Neo4j Launches Industry-First Native Graph Platform

Neo4j moves up the stack with advanced analytics for artificial intelligence applications and integrated visualization for non-technical users.

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At its GraphConnect conference in New York Oct. 24, graph database maker Neo4j introduced something called a Native Graph Platform, which adds analytics, data import and visualization to what many people consider the industry’s top graph database.

The company said its new offering expands Neo4j’s enterprise lineup by establishing relationships with a variety of new users and roles, including data scientists, big data IT, business analysts and line of business managers.

Some background: Graph search, an open-source database project built on all the networking people around the world do online every day, is the most far-reaching search IT to go mainstream since Google started storing up and ranking websites in 1999. Basically, a graph search database anonymously uses all the contacts in all the networks in which you work to help you find information.

Anything you touch, any service you use and anything people in your networks touch eventually can help speed information back to you. It avoids anything non-relevant that would slow down the search.

What a Graph Database Does

A graph database uses graph structures for semantic queries with nodes, edges and properties to represent and store data. Graph databases are used for storing, managing and querying complex and highly connected data. Moreover, the graph database architecture is particularly well-suited for exploring data to find commonalities and anomalies among large data volumes and unlocking the value contained in the data's relationships.

Hugely popular cloud services such as Google, Yahoo, Bing, Twitter, Facebook, Pinterest, LinkedIn, Google+ and Web-based email all use graph search. Thus, they have not only improved the way people interconnect, socialize and do business, they also help improve our search for information on the Web, because they are massive holders of these connections.

Whether for increased revenue, fraud detection or planning for a more connected future, building networks of connected data is a clear competitive advantage for companies. This will become even more evident in the future as machine learning, intelligent devices and real-time activities such as 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, CEO and co-founder Emil Efrem said.

“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,” Efrem said. 

Neo4j’s Native Graph Platform

New capabilities in Neo4j’s Native Graph Platform include:

  • Massive performance boost: For new or existing users, the Native Graph Platform introduces an 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 700 percent over version 2.2.  
  • Comprehensive server to server encryption: Neo4j 3.3 Enterprise Edition clusters support intra-server encryption for all operations, across geographies and cloud zones.
  • Neo4j ETL (currently available for preview): Data lake architects in IT will see how fast it has become to prepare and import data into the graph platform using Neo4j ETL, which not only reveals data connections, but materializes these connections across a variety of relational sources  and raw data formats living in Hadoop or other systems.
  • Advanced analytics for artificial intelligence: Enables 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 Desktop package: A developer and user’s mission control console for connecting to, exploring and developing with a local, registered copy of Neo4j Enterprise Edition and its associated platform components like APOC and algorithm libraries.

Neo4j also announced that it has donated an early version of Cypher for Apache Spark (CAPS) language toolkit to the openCypher project. This contribution will allow big data analysts to incorporate graph querying in their workflows, making it easier to bring graph algorithms to bear, dramatically broadening how they reveal connections in their data. 

Developers of Spark applications now join the users of Neo4j, SAP HANA, Redis Graph and AgensGraph, among others, in gaining access to Cypher, the leading declarative property graph query language. This also expands the tooling available to any developer under Apache 2.0 licenses.

Chris Preimesberger

Chris J. Preimesberger

Chris J. Preimesberger is Editor of Features & Analysis at eWEEK, responsible in large part for the publication's coverage areas. In his 12 years and more than 3,900 stories at eWEEK, he...