The main problem with getting Big Data to perform is, perhaps not surprisingly, its size. Massive data sets have to be stored somewhere, and this sometimes entails silos, which can be problematic for analytics. To get a single accurate answer to a Business Intelligence (BI) inquiry, huge quantities of data from across different categories have join in a timely manner. The logistics are daunting. Some believe that mobilizing the analytics to move across data instead of vice versa, is the answer.

Jean-Pierre Dijcks, master product manager at Oracle, spoke about using SQL to form a super-layer with a bird’s-eye view of all available data. “We can see the entire ecosystem in its totality — IoT, Oracle, all of it combined. We optimize the queries, push everything down as far as we can — algorithms to data, not data to algorithms,” he said.

Data gunk filter

Dijcks told host Peter Burris (@plburris) , host of theCUBE, from the SiliconANGLE Media team, during a special On-the-Ground segment at Oracle’s Redwood Shores Headquarters, about Oracle’s efforts to optimize data so that data queried daily is rightfully prioritized over data that might be analyzed annually. “We filter out a lot of the gunk that we don’t need,” he said, explaining how practical this is for real-world businesses.

“You have multiple silos; multiple things are in there — nobody knows really what’s in there. And so here we start to publish this into a common infrastructure layer. We have all the technological metadata, we track the queries — who queries what, who does all these things. So that’s a tremendous help in governance,” Dijcks stated.

“Big Data SQL will be a foundational layer to make a BI inquiry run smoother across more and more and more pillars of data,” he concluded, adding that this will be a key conversation at the upcoming Oracle OpenWorld conference.

Watch the complete video interview with Jean-Pierre Dijcks below:

Photo by SiliconANGLE