TigerGraph, a graph database is launching a native parallel graph (NPG) database which is said to revolutionize the graph database ecosystem with its new parallel architecture for native graph storage and processing that puts it ahead of the competition.
NPG is a platform for enterprise applications along with the availability of both its Cloud Service and GraphStudio, TigerGraph’s visual software development kit (SDK). As the world’s first and only Native Parallel Graph (NPG) system, Tiger Graph is a complete, distributed, graph analytics platform supporting web-scale data analytics in real time.
The Tiger Graph NPG is built around both, local storage and computation that support real-time Graph updates, and works like a parallel computation engine. These capabilities provide the following unique advantages:
• Fast data loading speed to build graphs-able to load 50 to 150 GB of data per hour, per machine
• Fast execution of parallel graph algorithms-able to traverse hundreds of millions of vertices/edges per second per machine
• Real-time updates and inserts using REST-able to stream2B+daily events in real time to a graph with 100B+ vertices and 600B+ edges on a cluster of only20 commodity machines.
• Ability to unify real-time analytics with large-scale offline data processing - the first and only such system.
TigerGraph’s Native Parallel Graph Technology (NPG) powers real-time deep link analytics for enterprises with complex and colossal amounts of data. It aims to be a graph database that could surpass the capabilities of existing graph databases by eliminating their limitations in terms of scalability, performance, and ease of use. They “come with limited capabilities for complex graph analytics and also come with limitations to deal with the big amount of data.” Yu Xu, founder and CEO, as quoted by Datanami.
Graph databases are increasingly becoming popular as a tool for data management, according to DBEngines. They owe this popularity to several reasons. First, they overcome the challenge of storing massive, complex and interconnected data by storing data in a graph format, including nodes, edges, and properties. They also offer advantages over both, traditional RDBMS and newer big data products, including:
• Faster joining of related data objects
• Greater dataset scalability
• More flexibility for evolving structures
TigerGraph is capitalizing this trend and expertise of its CEO Yu Xu, a PhD in distributed databases from University of California at San Diego to overcome competition in the graph database segment. There are other native graph databases, which means the data is stored in a graph-like manner and not added on after the fact, as with some graph solutions that reside atop Hadoop and NoSQL.
