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How Neo4j Is Making Graph Technology More Accessible

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How Neo4j Is Making Graph Technology More Accessible

In today's news: Neo4j is announcing Cypher for Apache Spark and the Neo4j Native Graph Platform. Come learn about it all, according to Neo4j's Head of Product Marketing.

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Thanks to Jeff Morris, Head of Product Marketing at Neo4j, for taking me through two new additions that are making graph technology more accessible for developers, big data IT, and data scientists. The graph database ecosystem is evolving quickly as business owners express an interest in better data presentations.

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

The company 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 into their work, which will 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 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 Rathle, 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 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,” says 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.”

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

Neo4j has also unveiled its new Native Graph Platform. The platform adds analytics, data import and transformation, visualization, and discovery, all on top of Neo4j’s cross-industry graph database. This new offering 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.

“Our customers’ needs have changed. Many companies started with us for retail recommendation engines or fraud detection, but now they need to drive their next generation of connected-data to power complex artificial intelligence applications,” says Emil Eifrem, CEO of Neo4j. “Our customers not only need a high performance, scalable graph database, they need algorithms to feed it, they need visualization tools to illustrate it, they need data integration to dive deeply into their data lakes. Our connections-first approach, facilitated by this new Native Graph Platform, makes it possible for our customers, like NASA, ICIJ, Comcast, Telia, and eBay to reach for the stars. And that’s what today’s GraphConnect is all about.”

The Native Graph Platform

Neo4j is making data connections more accessible, effective, and actionable for organizations. Its new Native Graph Platform takes a connection-first approach to query, visualize, and analyze data, making it more meaningful, more useful, and more easily adopted.

The Native Graph Platform introduces new features and products to serve a broader range of individuals, including:

  • Performance boost: For new or existing users, the Native Graph Platform introduces a performance boost of the Neo4j 3.3 Database, which has expanded its use of native indexing, re-factored the Cypher query interpreter, and sped up write and update performance by as much as 55 percent over version 3.2 and 346% over version 2.3.

  • Server-to-server encryption: Data center and cloud administrators will rest easy knowing that Neo4j 3.3 Enterprise Edition clusters support intra-server encryption for all operations, across geographies and cloud zones.

  • Neo4j ETL: Data lake architects in IT will enjoy how fast it has become to prepare and import data into the graph platform using Neo4j ETL, which not only reveals data connections but also materializes these connections across a variety of relational sources and raw data formats living in Hadoop or other systems.

  • Advanced analytics for artificial intelligence: Allows data scientists to use Neo4j’s graph algorithms in developing AI logic for forward-looking projects, while they can also use Cypher on Apache Spark as a means to traverse gargantuan data volumes as graphs.

  • Integration with graph discovery and visualization applications: Allows business users to visualize, understand, analyze, and explore their graph data via a variety of industry-leading partners.

  • Neo4j Mission Control package: A developer and user’s launch pad for connecting to, exploring, and developing with a local copy of Neo4j Enterprise Edition and its associated platform components like APOC and algorithm libraries.

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Topics:
graph databases ,apache spark ,cypher ,neo4j ,database

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