Good day, everyone, and welcome to the Oracle Executive Access for Investors conference call. Today's call is being recorded. At this time, I'd like to turn the call over to Paul Ziots, Director of Investor Relations. Please go ahead.
Thank you, Carissa. Hello, everyone, and thank you for joining us today for Oracle's Executive Access for Investors, an educational webcast series hosted by Oracle. Today is Thursday, April 12, 2012. Joining us today is Andy Mendelsohn, SVP , Oracle Database Server Technologies, and Equity Research Analyst, Kash Rangan from Bank of America Merrill Lynch. Today, Andy will discuss big data, why this is good for Oracle, and Oracle's key products and technologies, including the Oracle Big Data Appliance. Please note that Andy will not be discussing any information today that is not already publicly available. Also, please note that we'll be conducting a separate web event on another hot topic, analytics and Oracle's Exalytics in-memory machine. That will be later this month. At the conclusion of Andy's presentation, we'll turn the webcast over to Kash, who will kick off the Q&A.
You may submit questions at any time during the presentation by clicking on the Ask a Question tab above the webcast slides. Please keep in mind we will not comment on business in the current quarter. As a reminder, the matters we'll be discussing today may include forward-looking statements, and as such, are subject to the risks and uncertainties that we discuss in detail in our documents filed with the SEC. Specifically, the most recent reports on Form 10-K and 10-Q, which identify important risk factors which could cause actual results to differ from those contained in forward-looking statements. You're cautioned not to place undue reliance on these forward-looking statements, which reflect our opinions only as of the date of this presentation.
Please keep in mind that we're not obligating ourselves to revise, update, or publicly release the results of any revisions of these forward-looking statements in light of new information or future events. Lastly, unauthorized recording of this webcast is not permitted. Now I'll turn it over to Andy.
Thanks, Paul. Before we get into big data, I thought I'd just alert you guys to the fact that recently Gartner just published their relational database market share data for 2011. It's interesting, you know, in this era of big data to look at what happened, according to Gartner, over the last year. We're very happy to report that Gartner is saying the database market is very healthy, growing over 16.3% per year at a $24 billion market. We're also very happy that they announced that Oracle grew over 18% in the database market, and we took market share. Our number two and three competitors, Microsoft and IBM, actually lost market share in that market. The competitor Teradata, that's actually known as a big data data warehouse company, actually didn't grow nearly as fast as we did in this very vibrant relational database market.
Feel free to go out and talk to the Gartner analysts and get more details on what happened over the last year. OK, with that, let's move into the big data space. Everybody's seeing a lot of buzz around big data, and everybody's trying to figure out what does this mean for our customers and our industry. Let me just start off by sort of explaining this with an analogy. We all know cars. You know, Henry Ford invented the Model T 100 years ago. Today, Ford makes cars, and they have much better engines, and they're full of sensors and computer systems. At the end of the day, they're still cars. Cars have evolved a lot over the last 100 years. Big data should be thought of in the same way. Today, we have our information systems for business intelligence. People call them data marts and data warehouses.
They're loaded with all this transactional information from our transaction processing systems, like e-Business Suite and other application vendors. That information is really valuable. It's the crown jewels of companies, that transactional data. It's not going away. What people want to do with big data is that they just want to look at capturing new kinds of information to enhance and enrich that transactional data that they're currently using. For example, if you're a retailer, you might want to go out to Facebook and pull out information from your customers' Facebook pages, if they're willing to friend you. Most of that information is completely worthless, right? You know, all the pictures of babies and families and all that stuff. You don't want to keep that in your relational databases. The fact that somebody actually had a baby is really interesting to a retailer, right?
They can use that to upsell baby bottles and baby toys and everything else to babies. What's really important about big data is to understand there's a lot of this data. Most of it's completely worthless to the business, but there are really these gems, these nuggets of information, like the fact a customer just had a baby. You want to take that information, integrate it to your existing transactional data that you've got in your data warehouse, and really use that to make better business decisions and make more money for your company. OK, with that, let's go to the next slide. The basic idea with big data is that across all the various industries, there's new kinds of data that people are looking at using to augment their transactional data, to use that to grow their business and make better business decisions.
On this slide, we just highlight five different kinds of big data. We go through health care. There's a lot of remote patient monitoring and sensor data. Manufacturing organizations are using sensors to gather a lot of information about the manufacturing process. Of course, everybody knows that our cell phones have GPSs and are gathering location data about our every movement. Retailers are looking at social media, like I just mentioned, and trying to understand what their customers are up to and doing things like sentiment analysis. I'll just walk you through health care a little bit, because everybody really understands this really well. What's this all about? You're out on the tennis court, you're playing tennis, suddenly your arm doesn't feel quite right. You sit down, you're short of breath, you go to the doctor. The doctor looks at you, and you're perfectly fine. He can't see anything wrong.
In the era of big data, what are we doing now? We put sensors. The doctor puts a heart monitoring sensor on the patient, and he's out there. Again, most of that data being gathered is completely worthless. It just says the patient's heart is just perfectly fine. He goes out and plays tennis again, and it happens again. This time, the sensors are capturing what's going on. This is a really good example of big data. That information about the event that happened when you started feeling pain, that's what the doctor wants to look at. He doesn't want to look at all that other data that you've been capturing for the whole week that just says you're perfectly fine. This is a very common thread in big data. There's a lot of it. Most of it's completely worthless to the business.
You want to sift through that big data. You want to pull out the nuggets, in this case, about your potential heart problems. That's what you want to look at. That's what you want to integrate with your existing health care systems about this patient. You don't want the volumes of data that just says the patient's perfectly fine. OK, let's go to the next slide. The next slide is sort of what we and the industry analysts have sort of abstracted away from big data. What are the key elements that make something big data that's really new? They call this the four Vs: volume, velocity, variety, and value. Let me just go through this a little bit, because I think all of these are, you know, people think big data, they think, oh, it must be just big. There's a lot of it. That's true.
People like to think, oh, well, existing information, there's just so much data the existing information systems and databases can't deal with it. That's sort of, you know, that's crazy, right? The existing information systems and relational databases out there can deal with humongous amounts of data. We have petabyte data warehouses running on Oracle Exadata today. Customers have no problems dealing with the volume of data. OK, that is a characteristic that people like talking about. Velocity is another thing people like talking about. For example, the sensor data I mentioned, it's constantly streaming large amounts of data from this sensor on my heart to monitor what I'm doing. In a manufacturing process, there's a lot of this data streaming. It's really fast. GPS data is the same way. People like talking about velocity. Again, it's all about, you know, there's this large volume of data.
We can deal with large volumes of data. There's really nothing special about that. What's really new here is variety. There are new kinds of data, as I mentioned, beyond the traditional transactional data that people are using to make business decisions. There's social media data, like Facebook. There's Twitter. There's blogs. There's sensor data from smart meters and from manufacturing sensors or heart monitors, things of that sort. There's new kinds of data that people are gathering. This is what people refer to as variety. This is really the core of what people are getting at in big data, just new kinds of information. We want to gather it. Some of it's structured. Some of it's less structured, more textual in matter, like Facebook pages or whatever. The bottom line, it's just data.
We want to have information systems that are able to analyze that data and capture the valuable parts of it. Finally, value. Between variety and value, I think you really get the real essence of big data. Like I mentioned, this big data, there's a lot of it and high volumes of it. The real challenge is that most of it has very low business value. The real challenge is you want to sift through this data. You want to find the gold nuggets that's really valuable. That's the data you want to integrate into your data warehouse, integrate with your transactional data, and use that to make better business decisions. The real key isn't that relational databases can't store this data. Relational databases can store all this data. The real key is that most of the data is worthless to the business. You don't want to store it.
You want to only capture and store the really valuable information. That's what you want to store in your high-performance relational databases. OK, let's go to the next slide. Why are people so excited about big data? It's the next generation of the next evolution of our business intelligence information systems. For all these systems, what's the goal? The goal is to grow our revenue, to grow the bottom line, cut expenses, etc. McKinsey has done some studies. On this slide, we just point out some of the data they point out about how big data can be used to improve businesses. You feel free to look at the study in more detail. OK, in the next few slides, I'll just very briefly walk you through what we're doing at Oracle in our products to deal with big data.
What we like to do at the high level, what big data is all about is, as I said, it's an evolution of our information systems for business intelligence. The goal is to make better business decisions, to grow our revenue, to cut our expenses, etc. We look at it as a life cycle of data. The big data comes in through an acquisition phase. You want to, what we call, organize the data or sift through the data, looking for those gold nuggets of valuable information for the business. You want to load that data into your data warehouse, analyze it, and integrate it with your existing transactional data, and then use that to make better business decisions. Those are the four main phases. Let's go to the next slide.
On this next slide, which is a bit of an oversimplification, I just tried to position all the various software technologies that Oracle is deploying to provide a big data solution for our customers. I've organized it according to these four phases: acquisition, organization, analysis, and business decisions. There's a little color coding here. Red means basically software that Oracle has built and our existing Oracle products. The gray are the open source components we're getting from Apache Hadoop. Why don't I just go through this a little bit from left to right? The big data is coming in, and we show with these icons from social networks, maybe, or from sensors and Twitter, et cetera. We have an array of tools for capturing that information.
What's new in the big data space is HDFS, which is the Hadoop file system, the distributed file system, which some people are using for capturing large amounts of this big data. Oracle is also providing what we call the Oracle NoSQL Database, which people are using also in this acquisition phase sometimes. NoSQL databases are next-generation key value stores. Key value stores have been around for 40 years. You may remember them, and those of you who have them, go back to the mainframe days, where they have these things called indexed sequential access methods. They basically store a key and a value, and they let you look up by a key and get the value back. What's new there is these are now very scalable, because instead of having one index, essentially, you can now have hundreds of indexes.
You do a hashing technology layer to distribute the data across the 100 key value store indexes. That technology, like I said, has been around a long time. There's an established market for it. It's not very big. Oracle is actually the biggest player in that market. We have a product called BerkeleyDB, which is the leading key value store. We have now extended BerkeleyDB into what we now call the Oracle NoSQL Database to have a distributed key value store. Finally, of course, all the transactional data is coming in through enterprise applications, like Oracle e-Business Suite, PeopleSoft, SAP, et cetera. Lots of Oracle databases are under the covers there gathering that information. That is sort of where all the data is coming from in the big data world. OK, the data is flowing through to what we call the organization phase.
There is where Hadoop has a major role. What is Hadoop? Hadoop consists of this distributed file system that we mentioned earlier. It has what is called a MapReduce engine, which is just a Java development platform for building parallel applications. Hadoop is an interesting technology. It is an open source technology provided by the Apache Foundation. We work with Cloudera at Oracle. Cloudera has a distribution of Hadoop that we are using in our Oracle Big Data Appliance that I'll talk about on the next slide. Like a lot of new technologies, there is a lot of hype around it, but it has a lot of maturity problems. I'll just go through a couple of the issues around Hadoop. Number one, it is a development platform for very sophisticated Java developers to build parallel applications. One of the big problems around Hadoop is a skill set problem.
I talk to customers all the time, and they just don't have developers who know how to write these MapReduce programs. One of the big challenges of Hadoop is to raise the level of discourse around Hadoop. You don't have to have rocket scientists, Java parallel programming developers, but you can code at a higher level. One of the interesting things that people are looking at now is, how do you do that? In the relational database world, there is this language called SQL or SQL that we all provide. In the Hadoop world, they're saying, that SQL thing is actually pretty cool. If we provided SQL on top of Hadoop, we could open this up to lots more people that could actually write queries and reports on Hadoop. In the Hadoop world, there is a project called Hive, which is actually a very simple SQL engine.
I sort of agree with them. If Hadoop is going to be successful, it is probably going to have to have SQL. Pretty soon, it is going to look like an analytic relational database. That's a very interesting future for them to go. I guess we already know that market. That's called the relational database market. The other issues around Hadoop are just the typical stuff for a new platform. It doesn't have very good technology for security. It doesn't have very good technology for disaster recovery. Of course, relational databases do all that kind of stuff already really well. It's got some growing up to do in that phase. Let's talk about the red boxes in this column. This is where Oracle is trying to add some value to the Hadoop world. I'll just briefly talk about a few of these things.
Oracle Data Integrator is a code generator that Oracle uses today as an ETL tool for data warehousing. It is able to generate MapReduce programs automatically for customers based on data flows and transformations using a graphical user interface. Oracle Data Integrator is another way of raising the level of discourse for developers to not require incredibly sophisticated MapReduce programming skills. That's part of our offering in this big data space. We also have a loader for Hadoop. Once you get the data you want and you want to load it at a high performance into an Oracle data warehouse, we have the Oracle Loader for Hadoop, which is a MapReduce program that my developers have written to very rapidly move the valuable gems of information you find in the Hadoop world into an Oracle data warehouse at very high performance.
Finally, the last thing is something we call Oracle Direct Connect for HDFS. This is actually very cool. What we're doing here is we're saying, you know, Oracle has a really good SQL engine. The people in the Hadoop space really want SQL. What we are able to do with Oracle is use our technology for what we call external tables to basically let the Oracle SQL engine actually reference data in the Hadoop file system, pull it in via standard SQL programming constructs, query constructs, and let our developers write SQL that actually spans across data that's in the Hadoop file system and in the Oracle relational database, join it together, sort it, do all kinds of analytics. We think it's something a lot of our customers are going to be interested in. Let's move to the next column, the analysis phase. Here is what I talked about before.
We found those gems of data using our Hadoop MapReduce engine. We want to load them into the Oracle Database at a really high performance. We want to integrate it with our transactional data, enrich that transactional data, and then we want to analyze it. Of course, Oracle has great tools for doing that sort of thing. The one thing I just wanted to call out is in the Oracle Database, we have very powerful in-database analytic capabilities. There's not only SQL, which is the obvious analytic technology that comes with all relational databases. Oracle has also recently integrated R, which is a very powerful statistical and mathematical analytics development environment. We've integrated R into the database. It runs in the database and does very high-performance analytics on the big data in an Oracle Database.
We also, of course, have all the predictive analytics data mining algorithms that customers are looking for. Plus, we actually have very powerful spatial and other unstructured data analytics technologies built into the database. All this talk about relational databases aren't good for unstructured data is all nonsense. Fifteen years ago, relational databases became object relational databases. The whole objective of that was to make relational databases good stores, not just for structured data, but also for unstructured data. If you look at typical relational databases today, it's very common that more than half the data in those databases are unstructured. You go out to banks. Banks have scanned all the checks into their relational databases. The IRS stores all the tax returns in relational databases, where relational databases are loaded with spatial, geocoded information, text documents, XML documents, everything you can imagine.
The relational databases are very powerful platforms for storing and analyzing all kinds of data, both structured and unstructured. Finally, as we want to analyze the data, Oracle and lots of other vendors have query and reporting tools. We have analytical applications of various sorts, data discovery tools, and basically, all that stuff will be used and employed against the big data. OK, let's go to the next slide. One of the unique things Oracle has been doing over the last few years is, since we bought Sun, we have been building what we call engineered systems.
These are systems that combine our hardware and our software together to deliver very unique capabilities for customers, as far as incredibly high performance, really good time to value, which means the customer buys the system, they roll it into their shop, and they can be up and running in a few days, kind of thing. These have proven to be very popular. In the big data space, I just wanted to make sure everybody understands we have a complete set of engineered systems for helping customers very rapidly deploy big data solutions. We have everything from the Oracle Big Data Appliance that I'll talk about a little bit more in our next slide, and Oracle Exadata, of course, which all of you, I'm sure, are very familiar with, which is our very high-performance platform for running all kinds of workloads, including big data warehouses at really high performance.
Finally, Exalytics, which is our latest engineered system for doing business intelligence and for running Oracle's very powerful business intelligence tools, like BIE and our new Oracle Endeca data discovery technology. We'll be having a separate briefing on that technology. I won't go into that in any more depth. OK, let's go to the next slide and drill down a little bit on the Oracle Big Data Appliance, because that's the main topic for today. What is that? The Oracle Big Data Appliance is our engineered system for running Hadoop and the Oracle NoSQL Database at very high performance and very good time to value. If you're an Oracle customer today and you're a business who wants to get into the Hadoop space, you can just call us up, and we will sell you a rack, which is the Oracle Big Data Appliance.
A rack, which is essentially an 18-node Hadoop cluster in a box. It's got lots of cores, lots of memory, lots of storage. If you look at the price per terabyte of this box, we are a very aggressively priced technology. It's not a premium-priced product, like some people seem to think. It's a very competitively priced product. Our price per terabyte is very good. We're using standard off-the-shelf Sun commodity Intel servers. The key thing in the Hadoop world is people don't want just 18-node clusters. Some people want to run 100-node clusters or 200-node clusters. This Oracle Big Data Appliance is a building block. You start with one. If you need more, you buy another one, and you connect it over the InfiniBand interconnector we use here. You scale out.
It is very easy to scale out to build out very large clusters running Hadoop or the Oracle NoSQL Database. One of the big benefits, of course, is that you buy this from Oracle. We give you a single source of support for all the software and all the hardware that you use here. It is something that a lot of our customers are very interested in. What is the business value? It is what we mentioned before. You can use the Hadoop engine and NoSQL engine to gather information, big data information, to sift through that information to find the gold nuggets. I mentioned earlier, we have now integrated R with the Oracle Database to provide in-database analytics using R. R is also available on our Oracle Big Data Appliance for doing analytics in the Hadoop space. You can integrate R with your Hadoop parallel MapReduce programs.
One of the big values of this technology is to integrate with our existing data warehouses. Those of you who have heard about Oracle Exadata know that over the next few years, every Oracle customer who has a big data warehouse or data mart is going to be moving to Exadata. The benefits are just incredibly compelling from a price performance standpoint. One of the big focuses of the Oracle Big Data Appliance is to be a very good companion with an Exadata data warehouse. We use the same InfiniBand technology we use in Exadata to be the interconnect for the Oracle Big Data Appliance and to make it very easy to connect the Oracle Big Data Appliance to an Exadata data warehouse at very high performance. I mentioned earlier, we have this loader for Hadoop technology.
One rack of an Oracle Big Data Appliance can load 15 terabytes per hour of data into the Oracle Exadata data warehouse over this InfiniBand interconnect. We talked earlier, we have this direct connect for HDFS capability that lets the Oracle SQL engine reach out into the HDFS file system and analyze big data. We talked about the Oracle Data Integrator already as well. To close off the discussion, I want to talk a little bit about what our customers are up to. We are talking to a lot of customers about what they would like to do in this new world of big data information systems. I thought I would mention three of the customers and what they are doing, just to give you some insight into what is going on out there. The customers are in three industries: insurance, travel, and games.
Insurance is an industry we all understand pretty well. We all have car insurance. This particular customer already has an Oracle Exadata data warehouse. They're already capturing, of course, all their transactional insurance information about their customers, their accidents, their policy information, et cetera. What they would like to do is enhance that data with a new kind of data that you can get from cars. Cars are now loaded with sensors. They're capturing your every movement of what's going on out there. It's called car telematics data. What they would like to do is use that information to actually study the actual driving behavior of their customers and use that to better understand maybe what their insurance rates should be, what their driving habits are, and maybe even help customers be better drivers. This is actually a very classic use case.
They're interested in augmenting their Oracle Exadata system with an Oracle Big Data Appliance, big data appliance. They're very interested in our R technology as well. Next customer is in the travel industry. They run websites for customers who are looking at doing travel of various sorts. Today, of course, they're already capturing all the transactional data about their customers, what are the trips they're buying. What they would like to do is augment that information with what's going on in their websites. They want to capture the web logs. They want to get social media data to better understand what their customers are up to, what are the trips they're anticipating maybe going on, and combine that information with their existing information about their customers' previous transactions and use that to help make better promotional offers to the customers and grow their business.
The last customer is in this game space. Gaming is becoming a huge industry, as you all know. This company is in the business of selling game consoles of various sorts and internet games. They already have a big Oracle Exadata data warehouse already that's analyzing that information. They're looking into augmenting their Oracle Exadata data warehouse with an Oracle Big Data Appliance. They want to use it to, what you might expect, better understand what the customers are doing out in the games. They want to understand relationships between customers. One of the really interesting things in games is that people play games with each other. You want to understand the social networks of people who are playing with each other, because it's likely that if one person in that network wants to do something, the others will want to do the same thing.
They can use that information to better upsell information in this game space. That's three good examples. I can tell you, just talking to customers, there's a lot of interest in this space. As I mentioned earlier, it's a real evolutionary technology. Customers have their existing big business intelligence systems, their data warehouses, their data marts. They're really excited about augmenting their transactional data with this big data to help them grow their business. To conclude on the last slide, just to summarize what we're up to, Oracle is very unique in this space. We not only have a complete set of software for dealing with big data information problems, we also have a complete set of engineered systems as well. Customers can very rapidly roll out for their businesses complete deployments of big data hardware and software solutions.
As I mentioned earlier, our technology deals with both structured and unstructured data. We deal with SQL, and of course, we also have NoSQL technologies as well. We provide, because of this unique hardware-software combination, the fastest time to value for customers. If you're an IT guy and you want to roll out a big data system, you can just call up Oracle, and you can deploy the system incredibly fast by rolling out our engineered systems. Oracle will provide single vendor support across all the hardware and software that you're using in your big data environment. With that, I think we'll move into a question phase.
Right. Thank you, Andy. Before we go over to Kash, I just want to remind everybody that you can ask a question online by clicking the tab, Ask a Question. With that, Kash, please go ahead and start the Q&A.
Sure thing. First of all, thank you, Andy, Paul, and Ken, who may be on the line, for giving us a chance to come in and spend some time with you guys. I think you're back, Andy. We spoke about the database market itself. It looks like listening to you more and more, it feels like this big data evolution reinforces the core of what Oracle does really well and adds more new opportunities around it. I think there's a lot of confusion and misperception as to what exactly big data is. Thank you for doing this webcast with us. For investors on the line, we published a report today on Oracle and how it fits into big data and what Oracle is exactly doing on the big data side. With that, one item I wanted to get your input on is Cloudera. Why did you choose to work at Cloudera? I think there are certain other distributions out there in the marketplace. Just your thoughts on that.
We are very in the open source space. We are very interested in providing technologies that sort of conform with the open space ethos. Just like in the Linux space, we have the standard Linux distribution based on open source. It's not proprietary. The thing we liked about Cloudera versus some of the other distributions out there is they're not a proprietary distribution. If you store all your data using the Cloudera distribution for Hadoop, you're using the standard HDFS file system. If at some point in the future you decide Cloudera was nice, but you want to use somebody else, it'll be easy for you to move, because you're not locked into some proprietary technology. We really liked Cloudera's openness. Also, we think their expertise in the space is really good. They have some of the leaders of the Hadoop engineering community, open source community, working at that company. We thought they'd be a great partner for us to work with in this Hadoop space.
Got it. The second thing I want to touch upon is Hadoop and NoSQL databases have been designed for running on large commodity clusters, with the horizontal scalability being a key emphasis there. I'm wondering, when we think about the Oracle Big Data Appliance, what is the differentiating value proposition for this appliance? It looks like there is a lot of activity around Oracle with other competitors doing similar initiatives. What is the right way to think about your differentiation here?
We understand that the Hadoop community of customers, one of the things they like about it is the fact they can use commodity servers to build these clusters at very good price performance. When we built this system, we decided to just use standard off-the-shelf Intel servers for building out these Hadoop clusters, just like everybody else. We knew that the pricing has to be aggressive in this space. We can't charge some premium, ridiculous pricing for something where one of the attractions is that it's low cost. If you look at our cost per terabyte of this technology, it's very well priced. We think it'll be very attractive to our customers. We add a lot of value on top of that. Oracle has end-to-end support for all the software and hardware components here, which our enterprise customers really appreciate. Nobody else is doing that.
One of the big features, of course, is our integration with other Oracle systems. I talked about Oracle Exadata. I talked about Oracle Exalytics. Our install-based customers who are using Oracle Database, they might be using Oracle Exadata and BIE and Oracle Exalytics. This is a natural fit for them to expand into the big data space from their existing business intelligence systems. I mentioned we have a bunch of software that my group is building. We're building MapReduce software for our customers. We have the Oracle Data Integrator capability that does code generation for Hadoop. It makes the development of Hadoop MapReduce programs easier. We also have our SQL engine extended so they can reach out into the Hadoop space again to make Hadoop more accessible to the masses who know SQL but don't know how to do MapReduce programming.
We have R integrated into our Hadoop offering, which is another big plus for customers who want to do analytics in the Hadoop space. We think we have a very interesting offering that's got both a hardware side and a software side and a support side that I think is going to be very attractive to our customers.
I think some of the use cases you talked about, those three examples, point to the example of how Oracle Big Data Appliance ties into your core business and ties into the use case of Oracle Exadata, data warehouse, database implementation. We're starting to see that. Although you announced the product itself just at the user conference six months back, it's interesting to see how quickly the use cases are pervading in the marketplace.
Yes.
I wanted to just drill into unstructured data a little bit, because it's a relatively less understood topic. There's been a lot of activity in this space. What are some of the key problems that you think Oracle can address with unstructured data? What are future areas of innovation in this market that Oracle can address?
Yeah, as I already talked about this a little bit earlier, we started the classical relational database model that was invented 30 years ago is what somehow people seem to think it still is. The classical relational databases stored numbers and dates and strings in rows and columns, and that's all they did, right? About 15 years ago, there was a big revolution in relational database technology that was called object relational databases. The whole objective of that was to extend relational databases to store all kinds of data, not just rows and columns of numbers and strings. As I mentioned earlier, relational databases have now been extended, and the technology is very mature. You can store what we call LOBs or files in a relational database, and you can read and write those files faster than you can through file systems, believe it or not.
Relational databases are really, really good, very highly optimized for dealing with unstructured data. That's just the raw unstructured data. On top of that, we have a lot of value add. We have text indexing in the database, so you can do unstructured keyword text searches using SQL. We have integrated products on top of that. We have a solution called Secure Enterprise Search that actually lets you use relational databases as a search engine. We have spatial technology, so you can ask questions in relational databases using geo about coordinates. You can say, show me all the stores within five miles of this location. Cell phone GPS information is all over relational databases. There's a lot of value added. We have text mining, actually, algorithms in the database. You can look at all your documents. You can classify them. You can do all kinds of stuff.
We have data mining algorithms as well. Relational databases are very enriched structures for storing structured and unstructured data and for analyzing structured and unstructured data. That's just the Oracle database side. Oracle also has other technologies. The most recent one is a product we actually bought in acquisition called Endeca. We have the Oracle Endeca Information Discovery tool. This tool is also a very flexible technology that can analyze both structured and unstructured data. It's a combination of sort of traditional search keyword search-based technology and BI sort of look at the data and drill down into a technology that's very unique in the industry. It's a big part of our big data offering. It actually is being optimized to run on our Oracle Exalytics platform that we announced and launched just very recently. That is another big offering into that space. Oracle is very big into analytics.
We have vertical industry analytics and we have horizontal analytics through our BI apps products. In this whole space, we're very focused on all the data customers want, the structured and unstructured. We're very excited about this. This is a great opportunity for us as customers want to capture all kinds of information. We're an information company. We want to help them manage all their data, whether it's structured or unstructured.
My next question is actually going to be the synergy between your core business and unstructured data. I think you answered that very elegantly. Maybe move on to in-memory databases. A lot of talk, at least on Wall Street and perhaps in the industry too, in-memory databases. What are your thoughts? Is it an opportunity or a threat for Oracle?
Yeah, in-memory databases are an interesting technology. At some level, there are two things I want to get across. One is going back to my car analogy. Cars have evolved a lot over the years. One of the things people have figured out is that you can get really good performance at low cost with these things called turbochargers. You stick them on the engine, the engine runs a lot faster. They're expensive, so you don't put them in all the cars, but you put them in some of the cars. You can think of in-memory database technology as the same sort of thing. It's another feature of relational database. They can turbocharge the performance of the database. Like a lot of things, it's not for everybody.
For example, there's an economic side to in-memory databases that has been true for many years and is going to continue to be true for the next 5, 10 years. Disk drives, if you look at the cost per terabyte, are about 100 times cheaper than main memory. In between, there's a technology called Flash, which is about 10 times the cost of main memory. What customers always are going to want to do is say, OK, I want to get the best price performance out of my technology. Some customers, most customers, especially as we move into the era of big data, are going to say, heck, I can store this data really cheap on hard disks. I'm going to store all my data on hard disks. This is the approach Oracle is taking in our Oracle Exadata platform.
As that data gets warmer, we start reading it, we transparently move that data into Flash. As it gets really hot, we want to move that data into memory. That's where the in-memory database technology, in-memory column store technology that you hear a lot about, takes effect. All the relational database vendors are working on in-memory column store technology. It's a really nice feature. It's going to turbocharge databases. Pure in-memory databases, where you say, OK, all that data in my database has to fit in memory, that's really expensive. It's like saying, in the car industry, everybody's going to buy Ferraris. We all know everybody doesn't buy Ferraris. Why don't they? Because they're expensive. It's the same thing in the database space. People are driven by price performance. Inside a given company, some of those analysts are going to be given the Ferraris.
They're going to get those in-memory databases where all the data is in memory. Other analysts are going to get the economy cars and the midsize cars. We think the right strategy, and this is a strategy we're doing at Oracle, is dealing with a memory hierarchy. We think in-memory database, a pure in-memory database, where all the data fits in memory, has a place there, just like Ferraris have a place in the market. It's a market niche. It's not going to be everybody's going to be using that stuff. We think the right approach is to build a product, like we've done with Oracle Exadata, that deals with all forms of memory, from disk drives to Flash to main memory, and has the right balance to give our customers the right price performance that they want.
Got it. One other thing was there's some talk in the industry about the convergence of OLTP, the online transaction processing market, and the analytical market onto one platform that can handle both. What are your thoughts on that?
That's a very interesting space, because Oracle is very unique in that space. Today, if you buy our database machine or any other Oracle database, we can run OLTP in that database. We can run data warehousing. We can do OLAP. Oracle already today in our current products is the only product out there that can actually combine all these forms of processing in one database. If you look at what all the other vendors are doing, they are very specialized. IBM says, yeah, buy PISA for doing your big data warehouse. For OLTP, buy this DB2 product over here. Microsoft has the same thing. They have a parallel data warehouse product that's completely separate from their transaction processing product. Teradata only does data warehousing. They don't do transaction processing at all. We completely agree with that vision. We already do it today.
We're very unique in that space, and we will continue evolving and enhancing our products to continue to do better at OLAP and OLTP and data warehouse, all in the same database. That's been our strategy for 20 years, and it's going to continue being our strategy in the future.
Do we have time for one more question?
Please go ahead.
One other thing that I wanted to touch upon was just we would like to appreciate how strong the entrenchment of the relational database is. There are some in the industry that are talking about the risk of replacement, how easy it seems to be. I don't know how they get this vision, easy it is to replace a database and certify another application for another database. Can you talk about how should one view this entrenchment of the relational database? Why is it so tough to unseat from within the industry?
The relational database market is extremely competitive. We are where we are as the market leader because we have the best technology. We are the most innovative company. If we do not keep our guard up and keep innovating and bring out new technologies, like enhanced column store databases, et cetera, we are going to lose our position there. There is a stickiness, as you sort of referred to, when a customer is using Oracle and they want to move to another database. There is cost doing that. There is cost there. The main reason people stay with Oracle is not just that it is expensive to move to another database. It is that we have great technology that helps them solve their business problems better than anybody else's database. That is what this game is all about. Whoever has the best technology is going to win.
That is the game we have been playing for 30 years, and we are going to continue playing that game. If somebody wants to unseat us, they have to come out with a better product, basically. We love competing with other companies. That sort of gets our blood flowing. We are eager to take on all these new competitors in this market. We have been doing, as you saw from the Gartner market share data, we think we are doing a really good job of that and taking market share. We hope to continue that moving forward.
Our blood is flowing in the campus. We love the fact that there's a lot more dynamics in the industry. There's more debate, et cetera. Back to you, Paul.
Thanks, Kash. Let's squeeze one in here from the web before we conclude. Andy, one person is asking about, he's referring to Mark, actually. Mark heard some talk in OpenWorld in Japan recently talking about the growth, explosive growth in data expected from now until 2020, and something like 20 times the growth. This person is wondering about what your thoughts are regarding the contribution of big data to that growth.
The interesting thing about growth of database is that if you go back to like the year 2000, it's only 12 years ago, people thought a terabyte was a really big database. That was humongous. Now, 12 years later, a petabyte, which is 1,000 times bigger, is what people think is humongous. I have no doubt that what Mark's talking about, this whole exponential growth of data, is continuing. Of course, this is great for our business. The more data people have to manage, the more databases people want, and the more interesting technologies we can come out with to help them manage that data. There's no doubt that this is the trend. There's been this exponential growth in the size of databases, and it's going to continue. It's great for our business, and it's a really great challenge for my developers to come up with technology to help manage these huge databases.
Thank you, Andy. That's a great way to end up. Big data is good for Oracle. We would like to thank you all for joining us today on the web. Also, a special thank you to Kash for leading the Q&A portion of the call and for asking the questions that investors most typically want to hear about Oracle and big data. This concludes our call here. If anybody has any additional questions, please contact Oracle Investor Relations. Thank you for joining us today.