commonly used to model and analyze relationships, such as communities, influencers and recommendations, and other patterns found in social networks, cyber security, utilities and telecommunications, life sciences and clinical data, and knowledge networks.  

Property graphs model the real-world as networks of linked data comprising vertices (entities), edges (relationships), and properties (attributes) for both. Property graphs are flexible and easy to evolve; metadata is stored as part of the graph and new relationships are added by simply adding a edge. Graphs support sparse data; properties can be added to a vertex or edge but need not be applied to all similar vertices and edges.  Standard property graph analysis enables discovery with analytics that include ranking, centrality, recommender, community detection, and path finding.

Oracle Big Data Spatial and Graph provides an industry leading property graph capability on Apache HBase and Oracle NoSQL Database with a Groovy-based console; parallel bulk load from common graph file formats; text indexing and search; querying graphs in database and in memory; ease of development with open source Java APIs and popular scripting languages; and an in-memory, parallel, multi-user, graph analytics engine with 35 standard graph analytics.

Spatial Analysis and Services – Enrich and Categorize Your Big Data with Location

With the spatial capabilities, users can take data with any location information, enrich it, and use it to harmonize their data.  For example, Big Data Spatial and Graph can look at datasets like Twitter feeds that include a zip code or street address, and add or update city, state, and country information.  It can also filter or group results based on spatial relationships:  for example, filtering customer data from logfiles based on how near one customer is to another, or finding how many customers are in each sales territory.  These results can be visualized on a map with the included HTML5-based web mapping tool.  Location can be used as a universal key across disparate data commonly found in Hadoop-based analytic solutions. 

Also, users can perform large-scale operations for data cleansing, preparation, and processing of imagery, sensor data, and raw input data with the raster services.  Users can load raster data on HDFS using dozens of supported file formats, perform analysis such as mosaic and subset, write and carry out other analysis operations, visualize data, and manage workflows.  Hadoop environments are ideally suited to storing and processing these high data volumes quickly, in parallel across MapReduce nodes. 

An Enterprise-Class Big Data Platform for Spatial and Graph Data Processing

Oracle has taken nearly two decades of experience working with spatial and graph technologies to deliver a new Big Data platform designed to handle the most demanding workloads. With the introduction of Oracle Big Data Spatial and Graph, developers and data scientists can manage their most challenging graph, spatial, and raster data processing in a single enterprise-class Big Data platform. 

As observed by the partner community,

“Big Data systems are increasingly being used to process large volumes
of data from a wide variety of sources. With the introduction of Oracle
Big Data Spatial and Graph, Hadoop users will be able to enrich data
based on location and use this to harmonize data for further
correlation, categorization and analysis. For traditional geospatial
workloads, it will provide value-added spatial processing and allow us
to support customers with large vector and raster data sets on Hadoop
--Steve Pierce, CEO, Think Huddle

“Oracle Spatial
and Graph is already a very capable technology. With the explosion of
Hadoop environments, the need to spatially-enable workloads has never
been greater, and Oracle could not have introduced Oracle Big Data
Spatial and Graph at a better time. This exciting new technology will
provide value-add to spatial processing and handle very large raster
workloads in a Hadoop environment. We look forward to exploring how it
helps address the most challenging data processing requirements.”
   - Keith Bingham, Chief Architect and Technologist, Ball Aerospace

Oracle Big Data Spatial and Graph represents a new opportunity for the Big Data platform.  By offering out of the box spatial enrichment services and analysis functions, as well as dozens of the most popular graph analysis functions, analysts and developers can now apply commercial-grade algorithms to their Big Data workloads.

Learn more about Oracle Big Data Spatial and Graph at the OTN product website:

Read the Data Sheet

Read the Spatial Feature Overview

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