Neo4j 3.1 Introduces Causal Clustering To Deliver Consistent View of Data In Graph Database Applications

Neo Technology recently announced the release of Neo4j 3.1, a significant release that features notable enhancements to Neo4j’s clustering architecture that enhance the scalability and performance of the Neo4j graph database platform. Importantly, Neo4j 3.1 introduces causal clustering to its database platform, which brings causal consistency to Neo4j in contrast to the eventual consistency paradigm that characterizes most contemporary NoSQL database platforms. Stronger than causal consistency, Neo4j 3.1’s causal consistency ensures that all nodes within a graph database are consistently updated, thereby empowering organizations to deliver updated and consistent data to end users as a result of their interactions with the database.  Neo4j’s VP of Products Philip Rathle remarked on the significance of the introduction of causal consistency to the Neo4j platform as follows:

The introduction of Causal Clustering in Neo4j 3.1, combined with Neo4j’s new security foundation, delivers a scalable, reliable, and secure enterprise graph platform like no other on the market. Neo4j 3.1 will enable organizations to leverage the connections in their data for operational decision making while complying with their internal and external security mandates.

The causal clustering specific to Neo4j 3.1 means that the graph travels as one ACID-transaction that ensures the consistency of the graph. Unique within the NoSQL and graph database space, Neo4j 3.1’s causal consistency delivers consistent data for applications, thereby facilitating the achievement of higher degrees of compliance across the enterprise in contrast to applications that rely on eventual consistency. Neo4j 3.1 also features enhanced security and access management functionality in the form of support for Active Directory/LDAP authentication and enhanced role-based access control. Nevertheless, the core innovation of Neo4j 3.1 is represented by its causal clustering architecture that augments its ability to serve the needs of graph-based applications for enterprise customers at scale. Currently in Beta, Neo4j 3.1 is expected to become generally available in Q4 of 2016.

Neo4j 3.0 Enhances Developer Productivity While Delivering Massive Scalability And Deployment Flexibility

This week, Neo4j announced the release of Neo4j 3.0, a watershed release that focuses on empowering developers to build graph-based applications faster and more effectively. Notably, this release features details of Bolt, a binary protocol that delivers higher throughput and lower latency with respect to access to the graph database. In addition, Neo4j 3.0 announces language drivers for Java, .NET, JavaScript and Python that interact with the Neo4j database in collaboration with the Bolt connectivity protocol. Using Bolt-based language drivers, Neo4j developers can write code in Java, .NET, JavaScript and Python on the Neo4j platform in ways that approximate the structure of the original syntax, thereby empowering developers to write applications in the coding languages with which they are deeply familiar. Neo4j 3.0 also inaugurates the capability to build Java Stored Procedures that enable developers to store and execute complex assemblages of code on the Neo4j database. Java Stored Procedures can be written in any JVM language and interact with the Neo4j database by means of the Bolt binary protocol. The combination of the release of Bolt, language drivers and stored procedures functionality means that Neo4j developers now have an enhanced range of development options for creating graph-based applications at scale. This release also announces the availability of Neo4j Sync, a cloud platform for the Neo4j Browser that synchronizes and stores developer settings and scripts in ways that give developers increased access to scripts as they move from one database or platform to another as shown below:

neo 4j 2

Neo4j’s browser sync also gives developers streamlined access to their library of Cypher queries. Moreover, Neo4j 3.0 delivers the ability to deploy graphs to any cloud environments, containers or on-premise deployments. With the release of Neo4j’s “redesigned data store” architecture, developers can now leverage the platform’s enhanced developer experience functionality to develop applications that scale while nevertheless preserving performance. Overall, the release delivers significant developer-oriented functionality that renders it easier to build, deploy and manage graph-based database applications at scale. In particular, the release of Bolt, language drivers for Java, .NET, Javascript and Python and Java Stored Procedures, in conjunction with Neo4j Sync, mean that developers now have an enriched set of tools for rapid development on the Neo4j platform that variously allows them to re-use their scripts and settings where possible in a scale-out, high performance development environment.

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:

Neo Technology Raises $20M In Series C Funding For Its Neo4j Graph Database Technology

Neo Technology today announced the finalization of $20M in Series C funding. Today’s Series C funding raise was led by Creandum with additional participation from Dawn Capital. Existing investors Fidelity Growth Partners Europe, Sunstone Capital and Conor Venture Partners all participated in the round. The funding will be used to expand sales operations, enhance product development and build the open source community supporting the Neo4j platform and its attendant partner ecosystem. The funding comes hot on the heels of a year of explosive growth for Neo Technologies and its vendor-led open source graph database, Neo4j. Neo Technology’s CEO and co-founder Emil Eifrem remarked on the company’s growth as follows:

There are two strong forces propelling our growth: one is the overall market’s increasing adoption of graph databases in the enterprise. The other is proven market validation of Neo4j to support mission-critical operational applications across a wide range of industries and functions.

Eifrem notes how Neo Technology’s growth has been fueled by increasing enterprise-wide adoption of graph databases in conjunction with Neo4j’s consistent demonstration of its ability to support a variety of production-grade environments. In a phone interview with Cloud Computing Today, Eifrem further remarked how one of the challenges for Neo Technology consists of developing an incisive sales outreach strategy given that almost every enterprise could benefit from the adoption of graphing technologies. Eifrem elaborated that Neo Technology has chosen to tackle the challenge of prioritizing its sales outreach efforts by focusing on use cases that include data-driven recommendations (in e-commerce and social networking, for example), master data management, identity and access management, graph based search, network and IT operations, the internet of things and pricing, while nevertheless remaining open to other client requests and interests. Since the launch of Neo4j 2.0 last January, Neo4j has experienced over 500,000 downloads and boasts thousands of enterprise-grade deployments featuring organizations such as Walmart, eBay, Earthlink, CenturyLink, Pitney Bowes and Cisco. Based on its impressive record in 2014 and the explosive proliferation of use cases for graphing technology, 2015 could well represent an inflection point for Neo Technologies as it consolidates its leadership in the graph database space by using its additional funding to gain more market traction while continuing to educate the industry on the value proposition of adopting Neo4j.

Neo Technology Announces Release Of Neo4j 2.1 With Enhanced ETL Functionality

This week, Neo Technology announces the release in general availability of Neo4j 2.1, the graph database that powers graph technology for companies such as eBay, Walmart, HP and National Geographic. Featuring pre-built ETL technology that facilitates the transformation of SQL or relationally-structured data into the Neo4j graph database technology platform, version 2.1 makes it even easier for enterprises to both transition from RDBMS systems to graph technologies as well as to augment existing Neo4j deployments. Version 2.1 features advanced functionality for mapping structured data from csv files into Neo4j with concomitant increases of speed up to a factor of 100. Emil Eifrem, CEO of Neo Technology, remarked on the innovation specific to Neo4j 2.1 as follows:

Neo4j 2.1 represents a major step forward in lowering the bar to graph database adoption for organizations who have massive amounts of data in their relational databases…While Neo4j is already renowned for its ease, scalability, and speed, the new built-in ETL capabilities enable the same ease and speed when moving data from an RDBMS into a graph. This will make it easier than ever for organizations to unlock the hidden value of their data, by leveraging the connections.

Neo4j competes with the likes of Titan, OrientDB, VelocityGraph, Apache Giraph and an increasing number of proprietary graph databases built by startups intent on preserving their intellectual property as part of their product development strategy. This week’s release consolidates Neo4j’s position as the industry’s most popular graph database technology by rendering it easier to transform SQL-based data into its platform, thereby streamlining the process of the production of graph databases based on incoming batches and streams of relational data. Forrester Research estimates that at least 25% of enterprises will have adopted a graph database by 2017.

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.

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.