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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.
2018 Predictions: Big Graphs and Deep Link Analytics
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 hot, but can they break relational’s grip?
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.
A Look at the Graph Database Landscape
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.
TigerGraph Builds a Bigger Graph Database
Five years in the making, TigerGraph came out earlier this month with its graph database platform featuring parallel processing and analytics.
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.
World’s First Native Parallel Graph Adds Role-Based Security
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: Massively Parallel Graph Database with Deep Link Analytics
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.
TigerGraph Emerges with Native Parallel Graph Database
As part of today’s launch, TigerGraph announced that it received $31 million in Series A funding, which Xu claims makes the company the second most well-funded graph startup after Neo4j (formerly Neo Technologies). The company also announced the availability of TigerGraph 1.0, as well as the launch of TigerGraph Cloud, a hosted version of the database that resides on Amazon Web Services. It also formally changed its name from SQLGraph.
TigerGraph Emerges with Native Parallel Graph Database
TigerGraph founder and CEO Yu Xu is no stranger to the challenges of building distributed computational engines. After getting his PhD in distributed databases from University of California at San Diego, he went up the street to Teradata, where he led the MPP (massively parallel processing) database team and also worked on big data projects. Then Xu headed off to Twitter, where he helped built the social media company’s distributed data infrastructure.
New database entrant TigerGraph boasts top speed, $31M in new funding
TigerGraph Inc., formerly known as GraphSQL Inc., said it has built the first native parallel graph database-based platform using proprietary technology that the company says juices performance up to 100-fold compared with other graph platforms.
