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.
Neo4j is an example of a native graph database that was built from the ground up to store pieces of data as nodes and expresses their connectedness through edges. The market also has parallel graph databases available like Giraph that runs on Hadoop. These systems are, however, limited by their abilities to handle complex workloads.
• non-parallel graph databases running on symmetric multi-processor (SMP) systems cannot handle the complex workloads that today’s companies want to run against graph databases
• The core problem impacting parallel but non-native systems like Giraph is that they cannot be updated easily.
TigerGraph solves the above issues by being able to run on more than 1,000 nodes of commodity Intel processors, which gives it the capability to run queries with up to 20 hops with sub-second response time. It's native hence it allows the user to add the data to the database continually, without needing to re-run ETL processes.
Xu said the company will introduce new features to their cloud offering along with more support for various query languages in the near future. Formerly known as GraphSQL, TigerGraph is a breakthrough representing the next stage in the graph database evolution, according to the company. The platform is a complete, distributed, parallel graph computing platform that supports web-scale data analytics in real-time.