Graph database platform Neo4j raises $325M to inform decision-making

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Graph platform Neo4j today announced that it raised $325 million at an over $2 billion valuation in a series F round led by Eurazeo, with additional investment from GV. The capital, which brings the company’s total raised to date to over $500 million, will be put toward expanding Neo4j’s platform, workforce, and customer base, the company says.

Markets and Markets anticipates the graph database market will reach $2.4 billion by 2023 from $821.8 million in 2018. And analysts at Gartner expect that enterprise graph processing and graph databases will grow 100% annually through 2022, facilitating decision-making in 30% of organizations by 2023. Graph databases and graph-oriented databases leverage graph structures for semantic queries, with nodes, edges, and properties that store and represent data. They’re a type of non-relational technology that depicts the relationships connecting various entities — like two people in a social network, for instance — and that can analyze interconnected data.

Neo4j offers an open source NoSQL graph database written in Java and Scala with a declarative query language called Cypher. It supports a number of applications, including identity and access management, knowledge graph augmentation, and network and database infrastructure monitoring, as well as risk reporting compliance and social media graphs.

Neo4j’s founders encountered performance problems with relational database management systems, which inspired their decision to build the first Neo4j prototype. Emil Eifrem, the founder and CEO of the company, sketched what today is known as the property graph model on an airplane napkin during a flight to Mumbai in 2000. A property graph is a type of graph where relationships are not only connections but carry a name and some properties.

“Neo4j has been downloaded more than 120 million times by over 200 million developers, more than 50,000 of which are trained. Our main competition is legacy SQL systems that are bogged down by low-performance queries,” Eifrem told VentureBeat via email. “We see competition as a good thing, as smaller companies tend to stake out market niches that might go unidentified by the larger leaders. Competition fuels innovation, as it motivates every vendor to be better, and that’s good news for customers. ”

On the backend

Neo4j features constant time traversals that can scale up to billions of nodes, a flexible property graph schema that adapts over time, and drivers for popular programming languages like JavaScript, .NET, Go, and Python. It’s compliant with ACID (atomicity, consistency, isolation, and durability) requirements, meaning it guarantees database transactions even in the event of power failures and errors. And on the AI front, it supports high-performance graph queries on large datasets.


Above: An example of a graph database created with the Neo4j platform.

Image Credit: Neo4j

Development on Neo4j began in 2003, and it’s been publicly available since 2007 in two editions: a free Community edition and an Enterprise edition. The Enterprise edition adds hot backups, parallel graph algorithms, LDAP and active directory integration, multi-clustering, larger graphs, and more.

“Graph technologies are a purpose-built method for adding and leveraging context from data and are increasingly integrated with machine learning and AI solutions in order to add contextual information … Graphs also serve as a source of truth for AI-related data and components for greater reliability. This is especially important for AI bias. Providing these context and connections to AI systems to have more situationally appropriate outcomes mirrors the decisions in the same way humans do,” Eifrem said. “Graphs can also greatly increase the accuracy of machine learning models with the data you already have. Graphs increase the dimensionality of your data by adding relationships which we know are highly predictive of behavior.”

Graph database growth

Gartner predicts that graph processing and graph databases “will grow at 100% annually over the next few years to accelerate data preparation and enable more complex and adaptive data [analytics].” In a Neo Technology survey conducted by Evans Data Corporation, 49% of companies said that they anticipate taking on real-time recommendations through graph databases in the next two years. Fifty-eight percent said that they’re already using graph databases at scale.

Data analytics is the science of analyzing raw data to extract meaningful insights. A range of organizations can use data to boost their marketing strategies, increase their bottom line, personalize their content, and better understand their customers. Businesses that use big data increase their profits by an average of 8%, according to a survey conducted by BARC.

Startups like TigerGraph, MongoDB, Cambridge Semantics, DataStax, and others compete with Neo4j in a graph database market expected to be worth $2.4 billion by 2023, in addition to incumbents like Microsoft and Oracle. Even Amazon threw its hat in the graph database ring in November 2017 with the launch of Neptune, a fully managed graph database powered by its Amazon Web Services division.

But Neo4j — which has over 500 employees — has achieved a few pretty impressive milestones, including more than 3 million downloads as of November 2018 and over 300 enterprise subscription users. The company counts among its current and previous customers Lyft, Walmart, eBay, Adobe, Orange, Monsanto, IBM, Microsoft, Cisco, Medium, Airbnb, NASA, and the U.S. Army.

Neo4j customer Meredith Corporation says it scaled its Neo4j graph to analyze 30 billion nodes of digital traffic and has tested capacity to accommodate 100 billion in the future. Recently, Neo4j itself demonstrated real-time query performance against a graph with over 200 billion nodes and more than a trillion relationships running on over a thousand machines.

Last year, Neo4j introduced Neo4j for Graph Data Science, which the company claims is the first data science environment built to harness the predictive power of relationships for scenarios like fraud detection, customer and patient journey tracking, and drug discovery. It arrived alongside Neo4j Aura Professional on Google Cloud Platform, a fully integrated graph database service on the Google Cloud Marketplace designed for small and medium-size businesses. Neo4j also recently debuted the Neo4j BI Connector, which presents live graph datasets for analysis within popular business intelligence technologies including Tableau and Looker. And the company rolled out the Neo4j Connector for Apache Spark, an integration tool to move data bi-directionally between the Neo4j Graph Platform and Apache Spark.

In addition to Eurazeo and GV, Creandum also participated in San Mateo, California-based Neo4j’s latest fundraising round, as did Greenbridge Partners, DTCO, Lightrock, and One Peak Partners. Neo4j previously closed a $40 million venture round led by One Peak.


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