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The selected companies come from our massive data set of vendors and industry metrics. Yes, we use machine learning to analyze the industry in a detailed manner to determine a ranking for this list. We’re using a custom RankBoost algorithm adapted specifically for the big data community along with a plethora of propriety data sources.
When making a decision on which graph DB in which to invest, admins must take into account the development language the enterprise is going to use. There are different nuances among the database vendors for different use cases (some are more scale-out than others, for example), so organizations must scope this out ahead of time to make sure the most effective language and DB is selected for the corporate purpose.
Recently, we have seen the next-phase in the graph database evolution, with technology fulfilling the needs of e-Commerce by providing Deep Link Analytics. This enables customer intelligence in real time, along with powerful relationship analysis. With these real-time capabilities, e-Commerce sites can quickly synthesize and make sense of customer behavior. The result is the capture of key Business Moments, transient opportunities where people, businesses, data and “things” work together dynamically to create value used to personalize the customer experience, which leads to more transactions.
Today, enterprises use graph technology as a competitive edge for customer analytics, fraud detection, risk assessment and other complex data challenges. The technology offers the ability to quickly and efficiently explore, discover and predict relationships to reveal critical patterns and insights to support business goals.
Graph analytics is an ideal technology to support AML. Graphs overcome the challenge of uncovering the relationships in massive, complex and interconnect data. The graph model is designed from the ground up to treat relationships as first-class citizens. This provides a structure that natively embraces and maps data relationships, even in high volumes of data, and provides maximum insight into data connections and relationships.
TigerGraph, the startup that emerged last fall with a new native parallel graph database, released a free “developer edition” this week with the goal of giving potential users a test drive and a means of comparing the graph analytics platform with competitors like Amazon Neptune and Neo4j.
TigerGraph has announced the free developer edition of its graph analytics platform. The platform features enterprise graph massively parallel processing, support for Big Data, ability to write high-performance complex analytics queries, and the ability to continuous load over 100 GB per machine per hour.
TigerGraph produces a graph analytics platform for developers to create their own big data graph applications.
This technology stores all data sources in a single, unified multiple-graph store that can scale out and up to explore, discover and predict relationships. Unlike traditional graph databases, TigerGraph can scale real-time multi-hop queries to trillions of relationships.
If you’ve been watching the graph database space, you probably know TigerGraph. It’s one of the latest entries, coming out of stealth in September 2017. TigerGraph has managed to get attention in a number of ways: its massively parallel architecture, the benchmarks it has released, the names of its clients, and its strong presence in industry events.
“As graphs continue to go mainstream, the next phase of the graph evolution has arrived. Cypher vs. Gremlin is no longer the right question to ask. The time has come to rethink graph analytics with TigerGraph and GSQL, the most complete query language on the market”
There is a lot of industry chatter around the need for a standard graph query language — and we at TigerGraph, couldn’t agree more. New technology is coming to the market to finally address decade-old issues around scale and performance — providing real innovation, rather than a repacking and renaming of the old. GSQL was built from the ground up to deliver features needed by real-time big data, including: ease of use, familiarity of SQL-like syntax, and expressive power for solving real-world complex business problems—something legacy language options can not offer.