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Graph technology is making inroads in the financial services sector as the emerging database platform finds applications in areas ranging from risk assessment to portfolio management.
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. This offers just one reason why the graph data market is red hot and growing. 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 the fastest growing category in all of data management, according to DB-Engines.com, a database consultancy. Since seeing early adoption by companies including Twitter, Facebook and Google, graphs have evolved into a mainstream technology used today by enterprises in every industry and sector.
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 is bringing to market a third-generation graph database technology that uses massive parallelization to offer high performance, real-time computation.
A startup named TigerGraph emerged from stealth today with a new native parallel graph database that its founder thinks can shake up the analytics market.