SP Naidu Enhances the Customer Experience at Digital River with Real-Time Data

A visionary leader with a passion for technology, SP Naidu is Director, Data Technologies Engineering at Digital River, where he was brought in to modernize the company’s infrastructure, Before Digital River, he tackled complex data warehouse development challenges for over a decade as a Technical Architect at Target. SP started his career as a system analyst at Tata Consultancy Services and CGI (formerly IMRGlobal).

Watch this video to learn how SP modernized Digital River’s infrastructure with the help of DataStax Astra DB and what’s next for the company as they venture into machine learning.

Transcript

Being a digital champion means we are able to provide scalable and highly available infrastructure to our engineering and product teams so that they can focus more on their application architecture and product features.

Digital River’s primary goal is to provide services that are required for commerce, such as payments, tax, fraud detection, compliance, and logistics. Real-time data, it impacts the overall customer experience. One big use case is fraud detection. Suppose, when you are placing an order, we are able to protect the fraud and reduce the fraud rates; that will help our customers as well as our bottom line.

I was brought in to modernize the infrastructure. We were on the monolithic infrastructure where scalability was a bigger problem. The challenge, mainly, is working through the data modeling for distributed data and making our application teams adapt to these new technologies. We have been managing open-source Cassandra, which has been great, but the challenges have been keeping the software up to date, keeping the underlying infrastructure up to date on the security patching, and managing that infrastructure.

With the DataStax Astra DB, we don't have to spend all the time upscaling. We provide the rate limits, we provide the traffic patterns. And behind the scenes, DataStax helps us provision that database and making sure we are set for peak volumes. We were able to achieve an overall 60% reduction in the total cost of ownership. We are only paying for what we use. So it's an overall win, win from a cost prediction, manageability, and supportability.

We are entering into the ML models to better subscription rates, better billing options, and center to all of that is real-time data availability.