Despite the rapid growth that big data has seen in the last few years, important tools like graph databases that can make the process of extracting information from a large set of data efficient and easy are still out of bounds for most small companies and startups. However, an Australia-based startup DGraph Labs is aiming to change that by developing the first open source, native and distributed graph database, which it believes can dramatically improve the performance, scalability, and efficiency of cloud and big data applications.
On May 17, the graph database company founded by ex-Google employee Manish Jain announced that it has raised $1.1 million in a seed funding round that was led by Bain Capital Ventures and Blackbird Ventures. Other investors that participated in the round included Mike Cannon-Brookes, co-founder of enterprise software company Atlassian , and former Google employee, Mark Cummins. According to the company, it will use the proceeds from this funding to recruit new engineers and build core technologies.
In a statement released by the company, DGraph Labs’ founder and CEO, Mr. Jain said, “Graph data structures store objects and the relationships between them. In these data structures, the relationship is as important as the object. Graph databases are, therefore, designed to store the relationships as first class citizens.”
“Accessing those connections is an efficient, constant-time operation that allows you to traverse millions of objects quickly. Many companies including Google, Facebook, Twitter, eBay, LinkedIn and Dropbox use graph databases to power their smart search engines and newsfeeds.”
Continuing The Work
Mr. Jain came to Australia after being forced out of U.S. due to Visa problems, despite having spent several years working in the country. The idea to start DGraph Labs came to Mr. Jain from his experience at working at Google, where he was a part of the Web Search and Knowledge Graph Infrastructure group for over six years.
“This is a strong team continuing their work on a problem they’ve had considerable success with inside Google. It’s great to have the opportunity to back them to bring their expertise to the world,” Blackbird Ventures co-founder, Rick Baker, said.
The use of graph databases has increased phenomenally in the last few years. Apart from being used by social networks for their newsfeed and e-commerce companies for user recommendations, graph databases are also being used for medical and DNA research, behavior analysis, fraud detection, machine learning, artificial intelligence and various other things.
Though graph databases can be used to store graph data sets in relational databases, it becomes difficult to query graph data in relational databases because it requires a large number of table joins to identify the relationships. According to DGraph Labs, the reason graph databases are not being used widely is because till now they have been either non-distributed or non-native.
“In 2016, it’s hard to believe that there are still no commercial graph databases that are truly distributed,” Salil Deshpande, Managing Director at Bain Capital Ventures, said.
He also explained the reason behind that, saying, “Graph databases that exist today are not truly distributed: they run fine on one node but rely on a variety of architectural hacks to run on multiple nodes, and are thus not scalable. Whereas the ones that are distributed are not really graph databases: they are simply overlays of graph functionality on top of non-graph databases, which results in poor query performance especially when joins are involved, and query performance being correlated with the size and nature of the result set rather than the complexity of the query.”