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Startup Dgraph Labs growing graph database technology

Dgraph Labs looks expand its graph database platform, which offers the promise of reducing isolated data sources and speeding up queries.

Dgraph Labs Inc. is set to grow its graph database technology with the help of a cash infusion of venture financing.

The company was founded in 2015 as an effort to advance the state of graph database technology. Dgraph Labs' founder and CEO Manish Jain previously worked at Google, where he led a team that was building out graph database systems. Jain decided there was a need for a high-performance graph database technology that could address different enterprise use cases.

Dgraph said July 31 it had completed an $11.5 million Series A funding round.

The Dgraph technology is used by a number of different organizations and projects. Among them is Intuit, which uses Dgraph as the back-end graph database for its' open source project K-Atlas.

"We were looking for a graph database with high performance in querying large-scale data sets, fully distributed, highly available and as cloud-native as possible," said Dawei Ding, engineering manager at Intuit.

Ding added that Dgraph's graph database technology stood out from both architectural design as well as performance benchmarking perspective. Moreover, he noted that being fully open sourced made Dgraph an even more attractive solution for Intuit's open source software project.

The graph database landscape

Multiple technologies are available in the graph database landscape, including from Neo4j, Amazon Neptune and DataStax Enterprise Graph, among others. In Jain's view, many graph database technologies are actually graph layers, rather than full graph databases.

"By graph layer, what I mean is that they don't control storage; they just overlay a graph layer on top of some other database," Jain said.

So, for example, he said a common database used by graph layer-type technologies is Apache Cassandra or, in Amazon's case, Amazon Aurora.

Screenshot of graph database from Dgraph Labs showing information about all movies directed by Steven Spielberg
Graph database of all the movies directed by Steven Spielberg, their country of filming, genres, actors in those movies and the characters played by those actors.

"The problem with that approach is that to do the graph traversal or to do a graph join, you need to bring the data to the layer before you can interact with it and do interesting things on it," Jain commented. "So, there's multiple back and forth steps and, therefore, the performance likely will decrease."

In contrast, the founding principle behind Dgraph was that graph database technology could be developed that can scale horizontally while also upping performance, because the database controls how data is stored on disk.

Open source and the enterprise

Dgraph is an open source project and hit its 1.0 milestone in December 2017. The project has garnered more than 10,000 stars on GitHub, which Jain points to as a measure of the undertaking's popularity.

Going a step further is the company's Dgraph Enterprise platform, which provides more capabilities that an organization might need for support, access control and management. Jain said Dgraph Labs is using the open core model, in which the open source application is free to use, but then if an organization wants certain features, it must pay for it.

Jain stressed that the core open source project is functional on its own -- so much so that an organization could choose to run a 20 node Dgraph cluster with replication and consistency for free.

Why graph databases matter

We were looking for a graph database with high performance in querying large-scale data sets, fully distributed, highly available and as cloud-native as possible.
Dawei DingEngineering manager, Intuit

A problem with typical relational databases is that with every data model comes a new table, or new schema, and, over time, that can become a scaling challenge, Jain said. He added that in the relational database approach, large data sets tend to become siloed over time as well. With a graph database, it is possible to unify disparate data sources.

As an example of how a graph database technology approach can help to eliminate isolated data sources, Jain said one of Dgraph's largest enterprise customers took 60 different vertical data silos stored in traditional database and put all of it into a Dgraph database.

"Now, they're able to run queries across all of these data sets, to be able to power not only their apps, but also power real-time analytics," Jain said.

What's next for Dgraph Labs

With the new funding, Jain said the is to open offices for the company as well as expand the graph database technology.

One key of future expansion is building a Dgraph cloud managed service. Another that will be worked on is third-party integration, with different technologies such as Apache Spark for data analysis.

"Now that we have a bunch of big companies using Dgraph, they need some additional features, like, for example, encryption, and so we are putting a good chunk of our time into building out capabilities," he said.

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Would you consider using a graph database to replace an existing relational database?
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Yes absolutely. I see Graph DBs as a natural evolution to handle the increasingly connected & complex datasets.
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