NoSQL databases are schemaless, so data modeling isn’t necessary – at least in theory. In practice, Hackolade saw a niche in the market to provide a tool to prepare data for storage in MongoDB and other NoSQL databases.
In this week’s five-minute interview (conducted at GraphConnect 2018 in NYC), we discuss how demand from large customers inspired Hackolade to adapt their product for Neo4j and Hackolade’s view of the future of graph databases.
How did you decide to get into data modeling for NoSQL?
Pascal Desmarets: At Hackolade, we saw a niche in the market to do data modeling schema design for NoSQL databases.
Data modeling is a field that’s decades old for traditional databases, but no one was doing it for NoSQL databases.
Because NoSQL databases are labeled schemaless, people think that there’s not much work to be done when preparing for storing data in NoSQL databases. However, it turns out that it can get really tricky, and our tool helps with the design of these databases.
We originally developed the tool for MongoDB and document databases, and our Fortune 500 customers who use our product also use Neo4j, and they asked us to adapt our tool for Neo4j so they could have just one tool to do all their data modeling for their various different NoSQL databases.
Neo4j sales engineers and staff of Neo4j have been incredibly supportive of our product. That bodes very well for the future.
What made you choose Neo4j?
Desmarets: It’s mostly because Neo4j is the leader in its field of graph databases. And obviously the adoption is great. Through our customers, we’ve been pushed by customers to adapt our tools for Neo4j.
It’s been really interesting to discover the world of graph databases, which we didn’t really know before, and it’s been a fantastic experience.
What do you think is in store for the future of graphs?
Desmarets: With graph databases and data analytics, I think that some really difficult engineering problems have been ameliorated by having an interface to see the connections in our data.
When clients or business-facing folks ask for things that violate a relational database model, you now have a tool to show them what those relationships look like, and that wasn’t really available before.
That interface between the business-facing side and the engineering side is a lot easier, the friction is a little bit less, and there’s not as much work that has to be undone to explore data.
What is your favorite aspect of working with Neo4j?
Desmarets: In working with Neo4j we found a really helpful community that cares about the product that they’re working on. That’s been really great to see. Sometimes open source projects are really promising but are wrapped up in one or two people. The organization as a whole has been really easy to work with and extremely helpful and interested in the problems that we’re working on.
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