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Redwood City, Calif.-based TigerGraph, which bills itself as “the only scalable graph database for the enterprise,” on March 21 will introduce its latest release, TigerGraph 2.4. This is way more than a simple point release; it’s the first time 7-year-old TigerGraph has combined graph pattern matching with real-time deep link analytics — a mix ideal for fraud and money laundering detection, security analytics, personalized recommendation engines, artificial intelligence and machine learning.
TigerGraph has announced that OpenCorporates has migrated its back end database from Neo4j to TigerGraph. This is bold decision by OpenCorporates as it demonstrates that they were unable to work with Neo4J, a competitor graph database and migrated to an alternate platform.
The vendor OpenCorporates has chosen to switch to is TigerGraph, an up and coming startup for which OpenCorporates is a showcase of what it can do. Taggart explained that this forms the basis of a mutually beneficial relationship.
TigerGraph acknowledged the nature of OpenCorporates work, as well as the high profile that comes with it, and has provided its platform to OpenCorporates under special terms. OpenCorporates wins by migrating to a platform that works for them, TigerGraph wins by getting exposure and promoting GSQL, its query language.
TigerGraph, the fast graph analytics platform for the enterprise, announced that OpenCorporates, the open database of companies has chosen TigerGraph as its backend graph database. The move enables OpenCorporates to better support investigative queries over its open database containing records on more than 170 million companies.
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.