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TigerGraph wants to be the graph database that companies choose when they are running up against scalability limits, and it will find itself in direct competition with the JanusGraph fork of Titan, which is backed by Google and IBM, and the DataStax fork as well. Neo4j is not going to sit still, either.
The new features include better integration with popular relational and NoSQL engines, support for software containers, one-click availability in Amazon Web Services Inc. and Microsoft Corp. Azure marketplaces and a new graph algorithm library.
TigerGraph has added integration with popular databases and data storage systems including: RDBMS, Kafka, Amazon S3, HDFS, and Spark (coming soon). TigerGraph said a github repository will host open source connectors to TigerGraph as they roll out.
TigerGraph just announced a Neo4j Migration Toolkit, which is largely based on translating Cypher, Neo4j’s query language, to GSQL.
TigerGraph has announced the latest release of its graph analytics platform. This release offers integrations with popular databases and storage systems, Docker and Kubernetes support, availability on the AWS Marketplace and Microsoft Azure, and a new graph algorithm library.
In addition to the platform update, the company also released a Neo4j Migration Toolkit. The toolkit will enable developers to transform Cypher queries into GSQL.
The Strata Data Award for the Most Disruptive Startup goes to TigerGraph, a fast graph analytics platform designed to unleash the power of interconnected data for deeper insights and better outcomes.
According to the company, TigerGraph supports a massively parallel processing architecture in which graph nodes — the company uses the less common term “vertices” — exhibit both compute and storage features; employs a parallel loader to speed data ingestion; and has fashioned a GSQL analytics language to produce parallel graph queries.
IceKredit has found those features useful in its efforts to expand the availability of credit ratings and risk assessments, according to Minqi Xie, vice president and director of modeling and business intelligence at the financial technology company.
What would you say most motivates you to do what you do?
My biggest motivation is to enable businesses of all sizes to gain deeper insights, as well as achieve better business outcomes from their data. It’s all about enabling them to achieve what was previously impossible with modern technical solutions designed for today’s needs.
The risk of money laundering spans the entire financial services ecosystem – banks, payment providers and newer cryptocurrencies, such as Bitcoin and Ripple and more. Given how much financial activity occurs every second, everyday, it’s important for banks and financial organizations to develop a robust AML strategy that is effective in stopping fraudsters in their tracks.
However, few people outside the AML compliance profession fully appreciate how hard it can be to get it right. Thankfully, there are new technologies such as graph analytics that can help. As we dive into this topic, let’s first consider key challenges contributing to this exceedingly difficult task.
Graph databases have gotten much attention due to their advantages over relational models (see discussions here). However, while different technical companies rush into this area, Amazon, Microsoft, Oracle, IBM, etc., it is getting more challenging to evaluate different vendor’s product when a project wants to embark a graph database. It is recommended that purchasers or practitioners take a grain of salt before embarking on the adventure of the graph database world! That does not mean it’s hard to find the right database. The criteria in this article contain some simple and achievable steps for readers to try and get the truth.
A traditional AML solution would not flag an alert for this new customer. However, graph-based features dig deeper to find that new account shares a phone number with several customers with SARs. The graph-based solution creates a new AML alert missed by the traditional AML solution, marking it high risk for further monitoring and investigation. In this way, our graph-based solution finds the false negatives, or hard-to-detect money laundering cases, that would be missed using traditional solutions.