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Graph databases are gaining adherents for their performance querying related data. “The graph model offers an inherent indexed data structure, so it never needs to load or touch unrelated data for a given query,” Yu Xu, founder and CEO of graph database developer TigerGraph noted in a recent overview of graph technologies. “This makes it an excellent solution for better and faster real-time big data analytical queries.”
The year of the graph is here. Do you really need a graph database, and, if yes, how do you choose one? It’s official: graph databases are a thing. That’s the consensus here on Big on Data among fellow contributors Andrew Brust and Tony Baer. When AWS enters a domain, it officially signals the upward slope of the hype cycle. It’s a bit like newfound land – first it’s largely unknown and inhabited by natives, then the pioneers show there are opportunities, then the heavyweights will try to colonize it.
The movement to Enterprise Knowledge Graphs has been accelerated in the last months by two new developments. One is the addition of the Neptune graph database to Amazon’s database portfolio. The second is the funding of both cloud and on-premise graph systems like TigerGraph and other Bay Area startups. Many of the graph-architects from Google, LinkedIn and Facebook (all using graph databases) are now venturing out on their own to develop solutions for the enterprise.
The past year has been a big one for the big data and analytics market. Major events included IPOs from Cloudera and MongoDB, further validating the market. And, we’re seeing enterprises continuing to recognize the fact that traditional solutions, such as relational databases (RDBMS), cannot meet all current needs in modern enterprise data management.
Here are Yu Xu’s additional predictions for the market in the year ahead:
Graph databases are suddenly hot. Amazon Web Services Inc.’s announcement this week of Neptune, a graph database in the cloud, is the latest in a series of recent indications that this once-niche technology is edging toward the mainstream of enterprise information technology. In September, startup TigerGraph Inc. released a high-speed native parallel graph database platform after raising $31 million in a series A funding round.
Graph databases are finding favor for their unique ability to represent complex relationships that rapidly navigate between elements in the database to discover correlations.
Yu Xu is the founder and CEO of TigerGraph, the world’s first native parallel graph database. Dr. Xu received his Ph.D. in Computer Science and Engineering from the University of Califoria San Diego. He is an expert in big data and parallel database systems and also graph databases. He has 26 patents in parallel data management and optimization. Prior to founding TigerGraph, Dr. Xu worked on Twitter’s data infrastructure for massive data analytics. Before that, he worked as Teradata’s Hadoop architect where he led the company’s big data initiatives.
Its native parallel graph technology (NPG) powers real-time deep link analytics for enterprises trying to graph and process really Big Data. It’s touting it as the only system on the market to unify real-time analytics with large-scale offline data processing for graphs.
TigerGraph’s security feature makes it simple for departments to define access to specific areas of a data set. This is achieved through the creation of subgraphs representing a subset of the vertices and edges in a graph. Each subgraph has its own administrator, can overlap with other subgraphs as needed, and can be treated in the same way as one, standalone physical graph database. Benefits include less effort and cost for administrators, who are able to support controlled data access across departments — all with only one physical cluster system to manage.
TigerGraph is bringing to market a third-generation graph database technology that uses massive parallelization to offer high performance, real-time computation. In addition, it offers ‘deep link analytics,’ which means it can traverse hundreds of millions of vertices/edges per second per machine across a complex graph structure, empowering deeper, faster processing of graph-based data than any of its competitors.