qDatum Powers the Future of Big Data Exchange with DataStax EnterpriseJuly 29, 2015
Recently we had the opportunity to speak with Itamar Maltz, CTO at qDatum, from Germany. qDatum is a platform for data providers and consumers to share raw and aggregate data. They supply users with a self-service exchange where they can both distribute and trade their own data a swell as find the data they need.
qDatum is also a member of the Microsoft Ventures Accelerator program in Berlin.
DataStax: Tell us about qDatum and your role there?
Itamar: qDatum is a platform through which different business and academic entities can trade raw data using a simple API. My role is to make that work, technically.
DataStax: What kind of data will people share in the exchange and how do others access it? What sets you apart from other solutions?
Itamar: Though we are focusing mostly on app developers, we are completely agnostic to the type of data. What sets us apart is that we aim to be more of an infrastructure service than a data broker, allowing self service on top of a comprehensive API. With added layers that enable security, privacy, access control and monetary/subscription settings.
DataStax: Did you use a different technology before Cassandra?
Itamar: No, Cassandra was used in our first prototype along with our own storage mechanism.
DataStax: Why did you pick Cassandra? What kind of data is stored there?
Itamar: We’ve chosen Cassandra for various reasons such as scalability, support, stability and maturity. We use Cassandra mostly to store feeds or parts of feeds that require fast querying of a small number of records. We also make use of Cassandra to manage state data and tracking activity.
DataStax: How would you sum up the benefits you’ve realized with DataStax Enterprise (DSE)?
Itamar: It helped us deploy and develop faster as we spent less time tweaking and binding different products together.
DataStax: What caused you to use DSE over open source Cassandra?
Itamar: Packaging and support mostly. It’s like using a distribution over using vanilla linux, A lot of the weaving of the different tools was done for you already, And done by people much more knowledgeable in those tools.
DataStax: What features from the DataStax Enterprise (DSE) stack are you using at the moment? What business use case do they fulfill?
Itamar: We make use of DSE Search capabilities to allow more complicated queries against Cassandra stored feeds. DataStax’s integration of Solr makes that task easy and stable, saving us the headache of implementing such functionality ourselves.
DataStax: How has your experience been with Azure and DSE?
Itamar: There were challenges, But Azure is shaping up to be a very viable competitor to more established cloud providers.
DataStax: Tell us about the future of your project, will you be leveraging other parts of DSE to make it a reality?
Itamar: As we’re aiming to bring in more “brains” into the provisioning and searching of data feeds we might look into the analytics tools provided by DSE. Making more use of the management services is also something we’re looking into.
DataStax: What advice would you give to other startups that are thinking about using Cassandra for the first time in their solutions?
Itamar: Learn the theory and read the documents first. Cassandra is a great tool but requires some learning. It also has a lot of great less known features that might help you solve problems in ways you didn’t think about.
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