Organize Your Next Event with Who’s Up and DataStax EnterpriseApril 3, 2017
This post is one in a series of quick-hit interviews with companies using DataStax Enterprise (DSE) for key parts of their business. In this interview, we talked with Helgi Hermannsson, Founder of Who’s Up.
DataStax: Hello Helgi, thanks a lot for your time today. Could you tell us a bit about Who’s Up? What exactly do you offer and what is your role there?
Who’s Up: I am the founder of Who’s Up and we created an app that will make it easier to organize and collect payments for events with friends and colleagues.
The basic concept is that you get shown a list of ideas on the discover page of our app, you buzz the ones you like on your network, your interest is then seen by those users when they browse the list (there are no push notifications for just showing interest) and once there are enough people for an event the person who first buzzed is asked to organize the event. If they decline, the first person to accept will be the organizer. Once all the details are set up, people are asked if they want to confirm their presence, and also if they are happy with the final price, which the app then tracks. The app will send reminders and users can do their payments to the organizer through the app – but there’s also an option to pay for the event in cash.
DataStax: What differentiates Who’s Up from similar products?
Who’s Up: We are radically different from most event platforms out there right now. These mostly focus on finding suppliers and getting users to view those supplier listings to do events with strangers or maybe a couple close friends. We hope to build and strengthen users’ network, which we believe will make people healthier and happier.
Modern life is busy! As a consequence, most people don’t always know if they will have time for different events, which puts them off arranging and organizing events they most likely won’t be able to attend. Subsequently, their network shrinks and they lose visibility of what is going on… even people with a relatively healthy network will be challenged by the organizational effort of certain events – and this is what we hope to change, we want to make it easy and give people visibility of what they can do – similar to facebook newsfeed but for actually taking part, rather than just watching.
We are also the only app I am aware of that really tackles all the challenges of organizing an event start to finish – from helping to spark the event to reminding people where to go, to chasing any agreed cost split between users.
DataStax: Did you use a different technology before you started using DataStax Enterprise (DSE)?
Who’s Up: I started with SQL Server which is my bread and butter. But it is not free and I wanted something that would allow this to be a hobby while also learning more about big data. I also knew I needed to be able to easily integrate machine learning into the solution.
DataStax: Why did you decide to use DataStax Enterprise? What kind of data is stored in DataStax Enterprise?
Who’s Up: I have used R, Matlab, and some C# stuff before but found Scala and Apache Spark™ to be far more useful – really only a 100k records and you can get stuck with those, whereas with Scala you can do really cool stuff and I knew the heart of the app would be machine learning which must integrate flawlessly with everything else – when I started DSE was not just the best option but basically the only one! I was also able to do a lot more with SOLR – provided as DSE Search – than I first expected thanks to the random sampling feature. If you are a Data Scientist you will know being able to do random stuff is a basic building block for many cool things!
DataStax: How would you sum up the benefits you’ve achieved with DataStax Enterprise?
Who’s Up: I think many people shy away from using new tech like Apache Cassandra™ because they think it will take all their time learning how to use it. I think that is true for many NoSQL technologies and yes, there was a learning curve in our case, but I honestly believe it saved me time in the long run while also being a great underpinning for the future.
Cassandra forces you to keep your design clean – no giant procedures, no foreign keys. You are forced to put all the application logic in the APP and API layers where it belongs and so it is easier/cheaper to scale and maintain in the long term, a big part of that is that the tools for testing, refactoring, versioning are better for API/App Code than for SQL/Database Code.
The benefits of using DSE for us was having the capability to build an app that otherwise wouldn’t be possible with a small team, cost saving (free to start with the Startup Program!), and time-saving thanks to the integration between Cassandra, Spark & SOLR.
DataStax: What caused you to use DataStax Enterprise over open source Apache Cassandra™?
Who’s Up: The integration between Solr, Spark, and Cassandra works out of the box. This also makes updating easier in the future with newer Cassandra versions.
DataStax: Tell us about the future of your project, do you intend to leverage other parts of DSE to make it a reality?
Who’s Up: I will leverage Spark more. The quality of the machine learning algorithms and examples is always growing. As we building a user base this will become more relevant to ensure we provide the right idea and bring together the right group of people.
DataStax: What advice would you give to other startups that are thinking about using DataStax Enterprise for the first time in their solutions?
Who’s Up: I believe that DSE makes a very strong case for companies that need Search and Analytics for machine learning. If you don’t use either of those then you will want and need to use Cassandra once you start to struggle on single machine proving you have fairly simple data model… quite a common issue if you used MySQL as a datastore. Cassandra is not a drop-in substitute for SQL Server, or Postgres unless your strengths are on the API development side rather than with database programming which is more common and why many startups opt for doing a lot of development in the database.
You could also mix in Cassandra for sensor data or such to take the load off the relational database where you have built application logic.
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