Byron Igoe: Right, I mean, I may be somewhat bias given the kind of Bi technology that my company provides, but when I think about data mashups and the users being able to manipulate and combine not only the enterprise data but the external sources, the fact that it’s all still metadata, and you see the provenance, really trace it back to where that it is coming from, that you can really take that information for what it’s worth, you know, you can see that oh, okay they are just mashing up some of my sales reports from last quarter with a Twitter feed, so I will take that with a grain of salt.
Jim Ericson: Taking it with a grain of salt, but also may be you make, may be you roll that out for people to be able to look at it generally more broadly.
Eric Kavanagh: Feel free to send those tweets out there, lot of good tweets already today. Thank you folks for that, and we will be right back. Yeah, we heard that metadata bell in the opening segment there. That’s our favorite word because it’s just everywhere in this conversations, there it is. One of these days we will may be have some kind of a contest where the first person to tweet a bell something in there in it with referencing the metadata bell will wins a trip somewhere, maybe to New York, the Source Media Headquarters.
Jim Ericson: Oh, I am too scared away exactly.
Eric Kavanagh: Hey let’s bring in our next guest, David Inbar from Pervasive Software. Welcome to DM Radio.
David Inbar: Hi, good afternoon, Eric. It’s good to be here.
Eric Kavanagh: Sure, thanks. So in the opening segment you heard Philip talking about his mega trends, and of course Byron brought in this really good point about self-service BI helping to avoid that bottleneck of people, but there are lots of other bottlenecks out there, and gosh, I know one way to deal with them is that a thing called massively parallel processing, right?
David Inbar: Yes, indeed. So that you are seeing more and more attention being paid to things like Hadoop which is about parallel processing and other aspects of parallelism, and in many ways that’s exactly how the computer industry as a whole continued to expand its capabilities to handle increasing volumes of data, voice traffic, and everything else. And we are suddenly seeing a lot of that challenge being confronted now. I felt that LinkedIn example was interesting because they are certainly handling very large volumes of data everyday. We know that they are adding terabytes a day or terabytes an hour to their systems. And they are needing to churn through that, and they are identifying patterns and opportunities as well for themselves and their customers.
But for LinkedIn, it is a little bit unusual. They are Web company, and they have access to huge numbers of servers, either in their own systems or out in the cloud, and they write the checks for those, but most companies do not. So the question comes into play, how do they get positioned to take advantage of the increasing volumes of Web traffic and location-based data and other information that they could potentially leverage and that isn’t in their formal existing data systems today?
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Eric Kavanagh: Yeah, right, I am glad you brought Hadoop back into the picture just because it’s such a hot topic these days, and I think it’s still a long way from maturation, but it’s surprising to see how quickly, certainly the data management vendors have jumped on that bandwagon or are talking a lot about it. It does change the way you view the information life cycle, right, or the data life cycle because now there is a cheap way and much cheaper than before way of storing very large amounts of data.
And you can get pretty creative if you want and start grabbing things like Twitter feeds and LinkedIn discussions and Facebook discussions for things like brand awareness and stuff like that, and boy if you can find a way to glean insights from those feeds you can be a lot more agile than otherwise, right?