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July 31, 2019

Funders Target Flexible Graph Databases

George Leopold

The graph database ecosystem continues to expand with new venture funding for Dgraph Labs Inc., a transactional and distributed graph database startup.

The San Francisco-based startup announced an $11.5 million Series A funding round on Wednesday (July 31) led by Redpoint Ventures. Previous funders Airtree Ventures, Bain Capital Ventures, Blackbird Ventures and Grok Ventures also participated.

Founded in 2016, Dgraph said it would use the funding to support emerging GraphQL applications based on its as-a-service platform. That service is billed as a scalable general-purpose graph database targeting growing enterprise demand for the technology as database queries become increasingly complex.

Dgraph’s service seeks to address what it says is the shortfall in current graph platforms that are “either native and don’t scale or are layered on top of other databases and lack performance.”

Manish Jain, Dgraph’s founder and CEO, formerly served as a senior software engineer at Google (NASDAQ: GOOGL) where he led the search giant’s knowledge graph serving system. “Most companies do not have the capability or desire to spend millions of dollars building a custom graph database,” Jain said in announcing the funding round.

“Yet current ‘off-the-shelf’ solutions don’t provide the benefits they require, such as horizontal scalability, performance, multiple data models which need to be served and flexible schema,” Jain added.

The startup notes that large online retailers like Airbnb, Dropbox, Twitter (NYSE: TWTR) are developing custom graph systems to boost developer productivity and improve customer service. The key challenge is developing a scalable graph database, Dgraph argues.

The graph approach inspired by the Google effort provides native support for GraphQL and JSON and is tailored to application development. As the relationships within data are accounted for when executing queries, the startup touts its approach as simplifying those data models while boosting query performance.

Market tracker Gartner forecasts surging growth for graph processing and graph databases over the next three years. Enterprise applications will focus on accelerating “data preparation and enable more complex and adaptive data science,” Gartner noted in a February report on data and analytics trends for 2019.

“Data sets are getting more complex while databases serving them have stayed largely the same for the last 20 years, causing companies to stick graph solutions on top of relational databases with spit and tape,” Jain noted in a blog post.

Custom platforms attempt to run a graph layer on top of SQL, an approach that “comes with a lot of downsides,” he added. Moreover, those approaches can’t be adopted by other companies or, worse, other use cases within the same company, the startup argues.

Among Dgraph’s value propositions is a flexible schema that can scale while accelerating complex queries.

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