Abhishek: Yeah, that was a good question. We do not see things converging, and we think that the relational database is not going away. It feels like that was a little bit of hype years ago that that may happen, but in reality we are probably seeing the opposite. To the end-user or the data analyst or the knowledge worker who is working with data and seeing data, they want to see one interface to it.
Not to sound overly self-serving of something InetSoft tries very hard to be, but we can be that one interface. All the very different formats of data doesn't matter in reality in the back end of it. We are not seeing customers retiring the relational databases and moving to favorite non-relational store.
What they're doing, they are rightsizing potentially, and so there's less storing in proprietary MPP data warehouse platforms, as an example and storing more in the data lake and so they have a multi-tiered strategy at that point or they are using technologies for data virtualization, data federation to unify querying across a number different data sources.
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There's a lot of options out here which are interesting. It's really cool to see how customers are putting this together and implementing, but I don't think we are seeing one type of database or store taking over everything else. That's a really, really interesting, kind of multi-tiered architecture.
Larry: Here is a related question from Sally, and she's asking what is the key to focus on when translating data lakes into business intelligence. Should the end-user be an enabled data specialist? Well the answer to that is well the end-user could be a non-technical business user, it could also be the PHD data scientist trying to query billions of records.
Again to the point we've been making, the three-tiered architecture actually allows you to satisfy both end-user communities. If you want to query billions of records to try to understand clusters of customer behavior you should absolutely do that, but if you want to use it more from a KPI reporting standpoint, well we should enable that as well. That's where it makes sense to have data both in a relational data warehouse. It's an "and" not an "or."
Abhishek: That's right, great okay, our next trend, variety not volume or velocity drives big data investments, and this kind of gets down to those three Vs by which we define Big Data.
Holly, do you want to kick us off on some thoughts on this? Holly: Yes, so as data government defines Big Data or differentiates Big Data from other data originally is by the three Vs or the scale of the issue. It's the volume, the variety, and the velocity and we have tended to focus on the volume to call it Big Data. We have tended to focus on the volume, but the council here is to not just focus on the volume, and what we are seeing is it's the variety.
And the variety of the data is becoming just as important. One of the current big drivers is to make a big investment in these data platforms. For the new data platform investments, though I want to minimize velocity, certainly variety is definitely a key driver. We touched on it when mentioning the internet of things.
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All the different types of data that is coming in from new data sources all the time, and even and we are going to touch on this a little bit later on it speaks to some of the questions and some of the other trends, but a lot of people think of the data just coming in to the platform. But you should know the primary sources are your file system, web logs, internet of things, and increasingly the work platform itself is creating data.