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TigerGraph Cloud releases graph database as a service

TigerGraph, a graph database vendor, now offers a native cloud database-as-a-service product with its newest release.

With the general release of TigerGraph Cloud on Wednesday, TigerGraph introduced its first native graph database as a service.

In addition, the vendor announced that it secured $32 million in Series B funding, led by SIG.

TigerGraph, founded in 2012 and based in Redwood City, Ca., is a native graph database vendor whose products, first released in 2016, enable users to manage and access their data in different ways than traditional relational databases.

Graph databases simplify the connection of data points and enable them to simultaneously connect with more than one other data point. Among the benefits are the ability to significantly speed up the process of developing data into insights and to quickly pull data from disparate sources.

Before the release of TigerGraph Cloud, TigerGraph customers were able to take advantage of the power of graph databases, but they were largely on-premises users, and they had to do their own upgrades and oversee the management of the database themselves.

"The cloud makes life easier for everyone," said Yu Xu, CEO of TigerGraph. "The cloud is the future, and more than half of database growth is coming from the cloud. Customers asked for this. We've been running [TigerGraph Cloud] in a preview for a while -- we've gotten a lot of feedback from customers -- and we're big on the cloud. [Beta] customers have been using us in their own cloud."

Regarding the servicing of the databases, Xu added: "Now we take over this control, now we host it, we manage it, we take care of the upgrades, we take care of the running operations. It's the same database, but it's an easy-to-use, fully SaaS model for our customers."

In addition to providing graph database management as a service and enabling users to move their data management to the cloud, TigerGraph Cloud provides customers an easy entry into graph-based data analysis.

Some of the most well-known companies in the world, at their core, are built on graph databases.

Google, Facebook, LinkedIn and Twitter are all built on graph technology. Those vendors, however, have vast teams of software developers to build their own graph databases and teams of data scientists do their own graph-based data analysis, noted TigerGraph chief operating officer Todd Blaschka.

"That is where TigerGraph Cloud fits in," Blaschka said. "[TigerGraph Cloud] is able to open it up to a broader adoption of business users so they don't have to worry about the complexity underneath the hood in order to be able to mine the data and look for the patterns. We are providing a lot of this time-to-value out of the box."

Data interaction within a graph database is demonstrated on a TigerGraph Cloud starter-kit dashboard.
A TigerGraph Cloud starter-kit dashboard shows the data interaction within a graph database.

TigerGraph Cloud comes with 12 starter kits that help customers quickly build their applications. It also doesn't require users to configure or manage servers, schedule monitoring or deal with potential security issues, according to TigerGraph.

That, according Donald Farmer, principal at TreeHive Strategy, is a differentiator for TigerGraph Cloud.

It is the simplicity of setting up a graph, using the starter kits, which is their great advantage. Classic graph database use cases such as fraud detection and recommendation systems should be much quicker to set up with a starter kit, therefore allowing non-specialists to get started.
Donald FarmerPrincipal, TreeHive Strategy

"It is the simplicity of setting up a graph, using the starter kits, which is their great advantage," he said. "Classic graph database use cases such as fraud detection and recommendation systems should be much quicker to set up with a starter kit, therefore allowing non-specialists to get started."

Graph databases, however, are not better for everyone and everything, according to Farmer. They are better than relational databases for specific applications, in particular those in which augmented intelligence and machine learning can quickly discern patterns and make recommendations. But they are not yet as strong as relational databases in other key areas.

"One area where they are not so good is data aggregation, which is of course a significant proportion of the work for business analytics," Farmer said. "So relational databases -- especially relational data warehouses -- still have an advantage here."

Despite drawbacks, the market for graph databases is expected to grow substantially over the next few years.

And much of that growth will be in the cloud, according to Blaschka.

Citing a report from Gartner, he said that 68% of graph database market growth will be in the cloud, while the graph database market as whole is forecast to have at least 100 percent year-over-year annual growth through 2022.

"The reason we're seeing this growth so fast is that graph is the cornerstone for technologies such as machine learning, such as artificial intelligence, where you need large sets of data to find patterns to find insight that can drive those next-gen applications," he said. "It's really becoming a competitive advantage in the marketplace."

With respect to the $32 million TigerGraph raised in Series B financing, according to Xu it will be used to help TigerGraph expand its reach into new markets and accelerate its emphasis on the cloud.

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