Paul Barth, Podium Data - #MITCDOIQ - #theCUBE
Channel: SiliconANGLE theCUBE
Date: 2016-07-13

01. Paul Barth, Podium Data, Visits #theCUBE!. (00:19)
02. Tell Us About The Problem You're Trying To Solve. (00:50)
03. How Is Your Approach To Solving The Complixity Problem Different. (04:02)
04. Help Us Connect What You're Working On To The Mandates Of Chief Data Officer. (05:46)
05. Do You See The Data Lake Becoming The Data Warehouse. (07:010)
06. Do Data Lakes Adapt Well To Stream Processing. (08:18)

Track List created with http://www.vinjavideo.com.
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How one company is making Hadoop more accessible | #MITCDOIQ
by Gabriel Pesek | Jul 14, 2016 

At the MIT CDOIQ Symposium taking place in Cambridge, MA, attendees are sharing fresh takes on how to solve existing problems with each other, and they are also learning about tomorrow’s tech solutions in the process.

Paul Barth, cofounder and CEO of Podium Data, joined Stu Miniman (@stu) and Paul Gillin (@pgillin), cohosts of theCUBE, from the SiliconANGLE Media team, during the Symposium to share what his company is doing to address Hadoop’s barriers to easy usage by enterprises — and what effect that will have on successful businesses.

Making Hadoop the data key

As Barth mentioned, he is a regular at the MIT CDOIQ events, and Podium Data is serving to answer a problem he’d often discussed while attending.

“I’ve been at this conference for many years, since its founding, talking about what it takes to implement governance,” Barth said. 

“And one of the problems that I found consistently is that the complexity of the systems in an enterprise … make it very difficult to implement governance. You may come up with great policies, you may have good processes and roles and stewards, but if you don’t have an asset to govern, you really will have trouble or never be able to catch up and maintain the governance policies you’re trying to put in place.”

With Hadoop’s growing adoption in data management, though, some of the answers to these problems are coming to light. “The reason [Hadoop] is ideally suited [for governing your data] is that the low cost point … removes barriers to entry … to make an efficient solution, [and] you can dump all your data in one place,” Barth said. “You’re still left with these issues around organizing the data and sharing quality, there’s still a lot of data management work to do, but a lot of the barriers to getting started and moving quickly are eliminated.”

Configuration of Hadoop

However, as many have discovered, Hadoop is not exactly an “out-of-the-box” solution to data-management needs. Barth addressed this with his own experiences in trying to apply it and how this led to Podium Data’s formation.

“What I found was that the Hadoop stack alone was really not a turnkey data lake and that many enterprises didn’t have the Hadoop skills or the integration capabilities to pull together all this new technology with new languages and new protocols and new databases,” he said. “So what I decided was that there was room for a product in there.”

As Barth put it, Podium Data is intended “to really allow agile data management and do away with a lot of maxims that we’ve held onto for years,” so that enterprises and their IT workers, as well as the analysts, can do more without as much struggle.

Podium for all users

“The reason that Podium Data is designed the way it is … is that this environment has a lot of data … and you need an enterprise-class platform that manages all of that end-to-end … talks to any Hadoop distribution, and uses the power of Hadoop to give you that cost performance, the flexibility, the responsiveness, but it gives you the management controls that you expect in a robust enterprise platform,” Barth said, outlining the initial draw of Podium Data before going into more specific aspects.

The big draw, however, is the ease of access being made available to parts of the business that have traditionally been shut out of the deep data side by nature of the interfaces and presentation.

“Now what’s happening,” Barth explained, “is that by using metadata in a business-friendly interface, we’re bringing business analysts and data analysts into that process of understanding the raw data, helping with the preparation and validation of it, and then using a lot of automation during our ingestion to bring in things like legacy mainframe data-sources and convert them into Hadoop-ready formats.”



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