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Neo4j Unveils Cypher for Apache Spark and Industry-First Native Graph Platform.

Neo4j Announces Cypher for Apache Spark: Open Source Contribution Making 'SQL for Graphs' Available on Apache Spark

New contribution to the Hadoop ecosystem enables graph analytic capabilities for Spark, making Cypher available to the popular in-memory analytic engine.

Neo4j have announced that it has released the preview version of Cypher for Apache Spark (CAPS) language toolkit. This combination allows big data analysts to incorporate graphs and graph algorithms in their work, which will dramatically broaden how they reveal connections in their data. Spark joins Neo4j, SAP HANA, Redis and AgensGraph, among others in supporting Cypher, the world's leading declarative graph query language, as the widespread openCypher initiative expands its reach.

As graph-powered applications and analytic projects gain success, big data teams are looking to connect more of their data and personnel into this work. This is happening at places like eBay (recommendations via conversational commerce), Telia (smart home) and Comcast (smart home content recommendations). Until now, graph pattern matching has been unavailable to data scientists using Spark. Now, with Cypher for Apache Spark, these scientists can iterate easier and connect adjacent data sources to their graph applications much more quickly.

"Cypher for Apache Spark is an important milestone in both the pervasiveness of graph technology, and in the evolution of the Cypher query language itself," explains Philip Rath le, VP of product at Neo4j. "In making Cypher available for Apache Spark, we looked closely at the way data scientists work with Spark, and then in coordination with the openCypher group, used the latest features in the language to enable patterns of Cypher querying that would be most suitable for Apache Spark users. Cypher for Apache Spark enables a full composability language: enabling it to not only return tables of data, but also return graphs themselves as a result of queries. This allows data scientists to chain queries together with in-memory Spark-based graph representations between steps. This capability lets Spark users carry out sophisticated graph analytics much more easily, directly within their Hadoop environment."

Neo4j is releasing Cypher for Apache Spark under the Apache 2.0 license, in order to unite Cypher with the broadest community of big data analysts, data scientists and IT architects so they, too can experience the transformative influence of connected data.

"As data accumulates in lakes at accelerating speeds and in unprecedented volumes, the challenge of extracting value from it by traversing differentiated structures and inferring context from them grows exponentially," said Stephen O'Grady, analyst and co-founder at RedMonk. "Neo4j and its Cypher graph query language intend to be the de facto solution to precisely this problem."

Neo4j Unveils Industry-First Native Graph Platform, Evolving From Graph Database Into a Graph Company.

With widespread popularity of graph database, Neo4j moves up the stack with advanced analytics for artificial intelligence applications and powerful visualization for non-technical users

Neo4j have also unveiled its new Native Graph Platform. The platform adds analytics, data import and transformation, visualization, and discovery, all on top of Neo4j's wildly successful and cross-industry proven graph database. This new offering dramatically expands Neo4j's enterprise footprint by establishing relationships with a variety of new users and roles, including data scientists, big data IT, business analysts and line of business managers.

Whether for increased revenue, fraud detection or planning for a more connected future, building networks of connected data proves to be the single biggest competitive advantage for companies today. This will become even more evident in the future as machine learning, intelligent devices and real-time activities like conversational commerce are all dependent on connections. This is why Neo4j is extending the reach of its native graph stack, which has already seen success across multiple use cases with organizations ranging from NASA to eBay to Comcast to link together a broader set of users, functionality and technologies.
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Publication:Database and Network Journal
Date:Oct 1, 2017
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