Neo Technology Announces Neo4j 2.3 Marked By Ability To Manage Intelligent Applications At Scale As Neo Technology Partners With IBM And Open Sources Cypher

Graph database leader Neo Technology today announced the availability of Neo4j 2.3, a partnership with IBM as well as the open sourcing of Cypher, its query language for graphs. Neo4j 2.3 features enhanced abilities to create massive graphs for rapidly scaling, intelligent applications that automate the application of business rules to real-time updates to data from disparate sources. The latest release supports the scale-out of the implementation of intelligent rules that enrich data relationships amongst application-specific entities. Neo4j 2.3’s improved ability to manage applications at scale features enhanced capabilities to develop queries in conjunction with improved Cypher performance and a more intelligent query planner. In addition to intelligent management of rapidly scaling applications, this release delivers expanded schema and metadata functionality that allows customers to more effectively manage and perform analytic operations on data. Neo4j 2.3 also features an integration with Spring Data, a slew of improvements to the Cypher query language and support for Docker.

In conjunction with the release of Neo4j 2.3, Neo4j also announces a partnership with IBM to render Neo4j available on IBM POWER8. The partnership features the deployment of Neo4j on a massive in-memory platform that can expediently support use cases that include internet of things data, supply chain or fraud-related analytics and updates to billions of data points from sources spanning the globe via real-time data ingestion. As noted in the press release, “IBM Power Systems can provide up to 56 terabytes of extended memory space with CAPI flash architecture on a single machine,” thereby rendering possible the creation of graphs of a magnitude and scale not seen to date. The IBM POWER 8 allows customers to not only create massive graphs and graphical relationships between data, but to also act upon the insights delivered by those graphs in near real-time, thereby minimizing the time lag between the development of actionable business intelligence and the execution of proactive responses to data-driven events and insights. In yet another announcement, Neo4j will be open sourcing Cypher, its query language for graphs, as openCypher, a project that stands to revolutionize graph analytics in much the same way as SQL did for relational databases several decades ago. openCypher boasts an impressive roster of initial supporters that include Oracle, Databricks, Tableau, GraphAware, GrapheneDB, Graph Story and Information Analysis Incorporated (IAI). Ion Stoica, CEO of Databricks, remarked on the open sourcing of Cypher as follows:

Graph processing is becoming an indispensable part of the modern big data stack. Neo4j’s Cypher query language has greatly accelerated graph database adoption. We look forward to bringing Cypher’s graph pattern matching capabilities into the Spark stack, making graph querying more accessible to the masses.

As Stoica notes, Databricks has plans to integrate Cypher’s functionality into the Spark stack as part of the larger project of creating an integrated set of big data tools and applications. The interest had by Databricks in integrating Cypher into the Spark portfolio underscores the value of the query language developed by Neo4j and illustrates the significance of Neo4j’s graphing technology more generally for contemporary big data analytics. As such, the release of Neo4j 2.3, its partnership with IBM and the open sourcing of its query language Cypher marks a milestone in Neo4j’s evolution as it emphatically asserts its centrality to the big data revolution and demonstrates enhanced abilities to manage massive graphs and the automation that allows their applications to scale. The screenshot below illustrates Neo4j 2.3’s user interface for understanding graph-based data:


Neo4j Adopted By Retail Giants eBay and Walmart For Real-Time, E-commerce Analytics

Neo Technology recently announced that retail giants such as eBay and Walmart are using graph database Neo4j in production-grade applications that improve their operations and marketing analytics. In a recently published case study, Neo Technology revealed how eBay’s e-commerce technology platform acquisition, Shutl, leverages Neo4j to expedite delivery to the point where customers can enjoy same day delivery in select cases. Shutl constitutes the technology platform that undergirds eBay Now, a service that delivers products in 1-2 hours from local stores by means of relationships between couriers and stores. eBay decided to make the transition from MySQL to Neo4j because:

Its previous MySQL solution was too slow and complex to maintain, and the queries used to calculate the best route additionally took too long. The eBay development team knew that a graph database could be added to the existing SOA and services structure to solve the performance and scalability challenges. The team turned to Neo4j as the best possible solution on the market.

According to Volker Pacher, Senior Developer at eBay, eBay found that Neo4j enabled dramatic improvements in its computational and querying ability:

We found Neo4j to be literally thousands of times faster than our prior MySQL solution, with queries that require 10-100 times less code. Today, Neo4j provides eBay with functionality that was previously impossible.

eBay’s current ecommerce technology platform leverages Ruby, Sinatra, MongoDB, and Neo4j. Importantly, queries “remain localized to their respective portions on the graph” in order to ensure scalability and performance. Walmart, meanwhile, uses Neo4j to understand the online habits of its shoppers in order to deliver more relevant real-time product recommendations for their online shoppers. Neo4j’s adoption by eBay and Walmart symptomatically illustrates how graph databases are disrupting the nature of real-time analytics, a trend further underscored by Pivotal HD 2.0’s integration of GraphLab into its offerings, and the use of graphing technologies by startups such as Aorato.

DataRPM Closes $5.1M In Series A Funding For Natural Language Search Big Data Analytics Platform

DataRPM today announced the finalization of $5.1M in Series A funding in a round led by InterWest Partners. DataRPM specializes in a next generation business intelligence platform that leverages machine learning and artificial intelligence to facilitate the delivery of actionable business intelligence by means of a natural language-based search engine that allows customers to dispense with complex, time consuming data modeling and query production. DataRPM stores customer data within a “distributed computational search index” that enables its platform to apply its natural language query interface to heterogeneous data sources without modeling the data into intricate taxonomic relationships or master data management frameworks. Because DataRPM’s distributed computational search index empowers customers to run queries against different data sources without constructing data schemas that organize the constituent data fields and their relationships, it promises to accelerate the speed with which customers can derive insights from their data. Not only does the platform deliver a natural language interface, but it also performs data visualization of the requisite Google-like searches as illustrated below:

In an interview with Cloud Computing Today, DataRPM CEO Sundeep Sanghavi noted that its natural language search functionality is based on proprietary graphing technology analogous to Apache Giraph and Neo4j. The platform operates on data in relational and non-relational formats, although it currently does not support unstructured data. Available via both a cloud-based and on-premise deployment solution, DataRPM promises to disrupt Big Data analytics and contemporary business intelligence platforms by dispensing with the need for complex, time consuming and expensive data modeling as well as empowering business stakeholders with neither SQL nor scripting skills to analyze data. Today’s funding raise is intended to accelerate the company’s go-to-market strategy and correspondingly support product development in conjunction with the platform’s reception by current and future customers.

DataRPM belongs to the rapidly growing space of products that expedite Big Data analytics on Hadoop clusters as exemplified by the constellation of SQL-like interfaces for querying Hadoop-based data. That said, its natural language query interface represents a genuine innovation in a space dominated by products that render Hadoop accessible to SQL developers and analysts, as opposed to data savvy stakeholders with Google-like querying expertise. Moreover, DataRPM’s natural language search capabilities push the envelope of “next generation business intelligence” even further than contemporaries such as Jaspersoft, Talend and Pentaho, which thus far have focused largely on the transition within the enterprise from reporting to analytics and data discovery. Expect to hear more about DataRPM as the battle to streamline and simplify the derivation of actionable business intelligence from Big Data takes shape within a vendor landscape marked by the proliferation of analytic interfaces for petabyte-scale relational and non-relational databases.

Neo Technology Announces Release of Neo4j version 2.0 Graph Database Platform; Notes Use of Neo4j By Zephyr Health

Neo Technology today announced the release in general availability of version 2.0 of its graph database technology platform, Neo4j. The Neo4j graph database platform enables users to find connections between and amongst data points in high velocity and variety datasets “where the relationships between constituent data points are so numerous and dynamic that they cannot easily be captured within a manageable schema or relational database structure. Graph databases contain “nodes” or “vertices” and “edges” that indicate relationships between the different vertices/nodes.” Neo4j 2.0 features the addition of three notable features: (1) labels are now part of the data model and allow data scientists and developers to tag and index data for the purpose of more effectively understanding relationships between datasets; (2) enhancements to Cypher, the declarative query language used for the development of Neo4j graph applications; and (3) an interactive browser and query environment with a visual interface for data discovery.

Today, Neo Technology also announced that Zephyr Health is using Neo4j to power its cloud-based analytics platform:

The Zephyr analytics platform allows pharmaceutical makers, medical device manufacturers, and other health care customers, to discover unique connections across their data that can advance their R&D, clinical trials, and marketing. For instance, Zephyr’s engine helps pharmaceutical companies find the right doctors for a clinical trial by linking private and public data — such as specialty, geography, and clinical trial history.

Zephyr Health chose the Neo4j platform as the basis for its big data analytics environment because of its need to make connections between disparate data sets in real-time, as well as the highly dynamic nature of its datasets about hospitals and physicians. According to Neo Technology’s press release, Neo4j has effectively scaled in conjunction with the exponential growth of Zephyr’s datasets and delivered a solution that allows Zephyr’s business users to “be their own data scientists” by way of its data discovery and interactive browser functionality.

Zephyr Health’s adoption of Neo4j represents just one data point on a larger canvas of enterprise adoption of Neo4j as illustrated below:

The verticals from left to right illustrate Neo4j’s adoption in industries over and beyond verticals that traditionally use graph databases such as social media, online data and transportation. The larger point here is that, graph database technology—whether via Apache Giraph, Neo4j or otherwise—has arrived within the enterprise as a means of managing relationships between richly associative, dynamic, multivalent datasets in ways that enable connections and the inference of probabilistic relationships between nodes within the graph in ways that exceed the analytic capabilities of relational databases. The industry should expect use cases such as Zephyr Health’s elaboration on its use of Neo4j to proliferate as users of graph database technologies becoming increasingly comfortable explaining its business value and significance.