DynoSense Advances Health Scanner Analytics with DataStaxJanuary 22, 2016
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 Robin Chan, Director of Software Engineering at DynoSense.
DataStax: Tell us about DynoSense and your role there?
DynoSense: DynoSense Corp is a medical technology company founded in 2013 providing health relationship management service. Our patented Dyno is world’s first fully integrated multi-function health scanner technology that can capture a broad range of health data. The DynoTM can track more than 33+ health metrics including all fundamental vitals such as: [i] electrocardiogram (ECG), for all heart parameters and heart-related irregularities; [ii] photoplethysmography for extracting blood oxygen (SPO2), or Hemoglobin (SpHb); [iii] pulmonary plethysmography for extracting respiration rate, breathing volume and breathing efficiency; [iv] core temperature, and [v] non-cuff blood pressure, all in less than 60 seconds with a single action from the user.
I am the Director of Software Engineering at DynoSense, responsible for cloud infrastructure and data mining architecture for our sensors technology.
DataStax: Did you use a different technology before Cassandra?
DynoSense: We evaluated both relational databases like Oracle, Mysql, MS SQL and non-relational databases like MongoDB, HBase.
DataStax: Why did you pick Cassandra and DataStax Enterprise? What kind of data is stored there?
DynoSense: We decided to select Cassandra as our Database software as it’s best suited our sensor data storage scenario. We like the better and easier scalability of Cassandra; we can basically create a new node by one command. We like the SQL-like query, CQL which flattened the learning curve. DevCenter and Opscenter tools are powerful database tools for our developers. There is also good documentation, tutorials and support provided by Datastax especially after signing up for the startup program. Last but not least, DSE Analytics provide easy integration with Apache Spark which can save a lot of setup time.
DataStax: You currently use the DataStax Analytics feature, what business use case does it fulfill?
DynoSense: Once the sensor data is passed to our cloud environment, the data is being processed in real time to undergo multiple analytic algorithm such as denoising and chronic disease detection. With DSE Analytics, the jobs are being parallelized so that we can have much better performance.
DataStax: What advice would you give to other startups that are thinking about using Cassandra for the first time in their solutions?
DynoSense: First of all, definitely join the DSE startup program, it provides a lot of resources and support from Datastax and you can try out all the DSE integrated features for free. Also, you can sign up for the tutorials/training and join the Cassandra expo which saved us a lot of time for configuring and deploying DSE.
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