Personal blog of Yzmir Ramirez

Databases run the world, but database products are often some of the most mature and venerable software in the modern tech stack. Designers will pixel push, frontend engineers will add clicks to make it more difficult to drop out of a soporific Zoom call, but few companies are ever willing to rip out their database storage engine. Too much risk, and almost no return.

So it’s exceptional when a new database offering breaks through the barriers and redefines the enterprise.

Neo4j, which offers a graph-centric database and related products, announced today that it raised $325 million at a more than $2 billion valuation in a Series F deal led by Eurazeo, with additional capital from Alphabet’s venture wing GV. Eurazeo managing director Nathalie Kornhoff-Brüls will join the company’s board of directors.

That funding makes Neo4j among the most well-funded database companies in history, with a collective fundraise haul of more than half a billion dollars. For comparison, MongoDB, which trades on Nasdaq, raised $311 million in total (according to Crunchbase) before its IPO. Meanwhile, Cockroach Labs of CockroachDB fame has now raised $355 million in funding, including a $160 million round earlier this year at a similar $2 billion valuation.

The past decade has seen a whole new crop of next-generation database models, from scale-out SQL to document to key-value stores to time series and on and on and on. What makes graph databases like Neo4j unique is their focus on the connections between individual data entities. Graph-based data models have become central to modern machine learning and artificial intelligence applications, and are now widely used by data analysts in applications as diverse as marketing to fraud detection.

CEO and co-founder Emil Eifrem said that Neo4j, which was founded back in 2007, has hit its growth stride in recent years given the rising popularity of graph-based analysis. “We have a deep developer community of hundreds of thousands of developers actively building applications with Neo4j in any given month, but we also have a really deep data science community,” he said.

In the past, most business analysis was built on relational databases. Yet, inter-connected complexity is creeping in everywhere, and that’s where Eifrem believes Neo4j has a durable edge. As an example, “any company that ships stuff is tapping into this global fine-grain mesh spanning continent to continent,” he suggested. “All of a sudden the ship captain in the Suez Canal … falls asleep, and then they block the Suez Canal for a week, and then you’ve got to figure out how will this affect my enterprise, how does that cascade across my entire supply chain.” With a graph model, that analysis is a cinch.

Neo4j says that 800 enterprises are customers and 75% of the Fortune 100 are users of the company’s products.

We last checked in with the company in 2020 when it launched 4.0, which offered unlimited scaling. Today, Neo4j comes in a couple of different flavors. It’s a database that can be either self-hosted or purchased as a cloud service offering which it dubs Aura. That’s for the data storage folks. For the data scientists, the company offers Neo4j Graph Data Science Library, a set of comprehensive tools for analyzing graph data. The company offers free (or “community” tiers), affordable starting tiers and full-scale enterprise pricing options depending on needs.

Development continues on the database. This morning at its developers conference, Neo4j demonstrated what it dubbed its “super-scaling technology” on a 200 billion node graph with more than a trillion relationships between them, showing how its tools could offer “real-time” queries on such a large scale.

Unsurprisingly, Eifrem said that the new venture funding will be used to continue doubling down on “product, product, product” but emphasized a few major strategic initiatives as critical for the company. First, he wants to continue to deepen the company’s partnerships with public cloud providers. It already has a deep relationship with Google Cloud (GV was an investor in this round after all), and hopes to continue building relationships with other providers.

It’s also seeing a major uptick in interest from the APAC region. Eifrem said that the company recently opened up an office in Singapore to accelerate its sales in the broader IT market there.

Overall, “We think that graphs can be a significant part of the modern data landscape. In fact, we believe it can be the biggest part of the modern data landscape. And this round, I think, sends a clear signal [that] we’re going for it,” he said.

Erik Nordlander and Tom Hulme of GV were the leads for that firm. In addition, DTCP and Lightrock newly invested and previous investors One Peak, Creandum and Greenbridge Partners joined the round.

Sherman Ye founded VESoft in 2018 when he saw a growing demand for graph databases in China. Its predecessors, like Neo4j and TigerGraph, had already been growing aggressively in the West for a few years, while China was just getting to know the technology that leverages graph structures to store data sets and depict their relationships, such as those used for social media analysis, e-commerce recommendations and financial risk management.

VESoft is ready for further growth after closing an $8 million funding round led by Redpoint China Ventures, an investment firm launched by Silicon Valley-based Redpoint Ventures in 2005. Existing investor Matrix Partners China also participated in the Series pre-A round. The new capital will allow the startup to develop products and expand to markets in North America, Europe and other parts of Asia.

The 30-people team is comprised of former employees from Alibaba, Facebook, Huawei and IBM. It’s based in Hangzhou, a scenic city known for its rich history and housing Alibaba and its financial affiliate Ant Financial, where Ye previously worked as a senior engineer after his four-year stint with Facebook in California. From 2017 to 2018, the entrepreneur noticed that Ant Financial’s customers were increasingly interested in adopting graph databases as an alternative to relational databases, a model that had been popular since the 80s and normally organizes data into tables.

“While relational databases are capable of achieving many functions carried out by graph databases… they deteriorate in performance as the quantity of data grows,” Ye told TechCrunch during an interview. “We didn’t use to have so much data.”

Information explosion is one reason why Chinese companies are turning to graph databases, which can handle millions of transactions to discover patterns within scattered data. The technology’s rise is also a response to new forms of online businesses that depend more on relationships.

“Take recommendations for example. The old model recommends content based purely on user profiles, but the problem of relying on personal browsing history is it fails to recommend new things. That was fine for a long time as the Chinese [internet] market was big enough to accommodate many players. But as the industry becomes saturated and crowded… companies need to ponder how to retain existing users, lengthen their time spent, and win users from rivals.”

The key lies in serving people content and products they find appealing. Graph databases come in handy, suggested Ye, when services try to predict users’ interest or behavior as the model uncovers what their friends or people within their social circles like. “That’s a lot more effective than feeding them what’s trending.”

Neo4j compares relational and graph databases (Link)

The company has made its software open source, which the founder believed can help cultivate a community of graph database users and educate the market in China. It will also allow VESoft to reach more engineers in the English-speaking world who are well-acquainted with the open-source culture.

“There is no such thing as being ‘international’ or ‘domestic’ for a technology-driven company. There are no boundaries between countries in the open-source world,” reckoned Ye.

When it comes to generating income, the startup plans to launch a paid version for enterprises, which will come with customized plug-ins and host services.

The Nebula Graph, the brand of VESoft’s database product, is now serving 20 enterprise clients from areas across social media, e-commerce and finance, including big names like food delivery giant Meituan, popular social commerce app Xiaohongshu and e-commerce leader JD.com. A number of overseas companies are also trialing Nebula.

The time is ripe for enterprise-facing startups with a technological moat in China as the market for consumers has been divided by incumbents like Tencent and Alibaba. This makes fundraising relatively easy for VESoft. The founder is confident that Chinese companies are rapidly catching up with their Western counterparts in the space, for the gargantuan amount of data and the myriad of ways data is used in the country “will propel the technology forward.”

Neo4j, the premiere graph database development platform, announced the release of version 4.0 today, which features unlimited scaling among other updates.

Graph databases are growing increasingly important as they are used to find connections in data, such as if you bought this, you might like this related item on an e-commerce site; or if you have these friends, you might also know these people on a social site. It’s growing popular in business, and especially among data scientists, who find it useful to find relationships in large collections of data.

Neo4j founder and CEO Emil Eifrem says the company developed the graph database concept, and it has been growing and developing well. “2019 was a really good year for us, generally speaking, but I think more importantly in the graph space. We’ve chosen the category creation and go- to-market strategy when we put the word graph and database together, and we wanted to evangelize that as a concept,” he explained.

As for the new version, Eifrem says it’s a broad new release, but there are a few things he wanted to focus on. For starters is the ability to limitlessly scale. He says this is possible because of new sophisticated horizontal scaling in version 4.0. For previous versions, the company replicated data across the database, a common method for processing data, but it can slow down as the amount of data scales. They wanted to change this in the new version.

“What we’re adding now in 4.0 is partitioning. So this is what’s called ‘sharding’ in the database world. It’s this really ultra powerful feature that allows you to scale both reads and writes and size. Basically, you’re only limited by your budget, how many machines you can add,” he explained.

Another piece in the new release is the addition of role-based access. As graph databases spread from the department or team level across the organization, it becomes increasingly important to restrict certain data to only those who have access based on their role and privileges.

“Today, graph databases in Neo4j are being widely deployed across the enterprise, and now all of a sudden there’s multiple teams across the entire enterprise that wants to access the data. And then you get into security and privacy concerns,” he said. That’s where role based access can protect the data.

The new version has many other features including the ability to run multiple databases on a single Neo4j cluster and support for “Reactive” systems, which gives these kinds of developers “full control over how their applications interact with the database, including robust data pipelines, streaming data, machine learning and more,” according to the company.

Neo4j has been around 2007 and has raised over $160 million, according to Crunchbase.

Neo4j, a popular graph database, is available as an open-source product for anyone to download and use. Its enterprise product aimed at larger organizations is growing fast, but the company recognized there was a big market in between those two extremes, and today it introduced a new managed cloud service called Aura.

They wanted something in the product family for smaller companies, says Emil Eifrem, CEO and co-founder at Neo4j . Aura really gives these smaller players a much more manageable offering with flexible pricing options. “To get started with an enterprise project can run hundreds of thousands of dollars per year. Whereas with Aura, you can get started for about 50 bucks a month, and that means that it opens it up to new segments of the market,” Eifrem told TechCrunch. As he points out, even a startup on a shoestring budget can afford $50 a month.

Aura operates on a flexible pricing model, and offers the kind of value proposition you would expect from a cloud version of the product. The company deals with all of the management, security and updates for you. It will also scale as needed to meet your data requirements as you grow. The idea is to allow developers to concentrate on simply building applications and let Neo4j deal with the database for you.

He says over time, he could see larger businesses, which don’t want to deal with the management side of developing a graph database application, also using the cloud product. “Why would you want to operate your own database? You should probably focus on your core business and building applications to support that core business,” he said. But he recognizes change happens slowly in larger organizations, and not every business will be comfortable with a managed service. That’s why they are offering different options to meet different requirements.

Graph databases allow you to see connections between data. It is the underlying technology, for example, in a social networking app, that lets you see the connection between people you know and people your friends know. It is also the technology on an e-commerce site that can offer recommendations based on what you bought before because people who buy a certain product are more likely to purchase other related products.

Google today announced that it has partnered with a number of top open-source data management and analytics companies to integrate their products into its Google Cloud Platform and offer them as managed services operated by its partners. The partners here are Confluent, DataStax, Elastic, InfluxData, MongoDB, Neo4j and Redis Labs.

The idea here, Google says, is to provide users with a seamless user experience and the ability to easily leverage these open-source technologies in Google’s cloud. But there is a lot more at play here, even though Google never quite says so. That’s because Google’s move here is clearly meant to contrast its approach to open-source ecosystems with Amazon’s. It’s no secret that Amazon’s AWS cloud computing platform has a reputation for taking some of the best open-source projects and then forking those and packaging them up under its own brand, often without giving back to the original project. There are some signs that this is changing, but a number of companies have recently taken action and changed their open-source licenses to explicitly prevent this from happening.

That’s where things get interesting, because those companies include Confluent, Elastic, MongoDB, Neo4j and Redis Labs — and those are all partnering with Google on this new project, though it’s worth noting that InfluxData is not taking this new licensing approach and that while DataStax uses lots of open-source technologies, its focus is very much on its enterprise edition.

“As you are aware, there has been a lot of debate in the industry about the best way of delivering these open-source technologies as services in the cloud,” Manvinder Singh, the head of infrastructure partnerships at Google Cloud, said in a press briefing. “Given Google’s DNA and the belief that we have in the open-source model, which is demonstrated by projects like Kubernetes, TensorFlow, Go and so forth, we believe the right way to solve this it to work closely together with companies that have invested their resources in developing these open-source technologies.”

So while AWS takes these projects and then makes them its own, Google has decided to partner with these companies. While Google and its partners declined to comment on the financial arrangements behind these deals, chances are we’re talking about some degree of profit-sharing here.

“Each of the major cloud players is trying to differentiate what it brings to the table for customers, and while we have a strong partnership with Microsoft and Amazon, it’s nice to see that Google has chosen to deepen its partnership with Atlas instead of launching an imitation service,” Sahir Azam, the senior VP of Cloud Products at MongoDB told me. “MongoDB and GCP have been working closely together for years, dating back to the development of Atlas on GCP in early 2017. Over the past two years running Atlas on GCP, our joint teams have developed a strong working relationship and support model for supporting our customers’ mission critical applications.”

As for the actual functionality, the core principle here is that Google will deeply integrate these services into its Cloud Console; for example, similar to what Microsoft did with Databricks on Azure. These will be managed services and Google Cloud will handle the invoicing and the billings will count toward a user’s Google Cloud spending commitments. Support will also run through Google, so users can use a single service to manage and log tickets across all of these services.

Redis Labs CEO and co-founder Ofer Bengal echoed this. “Through this partnership, Redis Labs and Google Cloud are bringing these innovations to enterprise customers, while giving them the choice of where to run their workloads in the cloud, he said. “Customers now have the flexibility to develop applications with Redis Enterprise using the fully integrated managed services on GCP. This will include the ability to manage Redis Enterprise from the GCP console, provisioning, billing, support, and other deep integrations with GCP.”

Neo4j has helped popularize the graph database. Today it was rewarded with an $80 million Series E to bring their products to a wider market in what could be the company’s last private fundraise.

The round was led by One Peak Partners and Morgan Stanley Expansion Capital with participation from existing investors Creandum, Eight Roads and Greenbridge Partners. Today’s investment exactly doubles their previous amount bringing the total raised to $160 million.

Neo4j founder and CEO Emil Eifrem didn’t want to discuss valuation, calling it essentially a vanity metric. “We’re not sharing that. I never understood that. It’s just weird bragging rights. It makes no sense to customers, and makes no sense to anyone,” he said referring to the valuation.

Graph view of Neo4j funding rounds. Graphic: Neo4j

When you bring a company like Morgan Stanley on as an investor, it could be interpreted as a kind of signal that the company is thinking ahead to going public. While Eifrem wasn’t ready to commit to anything, he suggested that this is very likely the last time he raises funds privately. He says that he doesn’t like to think in terms of how he will exit so much as building a good company and seeing where that takes him. “If your mental framework is around building a great company, you’re going to have all kinds of options along the way. So that’s what I’m completely focused on,” Eifrem explained.

In 2016, when his company got a $36 million Series D investment, Eifrem says that they were working to expand in the enterprise. They have been successful with around 200 enterprise customers to their credit including Walmart, UBS, IBM and NASA. He says their customers include 20 of the top 25 banks and 7 of the top 10 retailers.

This year, the company began expanding into artificial intelligence. It makes sense. Graph databases help companies understand the connections in large datasets and AI usually involves large amounts of data to drive the learning models.

Two common graph database use case examples are the social graph on a social site like Facebook, which lets you see the connections between you and your friends or the purchase graph on an Ecommerce site like Amazon which lets you see if you bought one product, chances are you’ll also be interested in these others (based on your purchase history and what other consumers have done who have bought similar products).

Eifrem wants to use the money to expand the company internationally and provide localized service in terms of language and culture wherever their customers happen to be. As an example, he says today European customers might get an English speaking customer service agent if they called in for help. He wants to provide service and the website in the local language and the money should help accomplish that.

Neo4j was founded in 2007 as an open source project. Companies and individuals can still download the base product for free, but the company has also built a successful and growing commercial business on top of that open source project. With an $80 million runway, the next stop could be Wall Street.

Graph databases have always been useful to help find connections across a vast data set, and it turns out that capability is quite handy in artificial intelligence and machine learning too. Today, Neo4j, the makers of the open source and commercial graph database platform, announced the release of Neo4j 3.5, which has a number of new features aimed specifically at AI and machine learning.

Neo4j founder and CEO Emil Eifrem says he had recognized the connection between AI and machine learning and graph databases for a while, but he says that it has taken some time for the market to catch up to the idea.

“There has been a lot momentum around AI and graphs…Graphs are very fundamental to AI. At the same time we were seeing some early use cases, but not really broad adoption, and that’s what we’re seeing right now,” he explained.

AI graph uses cases. Graphic: Neo4j

To help advance AI uses cases, today’s release includes a new full text search capability, which Eifrem says has been one of the most requested features. This is important because when you are making connections between entities, you have to be able to find all of the examples regardless of how it’s worded — for example, human versus humans versus people.

Part of that was building their own indexing engine to increase indexing speed, which becomes essential with ever more data to process. “Another really important piece of functionality is that we have improved our data ingestion very significantly. We have 5x end-to-end performance improvements when it comes to importing data. And this is really important for connected feature extraction, where obviously, you need a lot of data to be able to train the machine learning,” he said. That also means faster sorting of data too.

Other features in the new release include improvements to the company’s own Cypher database query language and better visualization of the graphs to give more visibility, which is useful for visualizing how machine learning algorithms work, which is known as AI explainability. They also announced support for the Go language and increased security.

Graph databases are growing increasingly important as we look to find connections between data. The most common use case is the knowledge graph, which is what lets us see connections in a huge data sets. Common examples include who we are connected to on a social network like Facebook, or if we bought one item, we might like similar items on an ecommerce site.

Other use cases include connected feature extraction, a common machine learning training techniques that can look at a lot of data and extract the connections, the context and the relationships for a particular piece of data, such as suspects in a criminal case and the people connected to them.

Neo4j has over 300 large enterprise customers including Adobe, Microsoft, Walmart, UBS and NASA. The company launched in 2007 and has raised $80 million. The last round was $36 million in November 2016.

 Neo4j builds tools for creating graph databases, and today at its GraphConnect conference in New York City, it announced a new platform for developers to build graph-based applications using a common set of services.
Emil Eifrem, Neo4j co-founder, says while the concept of graph databases has steadily gained popularity in recent years, the databases need to connect to various enterprise systems. Read More

 Neo Technology, developers of the neo4j graph database have been growing steadily, and investors have noticed, rewarding them with a $20M Series C pay day. Creandum led the round joined by Dawn Capital and current investors Fidelity Growth Partners Europe, Sunstone Capital and Conor Venture Partners. The company’s last funding round was in November, 2012 for $11M, and this latest… Read More