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Last year I predicted increased adoption of big data analytics in the cloud, along with continued investment and effort by cloud vendors to offer new solutions, especially with the graph data market growing hot. Indeed 2018 was a major year for big data analytics and the graph database market, as we saw new releases, as well as continued adoption and breakthrough use cases of graph analytics by organizations across the world.
TigerGraph, a recent arrival, is a “real-time native parallel graph database.” TigerGraph is available for deployment in the cloud or on-premises, it scales both up and out, it automatically partitions a graph within a cluster, it’s ACID compliant, it has built-in data compression, and it claims to be faster than the competition. As we’ll see, it uses a message-passing architecture that is inherently parallel in a way that scales with the size of the data.
Graph mechanisms and visual approaches to managing data can empower organizations to access data at the scale required for credible machine learning inputs, facilitate feature selection, and assist with overall data quality measures important for this statistical branch of AI.
TigerGraph—a fast graph analytics platform for the enterprise that offers new features that include seamless integration with popular databases and storage systems, support for Docker and Kubernetes containers, availability on the Amazon Web Services Marketplace and Microsoft Azure, and a new graph algorithm library
TigerGraph, a graph analytics platform for the enterprise, is introducing TigerGraph Cloud, a robust way to run scalable graph analytics in the cloud. Users can get their TigerGraph service up and running, tapping into TigerGraph’s library of customizable graph algorithms to support key use cases including AI and machine learning.
It is a company that is worth following, especially as it has the chance to grow even faster once the public cloud solution is available. This will open up its technology to a far wider audience that will look to leverage one of the fastest analytical platforms around.
Graph analytics platform provider TigerGraph also announced a new cloud at the conference. TigerGraph is meant to give users a cost effective way to run scalable graph analytics in the cloud.
The startup, which expects to double its workforce of 44 people by next year launched a cloud version of its subscription offering on Tuesday called TigerGraph Cloud. Since launching its first product in 2017, the company says its gross revenue has increased at an annual rate of 300 percent. TigerGraph is on track to generate more than $10 million in revenues in 2019.
At re:Invent yesterday, the company announced it’s making its product available in Database as a Service (DBaaS) form on AWS, as TigerGraph Cloud.
The company says that in addition to the service itself, TigerGraph Cloud includes “out-of-the-box starter kits for quicker application development – for use cases such as Anti-Fraud, Anti-Money Laundering (AML), Customer 360, Enterprise Graph analytics and more.”
The transition of graph databases to the cloud reflects growing demand for public cloud connections to a range of database platforms that has gained momentum over the last two years, beginning with managed NoSQL and relational databases.