The graph database market is red hot and growing rapidly. It’s exciting to see cloud service giants now also positioning themselves as graph database providers: IBM and Microsoft announced graph databases in their clouds last year, and this year respectively. This week, Amazon joins the growing market by announcing its own graph database service, Amazon Neptune.

IBM, Microsoft and now Amazon joining the market squarely validates the industry’s growing interest in graph technology to help analyze and provide insights on the colossal amounts of data companies are having to deal with today. Developers and enterprises are becoming increasingly aware of the benefits of graph analytics: easy and natural data modeling, easy-to-write queries to solve complex problems, and quick, powerful insights from connected data.

With cloud service as one of the biggest trends in computing, graph databases are setting an exciting direction for this technology’s growth. All other database types (RDBMS, data warehousing, document DB, and key-value DB) started primarily on-premises and were welcomed before database-as-a-service was established. Graph databases, on the other hand, have developed more slowly due to their increasingly more sophisticated data model over other NoSQL databases. With large cloud service providers, such as Amazon, now offering a graph database service, it’s evident that graph database adoption will speed up significantly in the months to come.

The convenience and flexibility of cloud service is only one factor. Each service is measured by the tasks it can perform, and if the price paid is relative to its value.

TigerGraph’s scalable and distributed platform can run both on-premise and in the cloud depending on your business needs. The solution is designed to combine both native graph storage and compute, supports real-time graph updates and offers built-in parallel computation. Additionally, TigerGraph’s architecture is modular and supports both scale-up and scale-out deployment models for distributed applications and has the ability to traverse hundreds of million of vertices/edges per second per machine and to traverse three to 10+ hops, orders of magnitude faster than traditional approaches.

TigerGraph emerged from stealth in September of this year with its breakthrough Native Parallel Graph technology to power real-time deep link analytics, the next stage of graph analytics.

These technical breakthroughs are essential for real-time analytics for the largest datasets, including anti-money laundering, real-time fraud prevention, supply-chain logistics optimization, influence/risk score computation, and more.

It’s exciting to see more companies emerge in the graph database market. 2018 will be an even more exciting year for the graph database market with TigerGraph poised to redefine graph analytics as we know it today!