As this year’s Spark Summit conference continues, open source Spark users beyond the immediately-associated enterprises are getting to share their experiences its, giving insight into creative applications and developing interests for the future.

Jonathan Farland, senior data scientist at DNV GL Energy sat down with co-hosts John Walls and George Gilbert (@ggilbert41) of theCUBE, from the SiliconANGLE Media team, to discuss how his company is making use of Spark, along with the new opportunities it’s raised for them.

Advising and electricity

Introducing himself and his company, Farland described the overview of DNV GL as “a large organization [with] four pillars: maritime, oil & gas, energy and business assurance. We also have business units for software, cybernetics and research, but those are not our major bread and butter. So, within those four pillars,” he continued, “I work in the energy business unit, and energy is obviously a hot topic all over the world right now.”

“Consulting and providing advisory services” is the focus of Farland’s particular division, with attention to atmospheric conditions, human routines and electricity usage just some of the topics calling for their attention. As he noted, these have “so many complex drivers” that just laying out the basics of a single situation can be a time-consuming proposition.

Spark usage

On the topic of Spark and data leveraging, Farland feels there’s still quite a way to go. “I think the ball’s in a lot of people’s courts right now. We have not figured out exactly how to [fully utilize data].” But, he says, the improvements are already becoming evident, as he cited the historical move from monthly to hourly meter readings, and the discussions of multiple readings per minute which are looming on the horizon.

This improvement in energy metering is useful not just for refining data collection, but for tailoring utility services to better average supply to the analyzed demand. “What end-use metering does is it sort of chips away at the question of occupancy, human behavior with inside the house,” Farland said in one example. “So you can tell what the quantity of electricity usage was, but what drove that?”

Spark granularity

“Spark is finally giving us the ability [to make granular readings],” Farland stated, adding that with its usage, “We’ve made leaps and bounds.” That enabling of further potential was explored as he continued: ”We know what we want to ask, more or less, right now. We have data now that should allow us to answer the questions at a much broader level, and what I’m discovering now is that there’s questions we didn’t even know we wanted to ask, because we’re finally able to look at it all.”

Looking at the Spark Summit event, he was very positive about the learning opportunities it offered, saying “I have learned more about how Spark has been completely integrated into every enterprise platform you could think of. Everybody is somehow leveraging the strengths of Spark.”

While Spark is not an across-the-board device, he said, for the workloads it can be applied to, it does its job very well. “I can put Spark on my stack, and whatever analytics platform you have, immediately you see gains.”

Watch the full interview below, and be sure to check out more of SiliconANGLE and theCUBE’s coverage of Spark Summit 2016.

Photo by SiliconANGLE