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Diego Ferreira

The Next Level of Always-On Social Network with Vero Labs Built on DataStax Enterprise

By Diego FerreiraOctober 20, 2015

This post is one in a series of quick-hit interviews with companies using Apache Cassandra™ and/or DataStax Enterprise (DSE) for key parts of their business.  For this interview, we talked with TJ Marbois, CTO at Vero Labs.

DataStax: Hi TJ, many thanks for the opportunity of this interview. Could you please tell us a bit about yourself, Vero and what you offer?

TJ: I am TJ Marbois – CTO at Vero Labs. Our product, Vero, is a new mobile based social network that we have tried to build from the ground up by rethinking what it means to be social. We feel that the current state of social apps being driven by invasive data gathering and advertising based models is contrary to the idea of truly being social. So we’ve started with a core that places privacy and control over your data first.

DataStax: What makes your project successful, what differentiates you from other similar behavioral email applications?

TJ: We think our conceptual model and choice of flexible fast technology is what will help us succeed.  We’ve built a very nimble architecture around a core set of principles that we hope – promotes a more honest experience in social applications.

DataStax: How’s your experience with Cassandra so far?

TJ: Cassandra has been a great solution for us. As we have learned more about Cassandra and used it more, the reason of its gaining popularity became clear to us – it is a solid data store that can quickly adapt to our needs.  Since we are continually building out new ideas and trying different things it became clear that having a solid data layer that supported rapid development but gave us solid stability was needed…. Cassandra fits this bill perfectly.

DataStax: Why did you decide to use Cassandra? What kind of data is stored there?

TJ: Cassandra has the scalability and flexibility we were looking for. We keep all our application data there.  Social posts and complex structured user data.  We even use it for our chat engine.

DataStax: How would you sum up the benefits you’ve achieved with DataStax Enterprise (DSE)?

TJ: Solid working engine. We use DSE to store all these data and it is fast to access and flexible to query. The structural support for data centers distributed around the world helps our application to be highly available to customers and ready to scale by simply adding more nodes. The database platform is already redundant and smart – we feel safe on Cassandra that it will do what we need when we need it.

DataStax: What caused you to use DSE over open source Cassandra?

TJ: We saw the additional features as a plus for managing and moving faster.  The solid support from DataStax has been awesome, allowing us to speed forward.

DataStax: What features from the DataStax Enterprise (DSE) stack are you using at the moment? What business use case do they fulfill?

TJ: We use the automated repair service and monitoring in DataStax OpsCenter. We also use the built-in Spark for account jobs and DSE Search (Solr integration) for searching users and searching items in persons collections (items saved). We are definitely interested in what is coming up next with DataStax Enterprise!

DataStax: Tell us about the future of your project, do you intend to leverage other parts of DSE to make it a reality?

TJ: We are just ramping DC to DC cluster scheme – and I’m impressed with DSE ability to handle multi-data center installations and replications.  We think this is going to be a key safety and locale based routing feature for us.

DataStax: What advice would you give to other startups that are thinking about using Cassandra for the first time in their solutions?

TJ: Get into the Startup Program! It’s awesome! The DataStax team has been the greatest support – they listen to us and respond instantly to our needs. I think DataStax smartly understands that fostering startups is a great way to build future businesses and a great ecosystem of developers.



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