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Home » How Data Relationships Are Driving Graph Databases—and Big Investments
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How Data Relationships Are Driving Graph Databases—and Big Investments

John FoleyBy John FoleyDecember 1, 2021Updated:August 4, 20234 Mins Read
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Graph Databases
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Neo4j, an emerging company in a little-understood part of the database market, earlier this year secured what it described as the largest investment in a private company in database history, $325 million.

It was a remarkable windfall for a company that some might consider a niche player in the broader $50 billion database market, but it highlights two fundamental truths about the fast-growing cloud database sector:

  • One, the arcane world of graph databases (Neo4j’s specialty) is quickly becoming mainstream, as data connections become ever more important across industries.
  • And two, the investment community sees a big opportunity as businesses look for new and better ways to manage petabytes of data and to use that data in applications such as recommendations and supply chains

At the time of the funding announcement in June, Neo4j co-founder and CEO Emil Eifrem described the investment as an “inflection point” in the database market, with growth coming from “new generations of databases that address today’s data challenges in unprecedented ways.”

Now, Neo4j is looking to aggressively expand its base of graph developers. The company has announced a free tier of its AuraDB cloud database service, which makes it easy for developers to get started, without even a credit card. There are also Professional and Enterprise editions of AuraDB for teams that want to move beyond prototype development projects. 

Social Networks and More

Graph databases are organized around the concept of nodes—with each node representing an entity such as a person or product—and the relationships between and among nodes. Social networks are sometimes offered as examples of how graph databases connect the dots, but there are many other use cases: fraud detection, product recommendations, physical networks, and more.

Gartner forecasts the rapid growth of graph databases. In a report published earlier this year, the advisory firm estimated that graph technologies were used in only 10% of data and analytics innovations, but predicted that will jump to 80% by 2025.

Other vendors in the market include Dgraph and TigerGraph, as well as AWS with its Amazon Neptune database. Oracle supports graph capabilities as part of its “converged database” model.

Other developments include:

  • Memgraph on November 24 announced Memgraph 2.1, the latest release of the company’s graph application platform. In October, Memgraph raised $9 million in seed funding, led by Microsoft with participation by In-Q-Tel and others.
  • ArangoDB on October 6 disclosed $27.8 million in Series B funding, bringing its total funding to $47 million. The company describes its software as “a graph and beyond” database for graph, document, and search workloads.
  • Nebula Graph is an open-source graph database from VEsoft, which announced in June that it has received $8 million in pre-Series A funding led by Redpoint China Ventures.

Graph Data Science

Neo4j is included in my list of the Cloud Database Top 20. Founded in 2007, and with $515 million in total funding, the company is not exactly a startup. Neo4j counts 800+ enterprise customers, including 75% of Fortune 100 companies, in banking, retail, hospitality, insurance, auto manufacturing, and telecom.

A major focus for Neo4j is graph data science, which entails graph-native machine learning and analytics. Neo4j provides algorithms for community detection, similarity, pathfinding, and “centrality,” which is based on the relative influence of nodes.

Some of the most interesting graph applications involve people and knowledge management. NASA is using Neo4j’s technology for talent mapping and a knowledge graph, as described in this article by ZDNet. And Personnel Today explored whether graph databases could become a platform for “human-centric” HR systems.

Expanding Cloud Model

Neo4j introduced its managed cloud service Aura in 2019, and it’s now expanding to multi-clouds. “You’re going to see very aggressive investment in the cloud,” Ramanan Balakrishnan, Neo4j’s senior director of product marketing, told me following the company’s funding announcement.

Originally launched on Google Cloud, Aura is available on AWS, with plans for Microsoft Azure. Neo4j customers running on AWS include Levi Strauss, Sainsbury’s, and The Orchard.

Other areas of investment include graph data science and expanding Neo4j’s market reach, according to Balakrishnan.

Neo4j demonstrated the remarkable scalability of its database with a graph that comprised more than 200 billion nodes and 1 trillion relationships. That’s the equivalent of a social graph detailing how every person on Earth is connected with each other, according to Neo4j.

With growing zettabytes of data around the world and data relationships of increasing complexity, you can see why the adoption of graph databases is expected to keep growing.

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John Foley
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John is founder of the Cloud Database Report and host/Sr. Analyst for the Data Revolution channel on Acceleration Economy. For more than 20 years, John has covered database management systems and data warehouses, and the ongoing challenges businesses face with data quality, policy, performance, and scale. He also writes and podcasts regularly about the latest trends and innovations in cloud database platforms, including data integration, analytics, machine learning, data transformation, autonomous management, and hybrid clouds. John digs into real-world use cases and best practices that lead to data-driven insights and actions. Recently he helped drive strategy as a communications leader at Oracle, IBM, and MongoDB. That first-hand industry experience informs his perspective and analysis.

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