UPDATED 09:00 EDT / MARCH 06 2015

The future of mainframes is Hadoop | #BigDataSV

Tendu Yogurtcu, SyncsortMainframes mainly come up in the context of migrating the information inside to newer systems nowadays, but the situation is much more nuanced than that. Over half a century after the first computers entered the enterprise, big iron is still the best option for processing many types of transactions.

It’s the less time-critical workloads, such as analytics and document management systems, that are moving off mainframes, which is where Syncsort Inc. sees its chance to make a difference. Tendu Yogurtcu, the head of the data operations for the ETL stalwart, appeared on theCUBE recently to share the insider’s perspective on the opportunity at hand.

The premium capabilities of big iron are so indispensable to transactional workloads that it’s a waste to use them for non-essential tasks. According to Yogurtcu, reducing those costs is the primary motivation of customers turning to Syncsort for help with migrating business processes to platforms such as Hadoop.

Cutting operational expenses is low-hanging fruit, she explained, but that is not to say it’s an easy picking. Far from it. The majority of practitioners in the enterprise today “don’t know what the existing workloads are exactly because the person who wrote the code left a while ago, so there is no one to understand the 7,000 lines of [legacy] script and recreate it in Hadoop,” Yogurtcu detailed.

The other key challenge in moving data off mainframes lies not with big iron but rather with Hadoop itself. The upstream ecosystem is evolving at a breakneck speed that she said sees a new technology added every two to three months, which is makes it difficult for organizations to keep up.

That uncertainly over the direction of the platform has made decision-makers hesitant to commit to a specific component for fear that it may become obsolete before they can realize the intended return. Syncsort is trying to address that concern by abstracting the data topology from the underlying building blocks with its software, an approach that Yogurtcu said removes the need for organizations to stay on top of the changes in the  ecosystem.

“The execution layer decides what should be created as a map job, what should be created as a reduce job and where to run in the cluster,” she elaborated. “Whether you’re running on a laptop or in Hadoop today and Spark tomorrow is completely irrelevant to the user.”

The key to that functionality is the company’s partnerships with the major Hadoop vendors, which extend far beyond the obvious aspect of accommodating different types of implementations. The distributions act to stabilize Hadoop,  Yogurctu explained, a beneit that is vital to helping organizations ensure the longevity of their analytic investments.

Yet as important standardization is for enterprise adopters, she’s still on the fence about the controversial effort to create a common reference version of Hadoop. It’s too early to make predictions about the initiative, according to Yogurtcu, but it’s clear that the initiative must avoid polarizing the ecosystem in order to succeed.

Watch the full interview (17:53)


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