DataStax put spotlight on Axelerate through a interview with our CTO, Patrik Axelsson to highlight the usage of DataStax Enterprise in the Axelerate IoT Platform.
DataStax: Could you please tell us a bit about Axelerate, what exactly you offer and your role there?
Patrik: Axelerate provides a hyper-scaled Internet of Things (IoT) platform in cloud. We call this the IoT Business Platform, because that’s what IoT is really about, business. This platform helps you dramatically reduce time-to-market for your IoT solutions by providing the most essential components including Device Management, Analytics, Presentation, Automation and Billing. I’m currently operating as the CTO at Axelerate and making sure that the platform always uses the perfect matching technology for our business needs.
DataStax: What makes your IoT solution successful, what differentiates you to similar applications?
Patrik: We’ve built this platform from customer demands only. This is not a platform to play around with Arduinos or Raspberry Pis, even though that’s possible as well. This is a platform to provide enterprise-level delivery of IoT solutions. I think a lot of companies focus too much on communication in the world of IoT. What we’ve found is that customers find features like Organizational Management, Subscription Management and other administrative tools crucial for their choice of IoT provider.
DataStax: Did you use a different technology before you started using Apache Cassandra™?
Patrik: We store a lot of data. The majority of the data is collected from sensors. A typical data point is a GPS position received from a car or a truck. We receive a lot of these data points, and before they are transformed into meaningful business value we need to store them somewhere. Initially we used traditional relational databases but ended up switching to NoSQL technology to handle all the throughput. In essence, the primary reason we swapped was because of the trouble we had with scaling. We did some research around the trending NoSQL databases and found that Apache Cassandra™ covered our needs, with a lot of bonus benefits.
DataStax: Why did you decide to use Apache Cassandra™? What kind of data is stored there?
Patrik: We compared the different NoSQL technologies and found that Apache Cassandra™ was the best match for our case. We primarily use Apache Cassandra™ to store sensor data but we’ve also built some system functionality around Apache Cassandra™ to achieve high availability on these system components.
DataStax: How would you sum up the benefits you’ve achieved with DataStax Enterprise (DSE)?
Patrik: A powerful backpack of tools that helps us build a system that scales infinitely and never goes down.
DataStax: What caused you to use DSE over open source Apache Cassandra™?
Patrik: Simplicity. There’s a lot of management and operations related to spinning up and operating these kind of systems. DSE provides a lot of pre-built configurations, tools, integrations and benefits that helped us get started in no time. The enterprise level support that DataStax provides is also one of the many reasons we picked DSE over open source Apache Cassandra™.
DataStax: What features from the DataStax Enterprise (DSE) stack are you using at the moment? What business use case do they fulfill?
Patrik: We use Apache Cassandra™ to store data points received from all our connected devices and assets. Because of the great integration with Apache Hadoop™ and Apache Spark™ in DSE we can easily transform unstructured data into meaningful business value. We use the power of Apache Solr™ to lookup specific occurrences and events in this data. We also use OpsCenter.
DataStax: Tell us about the future of your project. Do you intend to leverage other parts of DSE to make it a reality?
Patrik: We WILL be integrating and building functionality around the Graph technology. Graphing your data gives you a relational insight that is extremely powerful (and useful).
DataStax: What advice would you give to other companies that are thinking about using Apache Cassandra™ for the first time in their solutions?
Patrik: Build PoCs and test clusters to get started. There’s a lot to learn but once you get your hands ready, you’ll end up with a killer Big Data system.