As the cloud matures, there are many organizations working to make it a more secure and well-governed technology. An active player in these efforts is Hortonworks Inc., known primarily for its development and support of the open-source data management platform, Apache Hadoop.

“That’s what we’re focused on, having a common security and governance model, no matter where data lives,” said Arun Murthy (pictured), founder and vice president of engineering at Hortonworks Inc.

Murthy recently joined Dave Vellante (@dvellante) and George Gilbert (@ggilbert41), co-hosts of theCUBE, SiliconANGLE Media’s mobile live streaming studio, during the Spark Summit East 2017 conference held in Boston, MA. (*Disclosure below.) Murthy talked about the implications of where client data resides, as well as his thoughts on Spark.

Does it matter where data lives?

Application data, usually generated from a mobile device, is not coming on-premises. It’s generally more cost-effective to collect it in a local cloud. Then, as that data will need to match with transactional or customer data and run analytics, a common way to secure and govern the data is needed, according to Murthy.

Applications (especially for the Internet of Things) need data that is born in the cloud and that will stay in the cloud, but they also need transactional data that will stay on-premises, Murthy explained. Therefore data has to span physical locations, and move back and forth between those locations.

“Increasingly, it’s about people deploying stuff in production, running workloads both on-premises and in the cloud,” said Murthy. The cloud is becoming more and more “real” for mainstream enterprises.

Spark enabling more AI

While Hadoop has become a popular platform for data management, there’s now a perception that Apache Spark can replace Hadoop. However, Spark can be a compute engine to complement or replace just some of Hadoop’s own engines, a major perk being its ability to enable machine learning for speedier, more automated data processing. Murthy believes that every product will eventually contain some artificial intelligence, machine learning or deep learning capabilities. In order to enable these features, a predictive model needs to be built in order to score what is happening in the real world against that model. Spark assists with that, Murthy concluded.

Watch the complete video interview below, and be sure to check out more of SiliconANGLE and theCUBE’s coverage of the Spark Summit East 2017 Boston. (*Disclosure: TheCUBE is a media partner at the conference. Neither Databricks nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo by SiliconANGLE