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Why is graph technology such a step forward? Virtually all existing risk assessment and monitoring systems are built on traditional relational databases, which store information such as counterparty, account, transaction, stakeholders, financial instruments and derivatives in separate tables, one for each type of business entity.
Machine learning has always been computationally demanding, and graph-based machine learning is no exception. With every hop, or level of connected data, the size of data in the search expands exponentially, requiring massively parallel computation to traverse the data. This is computationally too expensive for key-value databases which require too many separate lookups or RDBMS that struggle with too many slow joins. Even a standard graph database may not be able to handle deep link analytics on large graphs. A native graph database featuring massively parallel and distributed processing is needed.
NEW PRODUCT ANALYSIS: Unlike other graph databases that delve two to three levels deep into the connected data, TigerGraph’s pattern analytics is tuned to be efficient and tractable with the ability to go 10 or more levels deep into the interconnected entities. This is what AI and ML developers have been waiting for.
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