Anjul Bhambhri, Vice President, Big Data and Streams, IBM, discussed the company’s latest release, Big Insights, along with its overall Big Data strategy with theCUBE co-hosts Jeff Kelly and Dave Vellante, live at the Hadoop Summit 2013.

Big Insights. IBM’s flagship offering in the Big Data space, was just announced a few weeks ago and offers “a lot of the capabilities that we have made generally available,”  starts Bhambhri, noting that these were also identified as capabilities needed by the enterprise during the first day of the summit.

“We have brought SQL to Hadoop, we are now providing wire SQL update capabilities.” Another feature is bringing the processing of unstructured data to Hadoop. From a performance standpoint, “enterprise customers will be wanting, as they are using SQL, the same performance as using SQL to structured data,” she explained. “We are doing that by not moving data out of Hadoop on any other database on the side. We are able to run SQL inside Hadoop.”

With the added SQL support and IBM’s other Business Intelligece tools all supporting SQL, Big Insights becomes very important for customers who are augmenting their warehouse to bring different kinds of data into Hadoop. Thus, they can continue to leverage those existing investments.

“Big Data is not something that’s on only a certain platform, there is still a lot of data in the mainframe,” Bhambhri explained. It’s important for customers to bring Hadoop in those enterprises and access data in both Hadoop and big transactional applications in the mainframe.

Big Insights is completely based on Apache Hadoop and “what we are providing on top of that is capabilities like big SQL, security capabilities where you can secure your data, bringing in data encryption or masking, and an integration with Gardius that allows complete audit of who has access to what data, who runs MapReduce jobs, etc.,” Bhambhri explains.

Where’s Hadoop fit into IBM’s Big Data business?

Asked what impact Hadoop had on the overall IBM Big Data business, Bhambhri said Hadoop “is enabling many new warehousing applications which used to be expensive.” Many customers use it to augment their warehouses, as it is a great technology for all warehouse customers to bring in data that does not belong in a relational database, at least initially. For certain types of apps, “the warehouse is probably the highest performing way to analyze that data,” she added.

“Anywhere there’s new tech, there is going to be some disruption, which if fine,” Bhambhri stated . “In the end we want to bring to the customers what’s best for them” and make sure they can leverage the technology that’s best to solve their problems.

“We’ve embraced it,” she commented on Hadoop. “We are getting it ready for the enterprise, by helping them  to continue to leverage the tools and skill sets they have invested in. We are building an ecosystem of capabilities around Apache Hadoop,” so that customers could get the best out of both world and “provide a way to run your workload where it works and suits the best.”

Who should use IBM Big Insights?

Exploring the customers that Big Insights appeals to, Anjul Bhambhri said it was both long time IBM customers who knew the company analyzed market trends from both a technology stand point and that of the new insights they needed so that they cout serve their customers better, and also new customers who “don’t have a lot of legacy that they need to bring to the next decade.”

The latter will find the offering attractive “because [IBM is] embracing the open source and building an ecosystem around it, that they can use to solve their problems,” Bhambhri said. In order for them to become bigger companies “they have to bet on companies that can take them forward,” she added.

Asked to comment on Streams, Bhambhri mentioned implementations where people were leveraging Streams in the Telco segment to analyze call data records, to predict customer churn rates, to get insights and provide promotions at the right time. There are also use cases in the healthcare industry where the same data couldn’t be processed at the right time to predict certain events is now successfully processed, by marketing and ad agencies who need it as “every interaction the customer is having has to be captured, the context has to be captured’ and understood, as well as by security companies.