Massively Scalable Graph Analytics Platform
Objectivity’s ThingSpan is a purpose-built, massively scalable graph analytics platform that leverages the open source stack by natively integrating with Apache Spark and the Hadoop Distributed File System (HDFS). It provides ultra-fast navigation and pathfinding queries against huge distributed graphs. ThingSpan also supports parallel pattern-finding and predictive analytics in combination with Spark components, such as MLlib, GraphX, and Spark SQL.
ThingSpan excels in a mixed workload environment, fusing metadata from real-time streaming and sensor-based data with its distributed graph of stateful information to provide “in-time” context. In essence, ThingSpan bridges the gap of open technologies by realizing the full potential of streaming Fast Data processed by Spark and static Big Data stored in HDFS.
Runs natively on YARN/Hadoop for enterprise Big Data and leverages Apache Spark for streaming Fast Data.
Designed to integrate and operate with major open source technologies.
End-to-end fusion process built around Spark to simplify development of applications.
Use object modeling to group data and relations into objects to drive extreme processing and beyond petabyte scalability.
Working with ThingSpan
With ThingSpan, developing applications is simple as connecting data sources, defining workflow and defining export datasets. ThingSpan automatically handles the complex process of discovery and acquisition of metadata from data sources, discern relationships from data models, physical-to-logical model mapping and rationalization, define model-to-model relationships and much more.