Spatial and graph analytic services and data models that support Big Data workloads on Apache Hadoop and NoSQL database technologies.
Oracle Big Data Spatial and Graph includes two main components: A property graph database and 35 built-in graph analytics that discover relationships, recommendations and other graph patterns in big data and a wide range of spatial analysis functions and services to evaluate data based on 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.
Applying Hadoop Spatial Analysis To Big Data
In this lab, you will learn how to use and apply geo-enrichment and data harmonization services, and geographic and location analysis functions for categorizing and filtering your big data workloads. It will show you how to:
Applying Hadoop Spatial Analysis to Big Data (January 2018) (Zip file - 35.3MB)
Applying Spark Spatial Analysis To Big Data
In this lab, you will learn how to explore and analyze data from HDFS, from the Oracle database or streamed data. It will show you how to:
Applying Spark Spatial Analysis to Big Data (January 2018) (Zip file - 9.3MB)
Gain Insight into Your Graph Data
This hands-on lab will show you how to use the key APIs to manipulate property graph data stored in either Oracle NoSQL Database or Apache HBase, perform powerful text query to find vertices and edges of interest, and to pick suitable analytics to gain insight into your data. Specifically, it will use a real-world social graph to illustrate:
Accessing the Hands-on Lab:
|Graph Visualization Tools
|Tom Sawyer Software
|Tom Sawyer Perspectives is an advanced graphics-based Software Development Kit (SDK) for federating data from multiple sources and building enterprise-class graph and data visualization and analysis applications.
|Cytoscape Network Visualizer
|"Cytoscape is an open source software platform for visualizing complex-networks and integrating these with any type of attribute data. Plugins are available for various kinds of problem domains, including bioinformatics, social network analysis, and semantic web."