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Robin Schumacher, SVP and Chief Product Officer

DataStax, Graph, and the Move to a Multi-Model Database Platform

By Robin Schumacher, SVP and Chief Product OfficerFebruary 3, 2015

We’re thrilled today to announce our acquisition of Aurelius, the company behind the distributed graph database Titan. We’re excited to have the very talented people at Aurelius who are passionate about graph now working with our DataStax team who are equally passionate about Cassandra.

This event telegraphs an important move in our product strategy here at DataStax, which is our intention to add multi-model capabilities into Cassandra and DataStax Enterprise (DSE). As with everything we do, this direction is solely driven by our customers who are building today’s most innovative Web and mobile applications.

Why Multi-Model?

It’s not uncommon to see NoSQL databases characterized by their underlying data model (e.g. wide-row/column store, document, graph, etc.). However, the reality is that since NoSQL has gone mainstream, our customers are building modern systems where the underlying applications require more than one NoSQL data model format.

Because of this need, architects oftentimes have to shard an application and use different NoSQL providers to meet the multi-model requirements of the underlying system. This increases complexity, cost, the IT staff’s learning curve and slows the application’s time to market. That’s something we aim to remedy.

In the same way we’ve solved the mixed workload problem with DataStax Enterprise that allows you to run transactional, analytic, and enterprise search workloads in one database (thus eliminating the need to shard an application along those traditional lines), we now want to provide the ability for you to have support for multiple data models in the same database platform. Doing so removes the need for multiple NoSQL databases, and supplies you with simplicity, reduced costs and one software vendor with which to work.

Why Graph?

One of the most enjoyable parts of my job is to talk to our customers about their inventive new applications and hear about their current and future needs. About 2.5 years ago, no one talked to me about graph database support.

Fast forward to today and, my, how things have changed!

Since that time, the request for graph database support in DSE has been the single biggest customer ask, outpacing every other product/feature request. The customer demand for graph in DSE perfectly mirrors the dramatic increase in popularity of graph databases seen on (notice that #2 in popularity is the wide column store model, which is Cassandra’s category).

graph popularity db-engines.png

Why the huge spike in graph interest?

It’s really not hard to figure out. A very large part of today’s Web and mobile world is comprised of systems of engagement and systems of inquiry that deal with highly connected data.

Take your pick – fraud detection, social communication, contacts management, recommendation and personalization, financial analysis, buyer behavior analysis – all these and more must manage a seemingly infinite series of connections between data.

Just as modern businesses have found the 40-year old relational database model inadequate to handle the data distribution and performance needs of today’s Web and mobile applications, they’ve watched the RDBMS fail to deliver the speed, uptime and agility needed to service these highly connected data applications.

Enter the graph database: an engine that can model these types of engagement and inquiry systems in a way where connecting data is easy and where performance doesn’t suffer from the antiquated join methodology that slows down an RDBMS.

Now, add to that engine the power and benefits businesses get from Cassandra and DSE. You have continuous uptime, horizontal scalability and linear performance capabilities that handle any sized workload. In addition, you’re supplied with easy data distribution across multiple data centers and cloud availability zones, seamless integration with powerful analytics and search functionality, and operational simplicity coupled with a great TCO.

That, in short describes our vision for DataStax Enterprise Graph.

What’s Next?

Today, as many of you know, Titan works very well with Cassandra. But that said, there’s still a good deal of additional innovation and extra scale that our now combined Aurelius and DataStax engineering teams want to build into the platform so that it becomes the best solution for applications needing to manage highly connected data.

We’ll definitely keep you updated on our progress with DataStax Enterprise Graph, but you should also be on the lookout for more announcements coming from us as we add other multi-model capabilities into the DSE platform.

We’re certain that you will like what you see!



  1. Can we expect DSE to be backend-agnostic same as Titan, or will it be hard-wired to DataStax Cassandra?

    Will the Aurelius team continue to participate in the TitanDB open source project, or is that prohibited by their affiliation with DataStax?

    Who is the “goto” for questions such as:

  2. says:

    Very good article. I’m experiencing many of these issues as well..


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