Holly: Yes, this is Holly here, so I'm really excited to see this trend and all the focus on the Big Data tools. It's very interesting to see in the industry, the specific analytic requirements for encrypted data. A lot of the vendors in the BI industry are focused very heavily on reducing latency for interactive analysis and queries, all through the whole platform and making Big Data approachable as well as fast.
So, you will see a lot more today in the other trends that speak to this major trend and focus on analytics of the Big Data. So that's three, and then our customers are very excited with this and are participating in this. It's very exciting to me to see this happening.
Abhishek: Great, all right onto trend number two. Big Data no longer is just Hadoop. Purpose-built tools for Hadoop have become obsolete. Holly, how about you kick us off with some thoughts on this one?
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Creating a Data Platform
Holly: So our strategy as Abhishek as mentioned right at the beginning, a lot of people don't even want to differentiate Big Data anymore. It's just data, and in fact it's is very similar for Hadoop. Even Hadoop is no longer just Hadoop, and that's actually very important. The underlying trend is that we're creating a data platform, for in our case, specific analysis.
The data platform that's being created is for many purposes, but obviously we're focusing on BI and analytics, but that platform is widening and broadening and deepening and what specifically this trend speaks to is the underlying changes. Because of that broadening of the trend many of the analytic tools of BI tools that originally were developed just for Hadoop or just for the early Big Data platforms have become rather obsolete.
Because the platform and the bulk definition of Big Data and Hadoop has widened, those tools have become obsolete, increasingly with the digital revolution and the internet of things there are a lot of new data sources coming in every day. Those data sources are coming out faster as we will see later on, and so a lot of the tools that were designed specifically for Hadoop have had trouble gaining traction and that's because of the underlying direction of the platform itself to be broadened and deepened.
Abhishek: Yeah, from my perspective this is a testament to the adoption and the importance that Hadoop is playing in the analytics ecosystem. For customers, where, again back to that point, it's not just Big Data. It's a piece of the strategy installed. It is just data at this point for companies, and so they need a unified set of tools that support all of their different data stores and data engines, Hadoop being one core important one.
Application to Fraud Loss
So you know we've seen that fraud loss has always been to be as heterogeneous as possible and works incredibly well with Hadoop as well as your relational data storage and your fast analytical data warehouses and kind of whatever is coming, but we've seen a number of our partners as well who started being Hadoop specific companies.
Others that have expanded their offering as well to support more sources than just Hadoop, and that's, to me that's actually a really positive thing around the overall adoption. Hadoop is not kind of a silent science project anymore, but it is a core piece of the analytics platform, and the tools it supports need to work across other pieces of the analytics platform. Larry, any other thoughts from your perspective on this one.