Dheeraj Remella is the Chief Product Officer at VoltDB responsible for technical OEM partnerships and enabling customers to take their next step in data driven decision making. Dheeraj has been instrumental in each of our significant customer acquisitions. He brings 22 years of experience in creating Enterprise solutions in a variety of industries. Dheeraj is a strong believer in cross pollination of ideas and innovation between industries and technologies. Dheeraj holds a bachelor’s degree in computer engineering from Madras University.
Q1. What are the main challenges that 5G bring?
5G brings new opportunities to many industries, such as agriculture, manufacturing, and supply chain management, to name a few. These opportunities are going to increase the amount of data being generated by an order of magnitude. The main challenge that this will create is how to not just transport or capture that data but also to utilize it.
All the above-mentioned use cases require more than ultra-reliable low-latency communication (URLLC); they require ultra-reliable low-latency decision intelligence. Machine-type-communications rely on immediate decisions and actions. Data management technologies of yesteryear are not going to be able to meet these challenges. Enterprises need to consider data platforms that bring together the ability to ingest data, store and aggregate the data, and use it to drive cognitive decisions based on real-time events and historical intelligence (machine learning insights) to take appropriate actions.
Q2. How can digital twins bring IT (information technology) and OT (operational technology) together to maximize data value for process automation?
Historically, digital twins were simply a digital representation of a physical asset’s current state. This was useful for analytics and dashboards. But, as Industry 4.0 gets traction, these digital twins now need to participate in the day-to-day business operations. This means bringing the operational data (think network data, quality of service data) and business data together to ensure service level agreements are met and maintained. We call this new generation of digital twins, active digital twins.
This transformation into becoming an active participant means not just collecting the telemetry data from sensors but also invoking actions through actuators to complete the real-time sense control loop. While IT data informs the best action to take, OT data will help perform this action in the best possible way.
Q3. What role does a data platform play in this scenario?
Using data in a timely manner will be key to successfully leveraging 5G in Industry 4.0. First, let’s define what a ‘a timely manner’ means. In the cases of machine-type-communications and machine-to-machine communications, timely means in under 10 milliseconds. Now let’s define what ‘communications’ means. It’s not just sending data packets to each other; it’s more about the sensing devices sending this data while active digital twins make the appropriate decisions and then invoke actions via actuators.
To accomplish this, you need a data platform that can handle everything from ingestion to decision to minimize latency and keep infrastructure needs manageable for environments that do not have the luxury of unlimited hardware. This data platform is what effectively becomes the active digital twin of the physical assets.
Q4. Why is it necessary to think of this as data-driven problem solving than as assembly of technology components from various categories?
Quite often, enterprises resort to the technologies they already know and feel comfortable with to perform specific tasks very well and think just assembling a few of these point solutions together will meet this new compound problem they seek to solve. This is called the Einstellung effect, where a certain way of solving a problem is installed in the minds of leaders and they feel this will continue to work even in the face of changing requirements.
This has to change, and the problem needs to be understood holistically from all angles: latency, performance, scale, resiliency, infrastructure needs, cloud-native deployment, data complexity, and decision complexity. This shift in thinking is what is going to help these organizations and their leaders be able to meet today’s challenges while being prepared to address the challenges of tomorrow with agility.
Q5. For what applications do VoltDB customers use Apache Kafka in conjunction with your in-memory NewSQL data platform?
Apache Kafka has become a de-facto standard for moving data from the data source to an application destination. VoltDB is used in conjunction with Kafka, not just as in-memory storage but as a combination of in-memory storage, stream processing, and embedded business logic. This allows a solution builder to use VoltDB instead of multiple technologies, which in turn translates to a faster ROI, better resiliency, and better business continuity. Telecom vendors use this combination for applications such as convergent charging, policy control, real-time mediation, customer management, and revenue assurance (i.e. fraud prevention).
Enterprises use this combination of VoltDB and Kafka for incorporating decision intelligence into their streaming data for applications that benefit from event-driven cognitive decision making. These applications have been mostly for campaign/offer management, network security, bot detection, DDoS prevention, compliance management, IIoT security, mobile finance etc.
Q6. Is VoltDB and Kafka a possible solution for the challenges posed by 5G, IoT, and machine learning at scale?
The combination of VoltDB and Kafka is very effective for addressing the challenges of using fast streaming data for driving intelligent decisions and actions at scale. While machine learning falls outside the purview of what you can achieve with this combination, VoltDB can bring in machine learning insights to make ever richer decisions.
Now, I would be naive to say that this combination of VoltDB and Kafka can address all challenges posed by 5G and IoT, but it is definitely the best combination to address applications that require low-latency complex decisions on multiple streams of data.
Q7. In your opinion, what are VoltDB’s three most successful customer use cases?
Handsdown, the top three applications for VoltDB are business support systems (BSS), customer management, and revenue assurance (fraud prevention).
All of these use cases require applications that have:
- Scale, with the ability to handle billions of events per day.
- High performance, with the ability to process hundreds of thousands of events per second.
- Low latency, where the moment of engagement is in single-digit milliseconds.
- The ability to handle complex data.
- The ability to make complex decisions on streaming data.
- Immediate consistency and accuracy.
- No data loss.
- Geographic distribution.
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