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Introducing The Latest Release Of TigerGraph: Fastest Performance For Graph Analytics

The release continues, “TigerGraph enables organizations to accelerate their time to value to gain maximum insight from massive amounts of interconnected data at lightning speed. With accelerated scale out capability and TigerGraph’s novel MPP implementation, TigerGraph provides the perfect fit for organizations to quickly adapt to the data needs of the future.

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Database trends and applications

TigerGraph Expands its Platform’s Availability on AWS and Azure

“Organizations already rely on TigerGraph to enable some of the worlds largest and most mission-critical use cases,” said Dr. Yu Xu, CEO and founder, TigerGraph. “This release makes it even easier for enterprises to connect TigerGraph to their existing infrastructure. It’s all part of our commitment of delivering the best graph engine on the market to power the applications that are paving the way of the future.”

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The Graph Database Poised to Pounce on the Mainstream

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.

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Silicon Angle

With latest release of its graph engine, TigerGraph bids for database mainstream

TigerGraph Inc. aims to nudge its graph database closer to the mainstream market with enhancements announced today.

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.

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Knowledge graphs beyond the hype: Getting knowledge in and out of graphs and databases

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.

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TigerGraph updates its graph analytics platform

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.

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TigerGraph, Dow Jones DNA and Vertica Analytics Named Winners of First-Ever Strata Data Awards

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.

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Graph Processing Gives Credit Analysis Firms An Edge

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.

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The Top 100 Innovators in Data and Analytics 2018 – Dr. Yu Xu

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.

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Leveraging graph analytics to combat money laundering

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.

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