Cross-posting this from the announcement of the new spatial and graph capabilities. You can get more detail on OTN.
The product objective is to provide 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.
Here are some feature highlights.
Oracle Big Data Spatial and Graph includes two main components:
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.
Read the Data Sheet
Read the Spatial Feature Overview