>> Paige: Hello, everybody, and thank you for joining us today for the virtual Vertica BDC 2020. Today's breakout session is entitled, Vertica next generation architecture. I'm Paige Roberts, open social relationship Manager at Vertica, I'll be your host for this session. And joining me is Vertica Chief Architect, Chuck Bear, before we begin, I encourage you to submit questions or comments during the virtual session. You don't have to wait, just type your question or comment, in the question box that's below the slides and click submit. So as you think about it, go ahead and type it in, there'll be a Q&A session at the end of the presentation, where we'll answer as many questions, as we're able to during the time. Any questions that we don't get a chance to address, we'll do our best to answer offline. Or alternatively, you can visit the Vertica forums to post your questions there, after the session. Our engineering team is planning to join the forum and keep the conversation going, so you can, it's just sort of like the developers lounge would be in delight conference. It gives you a chance to talk to our engineering team. Also, as a reminder, you can maximize your screen by clicking the double arrow button in the lower right corner of the slide. And before you ask, yes, this virtual session is being recorded, and it will be available to view on demand this week, we'll send you a notification, as soon as it's ready. Okay, now, let's get started, over to you, Chuck. >> Chuck: Thanks for the introduction, Paige, Vertica vision is to help customers, get value from structured data. This vision is simple, it doesn't matter what vertical the customer is in. They're all analytics companies, it doesn't matter what the customers environment is, as data is generated everywhere. We also can't do this alone, we know that you need other tools and people to build a complete solution. You know our database is key to delivering on the vision because we need a database that scales. When you start a new database company, you aren't going to win against 30 year old products on features. But from day one, we had something else, an architecture built for analytics performance. This architecture was inspired by the C-store project, combining the best design ideas from academics and industry veterans like Dr. Mike Stonebreaker. Our storage is optimized for performance, we use many computers in parallel. After over 10 years of refinements against various customer workloads, much of the design held up and serendipitously, the fact that we don't store in place updates set Vertica up for success in the cloud as well. These days, there are other tools that embody some of these design ideas. But we have other strengths that are more important than the storage format, where the only good analytics database that runs both on premise and in the cloud, giving customers the option to migrate their workloads, in most convenient and economical environment, or a full data management solution, not just the query tool. Unlike some other choices, ours comes with integration with a sequel ecosystem and full professional support. We organize our product roadmap into four key pillars, plus the cross cutting concerns of open integration and performance and scale. We have big plans to strengthen Vertica, while staying true to our core. This presentation is primarily about the separation pillar, and performance and scale, I'll cover our plans for Eon, our data management architecture, Mart analytic clusters, or fifth generation query executer, and our data storage layer. Let's start with how Vertica manages data, one of the central design points for Vertica was shared nothing, a design that didn't utilize a dedicated hardware shared disk technology. This quote here is how Mike put it politely, but around the Vertica office, shared disk with an LMTB over Mike's dead body. And we did get some early field experience with shared disk, customers, well, in fact will learn on anything if you let them. There were misconfigurations that required certified experts, obscure bugs extent. Another thing about the shared nothing designed for commodity hardware though, and this was in the papers, is that all the data management features like fault tolerance, backup and elasticity have to be done in software. And no matter how much you do, procuring, configuring and maintaining the machines with disks is harder. The software configuration process to add more service may be simple, but capacity planning, racking and stacking is not. The original allure of shared storage returned, this time though, the complexity and economics are different. It's cheaper, even provision storage with a few clicks and only pay for what you need. It expands, contracts and brings the maintenance of the storage close to a team is good at it. But there's a key difference, it's an object store, an object stores don't support the API's and access patterns used by most database software. So another Vertica visionary Ben, set out to exploit Vertica storage organization, which turns out to be a natural fit for modern cloud shared storage. Because Vertica data files are written once and not updated, they match the object storage model perfectly. And so today we have Eon, Eon uses shared storage to hold Vertica data with local disk depot's that act as caches, ensuring that we can get the performance that our customers have come to expect. Essentially Eon in enterprise behave similarly, but we have the benefit of flexible storage. Today Eon has the features our customers expect, it's been developed in tune for years, we have successful customers such as Redpharma, and if you'd like to know more about Eon has helped them succeed in Amazon cloud, I highly suggest reading their case study, which you can find on vertica.com. Eon provides high availability and flexible scaling, sometimes on premise customers with local disks get a little jealous of how recovery and sub-clusters work in Eon. Though we operate on premise, particularly on pure storage, but enterprise also had strengths, the most obvious being that you don't need and short shared storage to run it. So naturally, our vision is to converge the two modes, back into a single Vertica. A Vertica that runs any combination of local disks and shared storage, with full flexibility and portability. This is easy to say, but over the next releases, here's what we'll do. First, we realize that the query executer, optimizer and client drivers and so on, are already the same. Just the transaction handling and data management is different. But there's already more going on, we have peer-to-peer depot operations and other internode transfers. And enterprise also has a network, we could just get files from remote nodes over that network, essentially mimicking the behavior and benefits of shared storage with the layer of software. The only difference at the end of it, will be which storage hold the master copy. In enterprise, the nodes can't drop the files because they're the master copy. Whereas in Eon they can be evicted because it's just the cache, the masters, then shared storage. And in keeping with versus current support for multiple storage locations, we can intermix these approaches at the table level. Getting there as a journey, and we've already taken the first steps. One of the interesting design ideas of the C-store paper is the idea that redundant copies, don't have to have the same physical organization. Different copies can be optimized for different queries, sorted in different ways. Of course, Mike also said to keep the recovery system simple, because it's hard to debug, whenever the recovery system is being used, it's always in a high pressure situation. This turns out to be a contradiction, and the latter idea was better. No down performing stuff, if you don't keep the storage the same. Recovery hardware if you have, to reorganize data in the process. Even query optimization is more complicated. So over the past couple releases, we got rid of non identical buddies. But the storage files can still diverge at the fifth level, because tuple mover operations are synchronized. The same record can end up in different files than different nodes. The next step in our journey, is to make sure both copies are identical. This will help with backup and restore as well, because the second copy doesn't need backed up, or if it is backed up, it appears identical to the deduplication that is going to look present in both backup systems. Simultaneously, we're improving the Vertica networking service to support this new access pattern. In conjunction with identical storage files, we will converge to a recovery system that instantaneous nodes can process queries immediately, by retrieving data they need over the network from the redundant copies as they do in Eon day with even higher performance. The final step then is to unify the catalog and transaction model. Related concepts such as segment and shard, local catalog and shard catalog will be coalesced, as they're really represented the same concepts all along, just in different modes. In the catalog, we'll make slight changes to the definition of a projection, which represents the physical storage organization. The new definition simplifies segmentation and introduces valuable granularities of sharding to support evolution over time, and offers a straightforward migration path for both Eon and enterprise. There's a lot more to our Eon story than just the architectural roadmap. If you missed yesterday's Vertica, in Eon mode presentation about supported cloud, on premise storage option, replays are available. Be sure to catch the upcoming presentation on sizing and configuring vertica and in beyond doors. As we've seen with Eon, Vertica can separate data storage from the compute nodes, allowing machines to quickly fill in for each other, to rebuild fault tolerance. But separating compute and storage is used for much, much more. We now offer powerful, flexible ways for Vertica to add servers and increase access to the data. Vertica nine, this feature is called sub-clusters. It allows computing capacity to be added quickly and incrementally, and isolates workloads from each other. If your exploratory analytics team needs direct access to the source data, they need a lot of machines and not the same number all the time, and you don't 100% trust the kind of queries and user defined functions, they might be using sub-clusters as the solution. While there's much more expensive information available in our other presentation. I'd like to point out the highlights of our latest sub-cluster best practices. We suggest having a primary sub-cluster, this is the one that runs all the time, if you're loading data around the clock. It should be sized for the ETL workloads and also determines the natural shard count. Additional read oriented secondary sub-clusters can be added for real time dashboards, reports and analytics. That way, subclusters can be added or deep provisioned, without disruption to other users. The sub-cluster features of Vertica 9.3 are working well for customers. Yesterday, the Trade Desk presented their use case for Vertica over 300,000 in 5 sub clusters running in the cloud. If you missed a presentation, check out the replay. But we have plans beyond sub-clusters, we're extending sub-clusters to real clusters. For the Vertica savvy, this means the clusters bump, share the same spread ring network. This will provide further isolation, allowing clusters to control their own independent data sets. While replicating all are part of the data from other clusters using a publish subscribe mechanism. Synchronizing data between clusters is a feature customers want to understand the real business for themselves. This vision effects are designed for ancillary aspects, how we should assign resource pools, security policies and balance client connection. We will be simplifying our data segmentation strategy, so that when data that originate in the different clusters meet, they'll still get fully optimized joins, even if those clusters weren't positioned with the same number of nodes per shard. Having a broad vision for data management is a key component to political success. But we also take pride in our execution strategy, when you start a new database from scratch as we did 15 years ago, you won't compete on features. Our key competitive points where speed and scale of analytics, we set a target of 100 x better query performance in traditional databases with path loads. Our storage architecture provides a solid foundation on which to build toward these goals. Every query starts with data retrieval, keeping data sorted, organized by column and compressed by using adaptive caching, to keep the data retrieval time in IO to the bare minimum theoretically required. We also keep the data close to where it will be processed, and you clusters the machines to increase throughput. We have partition pruning a robust optimizer evaluate active use segmentation as part of the physical database designed to keep records close to the other relevant records. So the solid foundation, but we also need optimal execution strategies and tactics. One execution strategy which we built for a long time, but it's still a source of pride, it's how we process expressions. Databases and other systems with general purpose expression evaluators, write a compound expression into a tree. Here I'm using A plus one times B as an example, during execution, if your CPU traverses the tree and compute sub-parts from the whole. Tree traversal often takes more compute cycles than the actual work to be done. Especially in evaluation is a very common operation, so something worth optimizing. One instinct that engineers have is to use what we call, just-in-time or JIT compilation, which means generating code form the CPU into the specific activity expression, and add them. This replaces the tree of boxes that are custom made box for the query. This approach has complexity bugs, but it can be made to work. It has other drawbacks though, it adds a lot to query setup time, especially for short queries. And it pretty much eliminate the ability of mere models, mere mortals to develop user defined functions. If you go back to the problem we're trying to solve, the source of the overhead is the tree traversal. If you increase the batch of records processed in each traversal step, this overhead is amortized until it becomes negligible. It's a perfect match for a columnar storage engine. This also sets the CPU up for efficiency. The CPUs look particularly good, at following the same small sequence of instructions in a tight loop. In some cases, the CPU may even be able to vectorize, and apply the same processing to multiple records to the same instruction. This approach is easy to implement and debug, user defined functions are possible, then generally aligned with the other complexities of implementing and improving a large system. More importantly, the performance, both in terms of query setup and record throughput is dramatically improved. You'll hear me say that we look at research and industry for inspiration. In this case, our findings in line with academic binding. If you'd like to read papers, I recommend everything you always wanted to know about compiled and vectorized queries, don't afraid to ask, so we did have this idea before we read that paper. However, not every decision we made in the Vertica executer that the test of time as well as the expression evaluator. For example, sorting and grouping aren't susceptible to vectorization because sort decisions interrupt the flow. We have used JIT compiling on that for years, and Vertica 401, and it provides modest setups, but we know we can do even better. But who we've embarked on a new design for execution engine, which I call EE five, because it's our best. It's really designed especially for the cloud, now I know what you're thinking, you're thinking, I just put up a slide with an old engine, a new engine, and a sleek play headed up into the clouds. But this isn't just marketing hype, here's what I mean, when I say we've learned lessons over the years, and then we're redesigning the executer for the cloud. And of course, you'll see that the new design works well on premises as well. These changes are just more important for the cloud. Starting with the network layer in the cloud, we can't count on all nodes being connected to the same switch. Multicast doesn't work like it does in a custom data center, so as I mentioned earlier, we're redesigning the network transfer layer for the cloud. Storage in the cloud is different, and I'm not referring here to the storage of persistent data, but to the storage of temporary data used only once during the course of query execution. Our new pattern is designed to take into account the strengths and weaknesses of cloud object storage, where we can't easily do a path. Moving on to memory, many of our access patterns are reasonably effective on bare metal machines, that aren't the best choice on cloud hyperbug that have overheads, page faults or big gap. Here again, we found we can improve performance, a bit on dedicated hardware, and even more in the cloud. Finally, and this is true in all environments, core counts have gone up. And not all of our algorithms take full advantage, there's a lot of ground to cover here. But I think sorting in the perfect example to illustrate these points, I mentioned that we use JIT in sorting. We're getting rid of JIT in favor of a data format that can be treated efficiently, independent of what the data types are. We've drawn on the best, most modern technology from academia and industry. We've got our own analysis and testing, you know what we chose, we chose parallel merge sort, anyone wants to take a guess when merge sort was invented. It was invented in 1948, or at least documented that way, like computing context. If you've heard me talk before, you know that I'm fascinated by how all the things I worked with as an engineer, were invented before I was born. And in Vertica , we don't use the newest technologies, we use the best ones. And what is noble about Vertica is the way we've combined the best ideas together into a cohesive package. So all kidding about the 1940s aside, or he redesigned is actually state of the art. How do we know the sort routine is state of the art? It turns out, there's a pretty credible benchmark or at the appropriately named historic sortbenchmark.org. Anyone with resources looking for fame for their product or academic paper can try to set the record. Record is last set in 2016 with Tencent Sort, 100 terabytes in 99 seconds. Setting the records it's hard, you have to come up with hundreds of machines on a dedicated high speed switching fabric. There's a lot to a distributed sort, there all have core sorting algorithms. The authors of the paper conveniently broke out of the time spent in their sort, 67 out of 99 seconds want to know local sorting. If we break this out, divided by two CPUs and each of 512 nodes, we find that each CPU so there's almost a gig and a half per second. This is for what's called an indy sort, like an Indy race car, is in general purpose. It only handles fixed hundred five records with 10 byte key. There is a record length can vary, then it's called daytona sort, a 10 set daytona sort, is a little slower. One point is 10 gigabytes per second per CPU, now for Verrtica, We have a wide variety ability in record sizes, and more interesting data types, but still no harm in setting us like phone numbers, comfortable to the world record. On my 2017 era AMD desktop CPU, the Vertica EE5 sort to store about two and a half gigabytes per second. Obviously, this test isn't apply to apples because they use their own open power chip. But the number of DRM channels is the same, so it's pretty close the number that says we've hit on the right approach. And it performs this way on premise, in the cloud, and we can adapt it to cloud temp space. So what's our roadmap for integrating EE5 into the product and compare replacing the query executed the database to replacing the crankshaft and other parts of the engine of a car while it's been driven. We've actually done it before, between Vertica three and a half and five, and then we never really stopped changing it, now we'll do it again. The first part in replacing with algorithm called storage merge, which combines sorted data from disk. The first time has was two that are in vertical in incoming 10.0 patch that will be EE5 or resegmented storage merge, and then convert sorting and grouping into do out. There the performance results so far, in cases where the Vertica execute is doing well today, simple environments with simple data patterns, such as this simple capitalistic query, there's a lot of speed up, when we ship the segmentation code, which didn't quite make the freeze as much like to bump longer term, what we do is grouping into the storage of large operations, we'll get to where we think we ought to be, given a theoretical minimum work the CPUs need to do. Now if we look at a case where the current execution isn't doing as well, we see there's a much stronger benefit to the code shipping in Vertica 10. In fact, it turns a chart bar sideways to try to help you see the difference better. This case also benefit from the improvements in 10 product point releases and beyond. They will not happening to the vertical query executer, That was just the taste. But now I'd like to switch to the roadmap first for our adapters layer. I'll start with a story about, how our storage access layer evolved. If you go back to the academic ideas, if you start paper that persuaded investors to fund Vertica, read optimized store was the part that had substantiation in the form of performance data. Much of the paper was speculative, but we tried to follow it anyway. That paper talked about the WS with RS, The rights are in the read store, and how they work together for transaction processing and how there was a supernova. In all honesty, Vertica engineers couldn't figure out from the paper what to do next, incase you want to try, and we asked them they would like, We never got enough clarification to build it that way. But here's what we built, instead. We built the ROS, read optimized store, introduction on steep major revision. It's sorted, ordered columnar and compressed that follows a table partitioning that worked even better than the we are as described in the paper. We also built the last byte optimized store, we built four versions of this over the years actually. But this was the best one, it's not a set of interrelated V tree. It's just an append only, insertion order remember your way here, am sorry, no compression, no base, no partitioning. There is, however, a tuple over which does what we call move out. Move the data from WOS to ROS, sorting and compressing. Let's take a moment to compare how they behave, when you load data directly to the ROS, there's a data parsing operation. Then we finished the sorting, and then compressing right out the columnar data files to stay storage. The next query through executes against the ROS and it runs as it should because the ROS is read optimized. Let's repeat the exercise for WOS, the load operation response before the sorting and compressing, and before the data is written to persistent storage. Now it's possible for a query to come along, and the query could be responsible for sorting the lost data in addition to its other processes. Effect on query isn't predictable until the TM comes along and writes the data to the ROS. Over the years, we've done a lot of comparisons between ROS and WOS. ROS has always been better for sustained load throughput, it achieves much higher records per second without pushing back against the client and hasn't Vertica for when we developed the first usable merge out algorithm. ROS has always been better for predictable query performance, the ROS has never had the same management complexity and limitations as WOS. You don't have to pick a memory size and figure out which transactions get to use the pool. A non persistent nature of ROS always cause headaches when there are unexpected cluster shutdowns. We also looked at field usage data, we found that few customers were using a lot, especially among those that studied the issue carefully. So how we set out on a mission to improve the ROS to the point where it was always better than both the WOS and the profit of the past. And now it's true, ROS is better than the WOS and the loss of a couple of years ago. We implemented storage bundling, better catalog object storage and better tuple mover merge outs. And now, after extensive Q&A and customer testing, we've now succeeded, and in Vertica 10, we've removed the whys. Let's talk for a moment about simplicity, one of the best things Mike Stonebreaker said is no knobs. Anyone want to guess how many knobs we got rid of, and we took the WOS out of the product. 22 were five knobs to control whether it didn't went to ROS as well. Six controlling the ROS itself, Six more to set policies for the typical remove out and so on. In my honest opinion is still wasn't enough control over to achieve excess in a multi tenant environment, the big reason to get rid of the WOS for simplicity. Make the lives of DBAs and users better, we have a long way to go, but we're doing it. On my desk, I keep a jar with the knob in it for each knob in Vertica. When developers add a knob to the product, they have to add a knob to the jar. When they remove a knob, they get to choose one to take out, We have a lot of work to do, but I'm thrilled to report that in 15 years 10 is the first release with a number of knobs ticked downward. Get back to the WOS, I've said the most important thing get rid of it for last. We're getting rid of it so we can deliver our vision of the future to our customer. Remember how he said an Eon and sub-clusters we got all these benefits from shared storage? Guess what can't live in shared storage, the WOS. Remember how it's been a big part of the future was keeping the copies that identical to the primary copy? Independent actions of the WOS took a little at the root of the divergence between copies of the data. You have to admit it when you're wrong. That was in the original design and held up to the a selling point of time, without onto the idea of a separate ROS and WOS for too long. In Vertica, 10, we can finally bid, good reagents. I've covered a lot of ground, so let's put all the pieces together. I've talked a lot about our vision and how we're achieving it. But we also still pay attention to tactical detail. We've been fine tuning our memory management model to enhance performance. That involves revisiting tens of thousands of satellite of code, much like painting the inside of a large building with small paintbrushes. We're getting results as shown in the chart in Vertica nine, concurrent monitoring queries use memory from the global catalog tool, and Vertica 10, they don't. This is only one example of an important detail we're improving. We've also reworked the monitoring tables without network messages into two parts. The increased data we're collecting and analyzing and our quality assurance processes, we're improving on everything. As the story goes, I still have my grandfather's axe, of course, my father had to replace the handle, and I had to replace the head. Along the same lines, we still have Mike Stonebreaker Vertica. We didn't replace the query optimizer twice the debate database designer and storage layer four times each. The query executed is and it's a free design, like charted out how our code has changed over the years. I found that we don't have much from a long time ago, I did some digging, and you know what we have left in 2007. We have the original curly braces, and a little bit of percent code for handling dates and times. To deliver on our mission to help customers get value from their structured data, with high performance of scale, and in diverse deployment environments. We have the sound architecture roadmap, reviews the best execution strategy and solid tactics. On the architectural front, we're converging in an enterprise, we're extending smart analytic clusters. In query processing, we're redesigning the execution engine for the cloud, as I've told you. There's a lot more than just the fast engine. that you want to learn about our new data support for complex data types, improvements to the query optimizer statistics, or extension to live aggregate projections and flatten tables. You should check out some of the other engineering talk that the big data conference. We continue to stay on top of the details from low level CPU and memory too, to the monitoring management, developing tighter feedback cycles between development, Q&A and customers. And don't forget to check out the rest of the pillars of our roadmap. We have new easier ways to get started with Vertica in the cloud. Engineers have been hard at work on machine learning and security. It's easier than ever to use Vertica with third Party product, as a variety of tools integrations continues to increase. Finally, the most important thing we can do, is to help people get value from structured data to help people learn more about Vertica. So hopefully I left plenty of time for Q&A at the end of this presentation. I hope to hear your questions soon.

>> Joy: Welcome to the Virtual Big Data Conference. Vertica is so excited to host this event. I'm Joy King, and I'll be your host for today's Big Data Conference Keynote Session. It's my honor and my genuine pleasure to lead Vertica's product and go-to-market strategy. And I'm so lucky to have a passionate and committed team who turned our Vertica BDC event, into a virtual event in a very short amount of time. I want to thank the thousands of people, and yes, that's our true number who have registered to attend this virtual event. We were determined to balance your health, safety and your peace of mind with the excitement of the Vertica BDC. This is a very unique event. Because as I hope you all know, we focus on engineering and architecture, best practice sharing and customer stories that will educate and inspire everyone. I also want to thank our top sponsors for the virtual BDC, Arrow, and Pure Storage. Our partnerships are so important to us and to everyone in the audience. Because together, we get things done faster and better. Now for today's keynote, you'll hear from three very important and energizing speakers. First, Colin Mahony, our SVP and General Manager for Vertica, will talk about the market trends that Vertica is betting on to win for our customers. And he'll share the exciting news about our Vertica 10 announcement and how this will benefit our customers. Then you'll hear from Amy Fowler, VP of strategy and solutions for FlashBlade at Pure Storage. Our partnership with Pure Storage is truly unique in the industry, because together modern infrastructure from Pure powers modern analytics from Vertica. And then you'll hear from John Yovanovich, Director of IT at AT&T, who will tell you about the Pure Vertica Symphony that plays live every day at AT&T. Here we go, Colin, over to you. >> Colin: Well, thanks a lot joy. And, I want to echo Joy's thanks to our sponsors, and so many of you who have helped make this happen. This is not an easy time for anyone. We were certainly looking forward to getting together in person in Boston during the Vertica Big Data Conference and Winning with Data. But I think all of you and our team have done a great job, scrambling and putting together a terrific virtual event. So really appreciate your time. I also want to remind people that we will make both the slides and the full recording available after this. So for any of those who weren't able to join live, that is still going to be available. Well, things have been pretty exciting here. And in the analytic space in general, certainly for Vertica, there's a lot happening. There are a lot of problems to solve, a lot of opportunities to make things better, and a lot of data that can really make every business stronger, more efficient, and frankly, more differentiated. For Vertica, though, we know that focusing on the challenges that we can directly address with our platform, and our people, and where we can actually make the biggest difference is where we ought to be putting our energy and our resources. I think one of the things that has made Vertica so strong over the years is our ability to focus on those areas where we can make a great difference. So for us as we look at the market, and we look at where we play, there are really three recent and some not so recent, but certainly picking up a lot of the market trends that have become critical for every industry that wants to Win Big With Data. We've heard this loud and clear from our customers and from the analysts that cover the market. If I were to summarize these three areas, this really is the core focus for us right now. We know that there's massive data growth. And if we can unify the data silos so that people can really take advantage of that data, we can make a huge difference. We know that public clouds offer tremendous advantages, but we also know that balance and flexibility is critical. And we all need the benefit that machine learning for all the types up to the end data science. We all need the benefits that they can bring to every single use case, but only if it can really be operationalized at scale, accurate and in real time. And the power of Vertica is, of course, how we're able to bring so many of these things together. Let me talk a little bit more about some of these trends. So one of the first industry trends that we've all been following probably now for over the last decade, is Hadoop and specifically HDFS. So many companies have invested, time, money, more importantly, people in leveraging the opportunity that HDFS brought to the market. HDFS is really part of a much broader storage disruption that we'll talk a little bit more about, more broadly than HDFS. But HDFS itself was really designed for petabytes of data, leveraging low cost commodity hardware and the ability to capture a wide variety of data formats, from a wide variety of data sources and applications. And I think what people really wanted, was to store that data before having to define exactly what structures they should go into. So over the last decade or so, the focus for most organizations is figuring out how to capture, store and frankly manage that data. And as a platform to do that, I think, Hadoop was pretty good. It certainly changed the way that a lot of enterprises think about their data and where it's locked up. In parallel with Hadoop, particularly over the last five years, Cloud Object Storage has also given every organization another option for collecting, storing and managing even more data. That has led to a huge growth in data storage, obviously, up on public clouds like Amazon and their S3, Google Cloud Storage and Azure Blob Storage just to name a few. And then when you consider regional and local object storage offered by cloud vendors all over the world, the explosion of that data, in leveraging this type of object storage is very real. And I think, as I mentioned, it's just part of this broader storage disruption that's been going on. But with all this growth in the data, in all these new places to put this data, every organization we talk to is facing even more challenges now around the data silo. Sure the data silos certainly getting bigger. And hopefully they're getting cheaper per bit. But as I said, the focus has really been on collecting, storing and managing the data. But between the new data lakes and many different cloud object storage combined with all sorts of data types from the complexity of managing all this, getting that business value has been very limited. This actually takes me to big bet number one for Team Vertica, which is to unify the data. Our goal, and some of the announcements we have made today plus roadmap announcements I'll share with you throughout this presentation. Our goal is to ensure that all the time, money and effort that has gone into storing that data, all the data turns into business value. So how are we going to do that? With a unified analytics platform that analyzes the data wherever it is HDFS, Cloud Object Storage, External tables in an any format ORC, Parquet, JSON, and of course, our own Native Roth Vertica format. Analyze the data in the right place in the right format, using a single unified tool. This is something that Vertica has always been committed to, and you'll see in some of our announcements today, we're just doubling down on that commitment. Let's talk a little bit more about the public cloud. This is certainly the second trend. It's the second wave maybe of data disruption with object storage. And there's a lot of advantages when it comes to public cloud. There's no question that the public clouds give rapid access to compute storage with the added benefit of eliminating data center maintenance that so many companies, want to get out of themselves. But maybe the biggest advantage that I see is the architectural innovation. The public clouds have introduced so many methodologies around how to provision quickly, separating compute and storage and really dialing-in the exact needs on demand, as you change workloads. When public clouds began, it made a lot of sense for the cloud providers and their customers to charge and pay for compute and storage in the ratio that each use case demanded. And I think you're seeing that trend, proliferate all over the place, not just up in public cloud. That architecture itself is really becoming the next generation architecture for on-premise data centers, as well. But there are a lot of concerns. I think we're all aware of them. They're out there many times for different workloads, there are higher costs. Especially if some of the workloads that are being run through analytics, which tend to run all the time. Just like some of the silo challenges that companies are facing with HDFS, data lakes and cloud storage, the public clouds have similar types of siloed challenges as well. Initially, there was a belief that they were cheaper than data centers, and when you added in all the costs, it looked that way. And again, for certain elastic workloads, that is the case. I don't think that's true across the board overall. Even to the point where a lot of the cloud vendors aren't just charging lower costs anymore. We hear from a lot of customers that they don't really want to tether themselves to any one cloud because of some of those uncertainties. Of course, security and privacy are a concern. We hear a lot of concerns with regards to cloud and even some SaaS vendors around shared data catalogs, across all the customers and not enough separation. But security concerns are out there, you can read about them. I'm not going to jump into that bandwagon. But we hear about them. And then, of course, I think one of the things we hear the most from our customers, is that each cloud stack is starting to feel even a lot more locked in than the traditional data warehouse appliance. And as everybody knows, the industry has been running away from appliances as fast as it can. And so they're not eager to get locked into another, quote, unquote, virtual appliance, if you will, up in the cloud. They really want to make sure they have flexibility in which clouds, they're going to today, tomorrow and in the future. And frankly, we hear from a lot of our customers that they're very interested in eventually mixing and matching, compute from one cloud with, say storage from another cloud, which I think is something that we'll hear a lot more about. And so for us, that's why we've got our big bet number two. we love the cloud. We love the public cloud. We love the private clouds on-premise, and other hosting providers. But our passion and commitment is for Vertica to be able to run in any of the clouds that our customers choose, and make it portable across those clouds. We have supported on-premises and all public clouds for years. And today, we have announced even more support for Vertica in Eon Mode, the deployment option that leverages the separation of compute from storage, with even more deployment choices, which I'm going to also touch more on as we go. So super excited about our big bet number two. And finally as I mentioned, for all the hype that there is around machine learning, I actually think that most importantly, this third trend that team Vertica is determined to address is the need to bring business critical, analytics, machine learning, data science projects into production. For so many years, there just wasn't enough data available to justify the investment in machine learning. Also, processing power was expensive, and storage was prohibitively expensive. But to train and score and evaluate all the different models to unlock the full power of predictive analytics was tough. Today you have those massive data volumes. You have the relatively cheap processing power and storage to make that dream a reality. And if you think about this, I mean with all the data that's available to every company, the real need is to operationalize the speed and the scale of machine learning so that these organizations can actually take advantage of it where they need to. I mean, we've seen this for years with Vertica, going back to some of the most advanced gaming companies in the early days, they were incorporating this with live data directly into their gaming experiences. Well, every organization wants to do that now. And the accuracy for clickability and real time actions are all key to separating the leaders from the rest of the pack in every industry when it comes to machine learning. But if you look at a lot of these projects, the reality is that there's a ton of buzz, there's a ton of hype spanning every acronym that you can imagine. But most companies are struggling, do the separate teams, different tools, silos and the limitation that many platforms are facing, driving, down sampling to get a small subset of the data, to try to create a model that then doesn't apply, or compromising accuracy and making it virtually impossible to replicate models, and understand decisions. And if there's one thing that we've learned when it comes to data, prescriptive data at the atomic level, being able to show end of one as we refer to it, meaning individually tailored data. No matter what it is healthcare, entertainment experiences, like gaming or other, being able to get at the granular data and make these decisions, make that scoring applies to machine learning just as much as it applies to giving somebody a next-best-offer. But the opportunity has never been greater. The need to integrate this end-to-end workflow and support the right tools without compromising on that accuracy. Think about it as no downsampling, using all the data, it really is key to machine learning success. Which should be no surprise then why the third big bet from Vertica is one that we've actually been working on for years. And we're so proud to be where we are today, helping the data disruptors across the world operationalize machine learning. This big bet has the potential to truly unlock, really the potential of machine learning. And today, we're announcing some very important new capabilities specifically focused on unifying the work being done by the data science community, with their preferred tools and platforms, and the volume of data and performance at scale, available in Vertica. Our strategy has been very consistent over the last several years. As I said in the beginning, we haven't deviated from our strategy. Of course, there's always things that we add. Most of the time, it's customer driven, it's based on what our customers are asking us to do. But I think we've also done a great job, not trying to be all things to all people. Especially as these hype cycles flare up around us, we absolutely love participating in these different areas without getting completely distracted. I mean, there's a variety of query tools and data warehouses and analytics platforms in the market. We all know that. There are tools and platforms that are offered by the public cloud vendors, by other vendors that support one or two specific clouds. There are appliance vendors, who I was referring to earlier who can deliver package data warehouse offerings for private data centers. And there's a ton of popular machine learning tools, languages and other kits. But Vertica is the only advanced analytic platform that can do all this, that can bring it together. We can analyze the data wherever it is, in HDFS, S3 Object Storage, or Vertica itself. Natively we support multiple clouds on-premise deployments, And maybe most importantly, we offer that choice of deployment modes to allow our customers to choose the architecture that works for them right now. It still also gives them the option to change move, evolve over time. And Vertica is the only analytics database with end-to-end machine learning that can truly operationalize ML at scale. And I know it's a mouthful. But it is not easy to do all these things. It is one of the things that highly differentiates Vertica from the rest of the pack. It is also why our customers, all of you continue to bet on us and see the value that we are delivering and we will continue to deliver. Here's a couple of examples of some of our customers who are powered by Vertica. It's the scale of data. It's the millisecond response times. Performance and scale have always been a huge part of what we have been about, not the only thing. I think the functionality all the capabilities that we add to the platform, the ease of use, the flexibility, obviously with the deployment. But if you look at some of the numbers they are under these customers on this slide. And I've shared a lot of different stories about these customers. Which, by the way, it still amaze me every time I talk to one and I get the updates, you can see the power and the difference that Vertica is making. Equally important, if you look at a lot of these customers, they are the epitome of being able to deploy Vertica in a lot of different environments. Many of the customers on this slide are not using Vertica just on-premise or just in the cloud. They're using it in a hybrid way. They're using it in multiple different clouds. And again, we've been with them on that journey throughout, which is what has made this product and frankly, our roadmap and our vision exactly what it is. It's been quite a journey. And that journey continues now with the Vertica 10 release. The Vertica 10 release is obviously a massive release for us. But if you look back, you can see that building on that native columnar architecture that started a long time ago, obviously, with the C-Store paper. We built it to leverage that commodity hardware, because it was an architecture that was never tightly integrated with any specific underlying infrastructure. I still remember hearing the initial pitch from Mike Stonebreaker, about the vision of Vertica as a software only solution and the importance of separating the company from hardware innovation. And at the time, Mike basically said to me, "there's so much R&D in innovation that's going to happen in hardware, we shouldn't bake hardware into our solution. We should do it in software, and we'll be able to take advantage of that hardware." And that is exactly what has happened. But one of the most recent innovations that we embraced with hardware is certainly that separation of compute and storage. As I said previously, the public cloud providers offered this next generation architecture, really to ensure that they can provide the customers exactly what they needed, more compute or more storage and charge for each, respectively. The separation of compute and storage, compute from storage is a major milestone in data center architectures. If you think about it, it's really not only a public cloud innovation, though. It fundamentally redefines the next generation data architecture for on-premise and for pretty much every way people are thinking about computing today. And that goes for software too. Object storage is an example of the cost effective means for storing data. And even more importantly, separating compute from storage for analytic workloads has a lot of advantages. Including the opportunity to manage much more dynamic, flexible workloads. And more importantly, truly isolate those workloads from others. And by the way, once you start having something that can truly isolate workloads, then you can have the conversations around autonomic computing, around setting up some nodes, some compute resources on the data that won't affect any of the other data to do some things on their own, maybe some self analytics, by the system, etc. A lot of things that many of you know we've already been exploring in terms of our own system data in the product. But it was May 2018, believe it or not, it seems like a long time ago where we first announced Eon Mode and I want to make something very clear, actually about Eon mode. It's a mode, it's a deployment option for Vertica customers. And I think this is another huge benefit that we don't talk about enough. But unlike a lot of vendors in the market who will dig you and charge you for every single add-on like hit-buy, you name it. You get this with the Vertica product. If you continue to pay support and maintenance, this comes with the upgrade. This comes as part of the new release. So any customer who owns or buys Vertica has the ability to set up either an Enterprise Mode or Eon Mode, which is a question I know that comes up sometimes. Our first announcement of Eon was obviously AWS customers, including the trade desk, AT&T. Most of whom will be speaking here later at the Virtual Big Data Conference. They saw a huge opportunity. Eon Mode, not only allowed Vertica to scale elastically with that specific compute and storage that was needed, but it really dramatically simplified database operations including things like workload balancing, node recovery, compute provisioning, etc. So one of the most popular functions is that ability to isolate the workloads and really allocate those resources without negatively affecting others. And even though traditional data warehouses, including Vertica Enterprise Mode have been able to do lots of different workload isolation, it's never been as strong as Eon Mode. Well, it certainly didn't take long for our customers to see that value across the board with Eon Mode. Not just up in the cloud, in partnership with one of our most valued partners and a platinum sponsor here. Joy mentioned at the beginning. We announced Vertica Eon Mode for Pure Storage FlashBlade in September 2019. And again, just to be clear, this is not a new product, it's one Vertica with yet more deployment options. With Pure Storage, Vertica in Eon mode is not limited in any way by variable cloud, network latency. The performance is actually amazing when you take the benefits of separate and compute from storage and you run it with a Pure environment on-premise. Vertica in Eon Mode has a super smart cache layer that we call the depot. It's a big part of our secret sauce around Eon mode. And combined with the power and performance of Pure's FlashBlade, Vertica became the industry's first advanced analytics platform that actually separates compute and storage for on-premises data centers. Something that a lot of our customers are already benefiting from, and we're super excited about it. But as I said, this is a journey. We don't stop, we're not going to stop. Our customers need the flexibility of multiple public clouds. So today with Vertica 10, we're super proud and excited to announce support for Vertica in Eon Mode on Google Cloud. This gives our customers the ability to use their Vertica licenses on Amazon AWS, on-premise with Pure Storage and on Google Cloud. Now, we were talking about HDFS and a lot of our customers who have invested quite a bit in HDFS as a place, especially to store data have been pushing us to support Eon Mode with HDFS. So as part of Vertica 10, we are also announcing support for Vertica in Eon Mode using HDFS as the communal storage. Vertica's own Roth format data can be stored in HDFS, and actually the full functionality of Vertica is complete analytics, geospatial pattern matching, time series, machine learning, everything that we have in there can be applied to this data. And on the same HDFS nodes, Vertica can actually also analyze data in ORC or Parquet format, using External tables. We can also execute joins between the Roth data the External table holds, which powers a much more comprehensive view. So again, it's that flexibility to be able to support our customers, wherever they need us to support them on whatever platform, they have. Vertica 10 gives us a lot more ways that we can deploy Eon Mode in various environments for our customers. It allows them to take advantage of Vertica in Eon Mode and the power that it brings with that separation, with that workload isolation, to whichever platform they are most comfortable with. Now, there's a lot that has come in Vertica 10. I'm definitely not going to be able to cover everything. But we also introduced complex types as an example. And complex data types fit very well into Eon as well in this separation. They significantly reduce the data pipeline, the cost of moving data between those, a much better support for unstructured data, which a lot of our customers have mixed with structured data, of course, and they leverage a lot of columnar execution that Vertica provides. So you get complex data types in Vertica now, a lot more data, stronger performance. It goes great with the announcement that we made with the broader Eon Mode. Let's talk a little bit more about machine learning. We've been actually doing work in and around machine learning with various extra regressions and a whole bunch of other algorithms for several years. We saw the huge advantage that MPP offered, not just as a sequel engine as a database, but for ML as well. Didn't take as long to realize that there's a lot more to operationalizing machine learning than just those algorithms. It's data preparation, it's that model trade training. It's the scoring, the shaping, the evaluation. That is so much of what machine learning and frankly, data science is about. You do know, everybody always wants to jump to the sexy algorithm and we handle those tasks very, very well. It makes Vertica a terrific platform to do that. A lot of work in data science and machine learning is done in other tools. I had mentioned that there's just so many tools out there. We want people to be able to take advantage of all that. We never believed we were going to be the best algorithm company or come up with the best models for people to use. So with Vertica 10, we support PMML. We can import now and export PMML models. It's a huge step for us around that operationalizing machine learning projects for our customers. Allowing the models to get built outside of Vertica yet be imported in and then applying to that full scale of data with all the performance that you would expect from Vertica. We also are more tightly integrating with Python. As many of you know, we've been doing a lot of open source projects with the community driven by many of our customers, like Uber. And so now with Python we've integrated with TensorFlow, allowing data scientists to build models in their preferred language, to take advantage of TensorFlow. But again, to store and deploy those models at scale with Vertica. I think both these announcements are proof of our big bet number three, and really our commitment to supporting innovation throughout the community by operationalizing ML with that accuracy, performance and scale of Vertica for our customers. Again, there's a lot of steps when it comes to the workflow of machine learning. These are some of them that you can see on the slide, and it's definitely not linear either. We see this as a circle. And companies that do it, well just continue to learn, they continue to rescore, they continue to redeploy and they want to operationalize all that within a single platform that can take advantage of all those capabilities. And that is the platform, with a very robust ecosystem that Vertica has always been committed to as an organization and will continue to be. This graphic, many of you have seen it evolve over the years. Frankly, if we put everything and everyone on here wouldn't fit on a slide. But it will absolutely continue to evolve and grow as we support our customers, where they need the support most. So, again, being able to deploy everywhere, being able to take advantage of Vertica, not just as a business analyst or a business user, but as a data scientists or as an operational or BI person. We want Vertica to be leveraged and used by the broader organization. So I think it's fair to say and I encourage everybody to learn more about Vertica 10, because I'm just highlighting some of the bigger aspects of it. But we talked about those three market trends. The need to unify the silos, the need for hybrid multiple cloud deployment options, the need to operationalize business critical machine learning projects. Vertica 10 has absolutely delivered on those. But again, we are not going to stop. It is our job not to, and this is how Team Vertica thrives. I always joke that the next release is the best release. And, of course, even after Vertica 10, that is also true, although Vertica 10 is pretty awesome. But, you know, from the first line of code, we've always been focused on performance and scale, right. And like any really strong data platform, the execution engine, the optimizer and the execution engine are the two core pieces of that. Beyond Vertica 10, some of the big things that we're already working on, next generation execution engine. We're already actually seeing incredible early performance from this. And this is just one example, of how important it is for an organization like Vertica to constantly go back and re-innovate. Every single release, we do the sit ups and crunches, our performance and scale. How do we improve? And there's so many parts of the core server, there's so many parts of our broader ecosystem. We are constantly looking at coverages of how we can go back to all the code lines that we have, and make them better in the current environment. And it's not an easy thing to do when you're doing that, and you're also expanding in the environment that we are expanding into to take advantage of the different deployments, which is a great segue to this slide. Because if you think about today, we're obviously already available with Eon Mode and Amazon, AWS and Pure and actually MinIO as well. As I talked about in Vertica 10 we're adding Google and HDFS. And coming next, obviously, Microsoft Azure, Alibaba cloud. So being able to expand into more of these environments is really important for the Vertica team and how we go forward. And it's not just running in these clouds, for us, we want it to be a SaaS like experience in all these clouds. We want you to be able to deploy Vertica in 15 minutes or less on these clouds. You can also consume Vertica, in a lot of different ways, on these clouds. As an example, in Amazon Vertica by the Hour. So for us, it's not just about running, it's about taking advantage of the ecosystems that all these cloud providers offer, and really optimizing the Vertica experience as part of them. Optimization, around automation, around self service capabilities, extending our management console, we now have products that like the Vertica Advisor Tool that our Customer Success Team has created to actually use our own smarts in Vertica. To take data from customers that give it to us and help them tune automatically their environment. You can imagine that we're taking that to the next level, in a lot of different endeavors that we're doing around how Vertica as a product can actually be smarter because we all know that simplicity is key. There just aren't enough people in the world who are good at managing data and taking it to the next level. And of course, other things that we all hear about, whether it's Kubernetes and containerization. You can imagine that that probably works very well with the Eon Mode and separating compute and storage. But innovation happens everywhere. We innovate around our community documentation. Many of you have taken advantage of the Vertica Academy. The numbers there are through the roof in terms of the number of people coming in and certifying on it. So there's a lot of things that are within the core products. There's a lot of activity and action beyond the core products that we're taking advantage of. And let's not forget why we're here, right? It's easy to talk about a platform, a data platform, it's easy to jump into all the functionality, the analytics, the flexibility, how we can offer it. But at the end of the day, somebody, a person, she's got to take advantage of this data, she's got to be able to take this data and use this information to make a critical business decision. And that doesn't happen unless we explore lots of different and frankly, new ways to get that predictive analytics UI and interface beyond just the standard BI tools in front of her at the right time. And so there's a lot of activity, I'll tease you with that going on in this organization right now about how we can do that and deliver that for our customers. We're in a great position to be able to see exactly how this data is consumed and used and start with this core platform that we have to go out. Look, I know, the plan wasn't to do this as a virtual BDC. But I really appreciate you tuning in. Really appreciate your support. I think if there's any silver lining to us, maybe not being able to do this in person, it's the fact that the reach has actually gone significantly higher than what we would have been able to do in person in Boston. We're certainly looking forward to doing a Big Data Conference in the future. But if I could leave you with anything, know this, since that first release for Vertica, and our very first customers, we have been very consistent. We respect all the innovation around us, whether it's open source or not. We understand the market trends. We embrace those new ideas and technologies and for us true north, and the most important thing is what does our customer need to do? What problem are they trying to solve? And how do we use the advantages that we have without disrupting our customers? But knowing that you depend on us to deliver that unified analytics strategy, it will deliver that performance of scale, not only today, but tomorrow and for years to come. We've added a lot of great features to Vertica. I think we've said no to a lot of things, frankly, that we just knew we wouldn't be the best company to deliver. When we say we're going to do things we do them. Vertica 10 is a perfect example of so many of those things that we from you, our customers have heard loud and clear, and we have delivered. I am incredibly proud of this team across the board. I think the culture of Vertica, a customer first culture, jumping in to help our customers win no matter what is also something that sets us massively apart. I hear horror stories about support experiences with other organizations. And people always seem to be amazed at Team Vertica's willingness to jump in or their aptitude for certain technical capabilities or understanding the business. And I think sometimes we take that for granted. But that is the team that we have as Team Vertica. We are incredibly excited about Vertica 10. I think you're going to love the Virtual Big Data Conference this year. I encourage you to tune in. Maybe one other benefit is I know some people were worried about not being able to see different sessions because they were going to overlap with each other well now, even if you can't do it live, you'll be able to do those sessions on demand. Please enjoy the Vertica Big Data Conference here in 2020. Please you and your families and your co-workers be safe during these times. I know we will get through it. And analytics is probably going to help with a lot of that and we already know it is helping in many different ways. So believe in the data, believe in data's ability to change the world for the better. And thank you for your time. And with that, I am delighted to now introduce Micro Focus CEO Stephen Murdoch to the Vertica Big Data Virtual Conference. Thank you Stephen. >> Stephen: Hi, everyone, my name is Stephen Murdoch. I have the pleasure and privilege of being the Chief Executive Officer here at Micro Focus. Please let me add my welcome to the Big Data Conference. And also my thanks for your support, as we've had to pivot to this being virtual rather than a physical conference. Its amazing how quickly we all reset to a new normal. I certainly didn't expect to be addressing you from my study. Vertica is an incredibly important part of Micro Focus family. Is key to our goal of trying to enable and help customers become much more data driven across all of their IT operations. Vertica 10 is a huge step forward, we believe. It allows for multi-cloud innovation, genuinely hybrid deployments, begin to leverage machine learning properly in the enterprise, and also allows the opportunity to unify currently siloed lakes of information. We operate in a very noisy, very competitive market, and there are people, who are in that market who can do some of those things. The reason we are so excited about Vertica is we genuinely believe that we are the best at doing all of those things. And that's why we've announced publicly, you're under executing internally, incremental investment into Vertica. That investments targeted at accelerating the roadmaps that already exist. And getting that innovation into your hands faster. This idea is speed is key. It's not a question of if companies have to become data driven organizations, it's a question of when. So that speed now is really important. And that's why we believe that the Big Data Conference gives a great opportunity for you to accelerate your own plans. You will have the opportunity to talk to some of our best architects, some of the best development brains that we have. But more importantly, you'll also get to hear from some of our phenomenal Roth Data customers. You'll hear from Uber, from the Trade Desk, from Philips, and from AT&T, as well as many many others. And just hearing how those customers are using the power of Vertica to accelerate their own, I think is the highlight. And I encourage you to use this opportunity to its full. Let me close by, again saying thank you, we genuinely hope that you get as much from this virtual conference as you could have from a physical conference. And we look forward to your engagement, and we look forward to hearing your feedback. With that, thank you very much. >> Joy: Thank you so much, Stephen, for joining us for the Vertica Big Data Conference. Your support and enthusiasm for Vertica is so clear, and it makes a big difference. Now, I'm delighted to introduce Amy Fowler, the VP of Strategy and Solutions for FlashBlade at Pure Storage, who was one of our BDC Platinum Sponsors, and one of our most valued partners. It was a proud moment for me, when we announced Vertica in Eon mode for Pure Storage FlashBlade and we became the first analytics data warehouse that separates compute from storage for on-premise data centers. Thank you so much, Amy, for joining us. Let's get started. >> Amy: Well, thank you, Joy so much for having us. And thank you all for joining us today, virtually, as we may all be. So, as we just heard from Colin Mahony, there are some really interesting trends that are happening right now in the big data analytics market. From the end of the Hadoop hype cycle, to the new cloud reality, and even the opportunity to help the many data science and machine learning projects move from labs to production. So let's talk about these trends in the context of infrastructure. And in particular, look at why a modern storage platform is relevant as organizations take on the challenges and opportunities associated with these trends. The answer is the Hadoop hype cycles left a lot of data in HDFS data lakes, or reservoirs or swamps depending upon the level of the data hygiene. But without the ability to get the value that was promised from Hadoop as a platform rather than a distributed file store. And when we combine that data with the massive volume of data in Cloud Object Storage, we find ourselves with a lot of data and a lot of silos, but without a way to unify that data and find value in it. Now when you look at the infrastructure data lakes are traditionally built on, it is often direct attached storage or data. The approach that Hadoop took when it entered the market was primarily bound by the limits of networking and storage technologies. One gig ethernet and slower spinning disk. But today, those barriers do not exist. And all FlashStorage has fundamentally transformed how data is accessed, managed and leveraged. The need for local data storage for significant volumes of data has been largely mitigated by the performance increases afforded by all Flash. At the same time, organizations can achieve superior economies of scale with that segregation of compute and storage. With compute and storage, you don't always scale in lockstep. Would you want to add an engine to the train every time you add another boxcar? Probably not. But from a Pure Storage perspective, FlashBlade is uniquely architected to allow customers to achieve better resource utilization for compute and storage, while at the same time, reducing complexity that has arisen from the siloed nature of the original big data solutions. The second and equally important recent trend we see is something I'll call cloud reality. The public clouds made a lot of promises and some of those promises were delivered. But cloud economics, especially usage based and elastic scaling, without the control that many companies need to manage the financial impact is causing a lot of issues. In addition, the risk of vendor lock-in from data egress, charges, to integrated software stacks that can't be moved or deployed on-premise is causing a lot of organizations to back off the all the way non-cloud strategy, and move toward hybrid deployments. Which is kind of funny in a way because it wasn't that long ago that there was a lot of talk about no more data centers. And for example, one large retailer, I won't name them, but I'll admit they are my favorites. They several years ago told us they were completely done with on-prem storage infrastructure, because they were going 100% to the cloud. But they just deployed FlashBlade for their data pipelines, because they need predictable performance at scale. And the all cloud TCO just didn't add up. Now, that being said, well, there are certainly challenges with the public cloud. It has also brought some things to the table that we see most organizations wanting. First of all, in a lot of cases applications have been built to leverage object storage platforms like S3. So they need that object protocol, but they may also need it to be fast. And the said object may be oxymoron only a few years ago, and this is an area of the market where Pure and FlashBlade have really taken a leadership position. Second, regardless of where the data is physically stored, organizations want the best elements of a cloud experience. And for us, that means two main things. Number one is simplicity and ease of use. If you need a bunch of storage experts to run the system, that should be considered a bug. The other big one is the consumption model. The ability to pay for what you need when you need it, and seamlessly grow your environment over time totally nondestructively. This is actually pretty huge and something that a lot of vendors try to solve for with finance programs. But no finance program can address the pain of a forklift upgrade, when you need to move to next gen hardware. To scale nondestructively over long periods of time, five to 10 years plus is a crucial architectural decisions need to be made at the outset. Plus, you need the ability to pay as you use it. And we offer something for FlashBlade called Pure as a Service, which delivers exactly that. The third cloud characteristic that many organizations want is the option for hybrid. Even if that is just a DR site in the cloud. In our case, that means supporting appplication of S3, at the AWS. And the final trend, which to me represents the biggest opportunity for all of us, is the need to help the many data science and machine learning projects move from labs to production. This means bringing all the machine learning functions and model training to the data, rather than moving samples or segments of data to separate platforms. As we all know, machine learning needs a ton of data for accuracy. And there is just too much data to retrieve from the cloud for every training job. At the same time, predictive analytics without accuracy is not going to deliver the business advantage that everyone is seeking. You can kind of visualize data analytics as it is traditionally deployed as being on a continuum. With that thing, we've been doing the longest, data warehousing on one end, and AI on the other end. But the way this manifests in most environments is a series of silos that get built up. So data is duplicated across all kinds of bespoke analytics and AI, environments and infrastructure. This creates an expensive and complex environment. So historically, there was no other way to do it because some level of performance is always table stakes. And each of these parts of the data pipeline has a different workload profile. A single platform to deliver on the multi dimensional performances, diverse set of applications required, that didn't exist three years ago. And that's why the application vendors pointed you towards bespoke things like DAS environments that we talked about earlier. And the fact that better options exists today is why we're seeing them move towards supporting this disaggregation of compute and storage. And when it comes to a platform that is a better option, one with a modern architecture that can address the diverse performance requirements of this continuum, and allow organizations to bring a model to the data instead of creating separate silos. That's exactly what FlashBlade is built for. Small files, large files, high throughput, low latency and scale to petabytes in a single namespace. And this is importantly a single rapid space is what we're focused on delivering for our customers. At Pure, we talk about it in the context of modern data experience because at the end of the day, that's what it's really all about. The experience for your teams in your organization. And together Pure Storage and Vertica have delivered that experience to a wide range of customers. From a SaaS analytics company, which uses Vertica on FlashBlade to authenticate the quality of digital media in real time, to a multinational car company, which uses Vertica on FlashBlade to make thousands of decisions per second for autonomous cars, or a healthcare organization, which uses Vertica on FlashBlade to enable healthcare providers to make real time decisions that impact lives. And I'm sure you're all looking forward to hearing from John Yavanovich from AT&T. To hear how he's been doing this with Vertica and FlashBlade as well. He's coming up soon. We have been really excited to build this partnership with Vertica. And we're proud to provide the only on-premise storage platform validated with Vertica Eon Mode. And deliver this modern data experience to our customers together. Thank you all so much for joining us today. >> Joy: Amy, thank you so much for your time and your insights. Modern infrastructure is key to modern analytics, especially as organizations leverage next generation data center architectures, and object storage for their on-premise data centers. Now, I'm delighted to introduce our last speaker in our Vertica Big Data Conference Keynote, John Yovanovich, Director of IT for AT&T. Vertica is so proud to serve AT&T, and especially proud of the harmonious impact we are having in partnership with Pure Storage. John, welcome to the Virtual Vertica BDC. >> John: Thank you joy. It's a pleasure to be here. And I'm excited to go through this presentation today. And in a unique fashion today 'cause as I was thinking through how I wanted to present the partnership that we have formed together between Pure Storage, Vertica and AT&T, I want to emphasize how well we all work together and how these three components have really driven home, my desire for a harmonious to use your word relationship. So, I'm going to move forward here and with. So here, what I'm going to do the theme of today's presentation is the Pure Vertica Symphony live at AT&T. And if anybody is a Westworld fan, you can appreciate the sheet music on the right hand side. What we're going to what I'm going to highlight here is in a musical fashion, is how we at AT&T leverage these technologies to save money to deliver a more efficient platform, and to actually just to make our customers happier overall. So as we look back, and back as early as just maybe a few years ago here at AT&T, I realized that we had many musicians to help the company. Or maybe you might want to call them data scientists, or data analysts. For the theme we'll stay with musicians. None of them were singing or playing from the same hymn book or sheet music. And so what we had was many organizations chasing a similar dream, but not exactly the same dream. And, best way to describe that is and I think with a lot of people this might resonate in your organizations. How many organizations are chasing a customer 360 view in your company? Well, I can tell you that I have at least four in my company. And I'm sure there are many that I don't know of. That is our problem because what we see is a repetitive sourcing of data. We see a repetitive copying of data. And there's just so much money to be spent. This is where I asked Pure Storage and Vertica to help me solve that problem with their technologies. What I also noticed was that there was no coordination between these departments. In fact, if you look here, nobody really wants to play with finance. Sales, marketing and care, sure that you all copied each other's data. But they actually didn't communicate with each other as they were copying the data. So the data became replicated and out of sync. This is a challenge throughout, not just my company, but all companies across the world. And that is, the more we replicate the data, the more problems we have at chasing or conquering the goal of single version of truth. In fact, I kid that I think that AT&T, we actually have adopted the multiple versions of truth, techno theory, which is not where we want to be, but this is where we are. But we are conquering that with the synergies between Pure Storage and Vertica. This is what it leaves us with. And this is where we are challenged and that if each one of our siloed business units had their own stories, their own dedicated stories, and some of them had more money than others so they bought more storage. Some of them anticipating storing more data, and then they really did. Others are running out of space, but can't put anymore because their bodies aren't been replenished. So if you look at it from this side view here, we have a limited amount of compute or fixed compute dedicated to each one of these silos. And that's because of the, wanting to own your own. And the other part is that you are limited or wasting space, depending on where you are in the organization. So there were the synergies aren't just about the data, but actually the compute and the storage. And I wanted to tackle that challenge as well. So I was tackling the data. I was tackling the storage, and I was tackling the compute all at the same time. So my ask across the company was can we just please play together okay. And to do that, I knew that I wasn't going to tackle this by getting everybody in the same room and getting them to agree that we needed one account table, because they will argue about whose account table is the best account table. But I knew that if I brought the account tables together, they would soon see that they had so much redundancy that I can now start retiring data sources. I also knew that if I brought all the compute together, that they would all be happy. But I didn't want them to tackle across tackle each other. And in fact that was one of the things that all business units really enjoy. Is they enjoy the silo of having their own compute, and more or less being able to control their own destiny. Well, Vertica's subclustering allows just that. And this is exactly what I was hoping for, and I'm glad they've brought through. And finally, how did I solve the problem of the single account table? Well when you don't have dedicated storage, and you can separate compute and storage as Vertica in Eon Mode does. And we store the data on FlashBlades, which you see on the left and right hand side, of our container, which I can describe in a moment. Okay, so what we have here, is we have a container full of compute with all the Vertica nodes sitting in the middle. Two loader, we'll call them loader subclusters, sitting on the sides, which are dedicated to just putting data onto the FlashBlades, which is sitting on both ends of the container. Now today, I have two dedicated storage or common dedicated might not be the right word, but two storage racks one on the left one on the right. And I treat them as separate storage racks. They could be one, but i created them separately for disaster recovery purposes, lashing work in case that rack were to go down. But that being said, there's no reason why I'm probably going to add a couple of them here in the future. So I can just have a, say five to 10, petabyte storage, setup, and I'll have my DR in another 'cause the DR shouldn't be in the same container. Okay, but I'll DR outside of this container. So I got them all together, I leveraged subclustering, I leveraged separate and compute. I was able to convince many of my clients that they didn't need their own account table, that they were better off having one. I eliminated, I reduced latency, I reduced our ticketing I reduce our data quality issues AKA ticketing okay. I was able to expand. What is this? As work. I was able to leverage elasticity within this cluster. As you can see, there are racks and racks of compute. We set up what we'll call the fixed capacity that each of the business units needed. And then I'm able to ramp up and release the compute that's necessary for each one of my clients based on their workloads throughout the day. And so while they compute to the right before you see that the instruments have already like, more or less, dedicated themselves towards all those are free for anybody to use. So in essence, what I have, is I have a concert hall with a lot of seats available. So if I want to run a 10 chair Symphony or 80, chairs, Symphony, I'm able to do that. And all the while, I can also do the same with my loader nodes. I can expand my loader nodes, to actually have their own Symphony or write all to themselves and not compete with any other workloads of the other clusters. What does that change for our organization? Well, it really changes the way our database administrators actually do their jobs. This has been a big transformation for them. They have actually become data conductors. Maybe you might even call them composers, which is interesting, because what I've asked them to do is morph into less technology and more workload analysis. And in doing so we're able to write auto-detect scripts, that watch the queues, watch the workloads so that we can help ramp up and trim down the cluster and subclusters as necessary. There has been an exciting transformation for our DBAs, who I need to now classify as something maybe like DCAs. I don't know, I have to work with HR on that. But I think it's an exciting future for their careers. And if we bring it all together, If we bring it all together, and then our clusters, start looking like this. Where everything is moving in harmonious, we have lots of seats open for extra musicians. And we are able to emulate a cloud experience on-prem. And so, I want you to sit back and enjoy the Pure Vertica Symphony live at AT&T. (soft music) >> Joy: Thank you so much, John, for an informative and very creative look at the benefits that AT&T is getting from its Pure Vertica symphony. I do really like the idea of engaging HR to change the title to Data Conductor. That's fantastic. I've always believed that music brings people together. And now it's clear that analytics at AT&T is part of that musical advantage. So, now it's time for a short break. And we'll be back for our breakout sessions, beginning at 12 pm Eastern Daylight Time. We have some really exciting sessions planned later today. And then again, as you can see on Wednesday. Now because all of you are already logged in and listening to this keynote, you already know the steps to continue to participate in the sessions that are listed here and on the previous slide. In addition, everyone received an email yesterday, today, and you'll get another one tomorrow, outlining the simple steps to register, login and choose your session. If you have any questions, check out the emails or go to www.vertica.com/bdc2020 for the logistics information. There are a lot of choices and that's always a good thing. Don't worry if you want to attend one or more or can't listen to these live sessions due to your timezone. All the sessions, including the Q&A sections will be available on demand and everyone will have access to the recordings as well as even more pre-recorded sessions that we'll post to the BDC website. Now I do want to leave you with two other important sites. First, our Vertica Academy. Vertica Academy is available to everyone. And there's a variety of very technical, self-paced, on-demand training, virtual instructor-led workshops, and Vertica Essentials Certification. And it's all free. Because we believe that Vertica expertise, helps everyone accelerate their Vertica projects and the advantage that those projects deliver. Now, if you have questions or want to engage with our Vertica engineering team now, we're waiting for you on the Vertica forum. We'll answer any questions or discuss any ideas that you might have. Thank you again for joining the Vertica Big Data Conference Keynote Session. Enjoy the rest of the BDC because there's a lot more to come