Knorr: How do you see Pivotal as a VMware-type play?
Maritz: In the sense that when EMC acquired VMware, they looked at it and said, "This is a big enough and transformative enough opportunity that we're not going to integrate it back into EMC. We're going to keep it separate for a variety of reasons to give it focus, to allow it to work with the EMC competitors, and above all to give the people who are in VMware a sense of ownership and connection to their own destinies." It was ... incredibly successful, and it gave the respective board of directors enough belief to say it's worth doing that again. The downside of the deal is when you expose it to the cold light of day at a very early stage -- you get that onus put on you.
Knorr: I met with John Roese, CTO of EMC the other day, and he said that Pivotal was two years ahead of its time. Do you see it that way?
Maritz: It depends on which point in the market are you trying to measure. We get more and more convinced that we're pointed in the right direction and the early adopters are moving with us now. Until you get the broad bulk of the market, like every market, it takes a while. If you look at virtualization, the first VMware Conference was 10 years ago. It took eight years for them to cross the 50 percent threshold. So these things happen over a period of time, but the key thing is to be aligned with the tides of history. And this is one where we're convinced that the tide is definitely coming in.
Knorr: Is there a way to characterize how applications built on your platform are going to be different?
Maritz: Well, let's take that from a couple of angles. One is that by putting your application on Cloud Foundry, you're getting two things: You're getting cloud independence and automation, which is true of both existing and future applications. The future applications are really characterized by using new data fabrics. If PC servers and minicomputers made CPU cycles free versus the mainframe, what is the cloud making free versus PC servers and Unix minicomputers and the rest of it? The things that they're making free are basically the ability for a developer to work with a large number of machines. For a developer today, it's no longer an avant-garde thing to say I need 10 machines, 50 machines, 100 machines, to throw at the problem.
So you get a lot of machines that a single app can dedicate to its problem. And certainly storage is becoming very cheap, so you can say you want to store this big blob of bits and store it for all time. That's no longer an extreme thing to do.
Those two things coming together means that the data fabric -- the database -- gets remade. Hadoop is an early example of the remaking of the database. Think of what Hadoop is. When it was pioneered first at Google, they in some sense deconstructed the traditional relational database and they took the persistence, the actual storage of the information and put it into a scale-out object store, which in Hadoop's case is HDFS. And they took the processing of the information and they moved that into collection of machines working in parallel on top of HDFS. So they were taking advantage of these two quantities that became "free" -- a large number of machines working with the information stored in a cheap scale-out object store.
We think that paradigm gets reapplied to all of the important ways we're working with data, because it allows you to work with much bigger data sets, much more quickly, much more cost-effectively. We think that's going to happen to relational query, to transaction processing, to high-speed event ingestion, etc. The whole database world is going to get remade to take advantage of large numbers of machines working in parallel with big bodies of data stored in an HDFS or similar object stores.
By taking advantage of those capabilities, you can build applications that reason over much bigger datasets, much more diverse datasets, and getting more value out of them, doing it more quickly, doing it more cost-effectively, and then being able to actually use that to drive some interaction with the user. So it's not just about analytics, where you kind of get some insight into the data. It's about how you use that in the context of some application that's going to drive a transaction or cause some interaction with the user.
So that's why we talk about applications and data. We're not just in the big data business. We're in the applications and data business, using these new resources that the cloud is going to manufacture for us to allow you to do things you simply couldn't cost-effectively do on a traditional, cloud server relational database architecture.
Knorr: In that whole description I don't think you mentioned the Internet of things once, which was a key point in your launch and announcement of the GE partnership. You could argue that big data won't be really big until the Internet of things ramps up.
Maritz: The Internet of things dramatically exacerbates it. As you heard me mention, one of the important data modalities is going to be this high-speed event ingestion.
The Internet of things is going to mean there will be many more events flowing in much greater quantities. In a lot of cases, it won't just be about ingesting them and analyzing them, but about reacting to them in real time. This is not about having a data scientist look it through. We're going to do something now.