>>This is Dave Violante. We're here at a W s with the Keep talking About Storage palate cars. Here is the director of product management for E B s Elastic block storage. Welcome. Good to see again. >>Nice to see it. If >>so, let's talk about E b s. You know, it all started with us. Three and course customers demand Maur. What do we need to know about E b s? Like, what are the options that you provide? Give us the late low down. >>Yeah. So the way to think about block storage in the AWS eight abreast constructors. Really two kinds of offerings. One is around instant storage, which is a form of block strategy. And then you have a block started service, which is E. B s Andi. Sort of. The key thing they're from customer standpoint of different shit between the two is if you warn your storage like cycle to be coincident with your instance like cycle, then you use instant surgeon. That's why we see a lot of our customers using since storage, because they won't want that experience if you want. On the other hand, it's storage life cycle that's different from your instance life cycle. So the ability to change instances, the ability to grow size is the ability to to take back ups. Then you want to choose the obvious experience. And there we have a series of volume types that customers can consume. Be a GP two we have, I want. We have our stream volumes, which are a C one and C one. >>So she's when you talk to customers of block stores. What did they tell you that they most care about? >>Yeah, uh, it is. It is a Lord around performance. It is a lot around. Availability is a lot on your ability. He's a fuse. Those of the core characteristics that that customers care about earlier this year as an example, one of the things that we launched for customers was the ability to encrypt their volumes by default on you. Say, Well, why is that important? So security becomes a big concern for customers a day as they think about their environment and with encryption by default. We just made it simple. With a single setting, you can now, at an account level, ensure that all your PBS volumes created from that point on our fully encrypted. >>Okay, let's talk about snapshots. So how o r r. Snapshots in the cloud? Different. And how are your customers using stamps? >>Yeah, that's great. Great. Great. Cigarette in tow. Common conversation. Customers who are coming from on premises environment are used to snapshots is being sort of this copy on right type attack volumes. The way to think about aws snapshot. Devious snapshots in particular are really to think of them as backup. And so that is the one sort of key thing that I always tell customers is to think of what we call snapshots, really as backups. Especially if you're coming from a non premises environment. >>Okay, um, how about things you're doing to really improve? Uh, EBS snapshots. I mean, is it more performance? Is it making simple Are expanding use cases. Yeah. >>Yeah. Let's talk about the use case scenario Is that that snapshots get use, and snapshots are really the underlying storage for water called Amazon machine images. Our aim eyes. That is how snaps that is, how our instances boot. That is also the way that customers create CBS Williams from, so you can create an obvious volume from a snapshot. So on that on that particular use case, one of the things that we're we're now launching is a capability via calling far snapshot restored. So you can now take a knee, be a snapshot and then within an availability is soon. Make it such that you can. You can now launch volumes from it without encountering any Leighton sing and back on DDE. That we think is a tremendously powerful capability for customs. Because if you can, it takes away all the undifferentiated heavy lifting that they had to do in order to lure the data from the snapshot into the volume completely out of the picture and allows them to focus on getting their data to their applications. That's right. >>All right, we'll give you the last word. Final thoughts on the innovations that you had. Congratulations on all the hard work. >>No, actually, this is the team has done a tremendous amount of work in art launches. Couldn't be happier to see this in the hands of customers. We look forward to seeing what they build from from the things that we provided them so excited to see that happen. >>That's actually quite amazing. It started all very simple with us three. And now we've seen service is just become more granular. Higher performance. Really meeting customer demands. She's thanks so much. Thank you so much. All right. Thanks for watching. Your body will be back right after this short break.

>> live from San Francisco, California It's the Q covering the IBM Chief Data Officer Summit brought to you by IBM. >> We're back. You're watching the Cube, the leader in life Tech coverage. My name is Dave Volant Day, and we're covering the IBM Chief Data officer event hashtag IBM CDO is the 10th year that IBM has been running This event on the New Cube has been covering this for the last I'd say four or five years. Beth rottenness here. She's the distinguished engineer and principal data scientist. Cognitive within GTS Large Service's organization within IBM. Bet thanks so much for coming on the Cube. >> Absolutely. Thank you for having me. >> So you're very welcome. So really interesting sort of title. I'm inferring a lot. Um, and you're sexually transforming lives through data and analytics. Talk about your role a little bit. >> So my role is to infuse workforce transformation with cognitive. I typically we go from I think you've heard the ladder to a I. But as we move up that ladder and we can actually >> apply artificial intelligence and NLP, which is a lot of what I'm doing, >> it is it's instrumental in being able to see human beings in a lot more dimensions. So when we classify humans by a particular job role skill set, we often don't know that they have a passion for things like coding or anything else. And so we're really doing a lot more where we're getting deeper and being able to match your supply and demand in house as well as know when we have a demand for someone. And this person almost meets that demand. Based on all the different dimensionality that weaken dio, >> we can >> put them into this specific training class and then allow them to go through that training class so that we can upgrade the entire upscale and reschedule the entire work force. >> So one of the challenges you're working on is trying to operationalize machine intelligence and obviously starts with that training and skill level so well, it's not easy company the size of of I B M E. You're starting the GTS group, which probably has an affinity, at least conceptually, for transformation. That's what you guys do for your clients. So how's that going? You know, where are you in that journey? >> I think that we're in the journey and we're doing really well. I think that a lot of our people and the people who are actually working on the ground, we're talking to our clients every single day. So people on the helped us, they're talking to clients and customers. They understand what that client is doing. They understand the means, the troops, the mores, the language of the customer, of the organization of the customer, in the client, giving those people skills to understand what they can do better. To help solve our client's problems is really what it's all about. So understanding how we can take all of the unstructured data, all of like the opportunity for understanding what skills those people have on the ground and then being able to match that to the problems that our clients and customers are having. So it's a great opportunity. I think, that I've been in GTS my entire career and being an I t. I think that you understand this is where you store or create or, you know, manage all of the data in an entire enterprise organization, being able to enable and empower the people to be ableto upscale and Reese kill themselves so that they can get access to that so that we can do better for our clients and customers. >> So when you think about operations, folks, you got decades of skills that have built up you. D. B A is, you got network engineers, you got storage administrators. You know the VM add men's, you know, Unix. Add men's, I mean and a lot of those jobs. Air transforming clearly people don't want to invest is much in heavy lifting and infrastructure deployment, right? They want to go up the stack, if you will. So my question is, as you identify opportunities for transformation, I presume it's a lot of the existing workforce that you're transforming. You're not like saying, Okay, guys, you're out. What is gonna go retrain or bring in new people? Gonna retrain existing folks? How's that going? What's their appetite for that? Are they eagerly kind of lining up for this? You could describe that dynamic. >> I think the bits on the ground, they're very hungry. Everyone is so, so, so hungry because they understand what's coming on. They listen to the messages, they're ready. We were also in flexing. I'm sure you've heard of the new collar program were influencing a lot of youth as early professional hires. I have 2 16 year olds in the 17 year old on my team as interns from a P Tech program in Boulder, and getting that mix in that diversity is really all what it's about. We need that diversity of thought. We need that understanding of how we can start to do these things and how people can start to reach for new ways to work. >> All right, so I love this top of the cube we've we've covered, you know, diversity, women in tech. But so let's talk about that a little bit. You just made a statement that you need that diversity. Why is it so important other than it's the right thing to do? What's the what's the business effect of bringing diversity to the table? >> I think that would. We're searching for information truth if you want. If you want to go there, you need a wide variance of thought, the white of your variance, the more standard you're me, and it's actually a mathematical theory. Um, so this is This is something that is part of our truth. We know that diversity of thoughts we've been working. I run and sponsor the LGBT Q Plus group. I do women's groups in the B A R G's and then we also are looking at neuro diversity and really understanding what we can bring in as far as like, a highly diverse workforce. Put them all together, give them the skills to succeed. Make sure that they understand that the client is absolutely the first person that they're looking at in the first person that they're using Those skills on enable them to automate, enable them to stop doing those repeatable tasks. And there's so much application of a I that we can now make accessible so that people understand how to do this at every single level. >> So it's a much wider scope of an observation space. You're sort of purposefully organizing. So you eliminate some of that sampling bias and then getting to the truth. As you say, >> I think that in order to come up with ethical and explainable, aye, aye, there's definitely a way to do this. We know how to do it. It's just hard, and I think that a lot of people want to reach for machine learning or neural nets that spit out the feature without really understanding the context of the data. But a piece of data is an artifact of a human behavior, so you have to trace it all the way back. What process? What person who put it there? Why did they put it there? What was that? When we when we look at really simple things and say, Why are all these tickets classified in this one way? It's because when you observe the human operator, they're choosing the very first thing human behaviors put data in places or human behaviors create machines to put data in places. All of this can be understood if we look at it in a little bit of a different way. >> I thought I had was. So IBM is Business is not about selling ads, so there's no one sent to future appropriate our data to sell advertising. However, if we think about IBM as an internal organism, there's certain incentive structures. There's there's budgets, there's resource is, and so there's always incentives to game the system. And so it sounds like you're trying to identify ways in which you can do the right thing right thing for the business right for people and try to take some of those nuances out of the equation. Is that >> so? From an automation perspectively build digital management system. So all the executives can go in a room and not argue about whose numbers are correct, and they can actually get down to the business of doing business. From the bottoms up perspective, we're enabling the workforce to understand how to do that automation and how to have not only the basic tenets of data management but incorporate that into a digital management system with tertiary and secondary and triangulation and correlations so that we have the evidence and we can show data providence for everything that we're doing. And we have this capability today we're enabling it and operational izing. It really involves a cultural transformation, which is where people like me come in. >> So in terms of culture, so incentives drive behavior, how have you thought through and what are you doing in terms of applying new types of incentives? And how's that working? >> So when we start to measure skills were not just looking at hard skills. We're looking at soft skills, people who are good collaborators, people who have grit, people who are good leaders, people who understand how to do things over and over and over again in a successful manner. So when you start measuring your successful people, you start incentivizing the behaviors that you want to see. And when you start measuring people who can collaborate globally in global economies that that is our business as IBM, that is who we want to see. And that's how we're incentivizing the behaviors that we want to. D'oh. >> So when I look at your background here, obviously you're you're a natural fit for this kind of transformation. So you were You have an anthropology background language. Your data scientist, you do modeling. >> I always say I'm a squishy human data scientist, but I got to work with a huge group of people to create the data science profession with an IBM and get that accredited through open group. And that's something we're very passionate about is to give people a career past so that they know where their next step is. And it's all about moving to growth and sustainable growth by making sure that the workforce knows how value they are by IBM and how valuable they are by our clients. What does >> success look like to you? >> I think success is closer than we think. I think that success is when we have everybody understanding everybody, understanding what it's like to pick up the phone and answer a customer service call from our client and customer and be able to empathize and sympathize and fix the problem. We have 350,000 human beings. We know somebody in some circle that can help fix a client's problem. I think success looks like being able to get that information to the right people at the right time and give people a path so that they know that they're on the boat together, all rowing together in order to make our clients successful. >> That's great. I love the story. Thanks so much for coming on the hearing. You're very welcome. Keep it right there, but we'll be back with our next guest is a day. Violante. We're live from Fisherman's. More for the IBM CDO Chief Data officer event. Right back sticker The cube dot net is where the

>> fly from San Francisco. It's the Cube covering Google Club next nineteen Tio by Google Cloud and its ecosystem partners. >> Welcome back to the Cubes live Google next nineteen coverage. I'm General Dave Violante. We're here for three days of wall to wall coverage, breaking down all the content from Google Clouds. Big conference here, Google next twenty nineteen or next gas joke of a vice president. Google Data Centers spans all the data centers that Google and Google Cloud deploy. He's the man in charge of thousands of full time employees, thousands of contractors, tens of thousands of construction worker. He's building out the infrastructure and footprint to make the cloud work for Joe. Welcome to the Cube. >> Thank you both Very much. >> So. Sin DARPA Kai, the CEO of Google, kicked off the Kino, the new CEO of Google Cloud. Thomas Korean came on always ten weeks into the job. Clearly, the investment in Google cloud new building on separate from campus. So Google and Google Cloud or two separate groups, has been reported clearly by us and others. But at the end of the day, you're gonna run all the stuffs on somewhere. So you know, you guys have deep, deep experience. I know personally and following Google and covering Google thie excellence and engineering the excellence in building on data centers. What is the status of just quickly Take a minute to explain how it's organized? Get Google proper, Which is where Ron knows Google, Google Search, etcetera, Gmail and Google Cloud. How's that? How's that operate? What's some of the data points? >> Okay, um so, as you know, the head of the teams that do everything from procuring land and writing energy contracts and buying renewable energy to designing, building and operating all the data centers. Cloud is one of my largest customers. But my other customers air search and ads and Gmail and G sweetened. So, really, our data centers I Google are built for the entire Google enterprise, and cloud happens to be one of our largest internal customers in that enterprise. >> How about some of stats countries, regions, data centers? What's the new one? Because you have regions, you availability zones. Talk about some of the stats inside the numbers >> s o what the starting at the Google level, we have data centers in four continents. So we're in North America South America, Asia and Europe. Of course, we have a probably one of the world's largest global private networks with, you know, thirteen undersea cables that are our own and hundreds of thousands of miles of dark fiber and lit fiber that way operate like I said, probably one of the world's largest networks we have in in Europe were in five countries in Europe, were in two countries in Asia. We're in one country in South America, and that's at the Google and North America. Of course, we have many, many, many sites across all of North America. That's it. The Google level now Cloud has nineteen regions that they operate in and fifty eight zones. So each region, of course, has multiple zones in it. You know, we we cover. Google has presence in over two hundred countries worldwide, so really, it is truly global operations. >> So the two hundred countries is Google wide nineteen cloud regions and fifty eight availability zones. That's Google Cloud. That's great. Okay, so do you not sort of mix infrastructure for cloud and things like Gmail and maps and search is that is that correct? This their separate infrastructures or >> it's It's not so separate infrastructure. So when when my team builds a data center, any one of our internal customers could be in that day this up. In addition to the Google owned and operated data centers, we also have some sites that are least in certain regions, and Cloud may be occupying those. But regardless of whether it's owned or leased, its the same hardware in there, it's the same operation staff that Aeryn they're the same expertise, the same deep knowledge about operating cloud environments. And so, regardless of whether we built it or we leased it >> from a CEO Syrian from a CEO's perspective, it's the same cell A nobody availabilities owners. I mean, that's what really matters, right? Okay, >> talk about the scale because one of the things I liked in the Kino Sundar is awesome. And Chris, Great keynote, You scale multiple times. He also had a clever comin around steal, she's said before publicly, amount of steel that goes into building this. This gives you guys large scale. Your guys are building on massive. It's like smart cities almost cause of your own like country, pretty much on the infrastructure. What are some of the key learning that you guys had because you have to be very efficient. Google likes to solve hard problems. You guys have done some things with sustainability. Specifically, talk about some of the learnings. As you guys have been building out these data centers for years with cloud on a massive expansion, you gotta watch the environment. You got to do some things. What if some of the learnings with some of the notable accomplishments you guys air forging on and what are some of the goals? >> So I googled we've been We've been at this for two decades. For more than twenty years we've been building and innovating on hyper efficiency, hyper scale, basically trying to build infrastructure that was more sustainable than had ever been thought possible. And then as our cloud business started to expand and boom, frankly, we set apart Teo build the world's most sustainable cloud. And really, what that means is that you know, we were the first company to announce that we were buying one hundred percent renewable energy, new renewable P P A's to match one hundred percent of our consumption and in twenty seventeen, we achieve that. That was after being carbon neutral for ten years before that. So going all the way back to two thousand seven were a carbon neutral company by mostly buying, buying high quality carbon offsets. Then we decided that no, we want to advance the transition, Teo renewable and sustainable energy. So we started buying direct power purchase agreements for wind and solar on DH. And then in twenty seventeen, we announced that we had matched one hundred percent. What that means is that we've acquired over three gigawatts of new solar and wind power purchase agreements, Mom. And now we're taking it a step further. We have a very ambitious kind of moonshot. Arguably, too, not only match our consumption, but match it twenty four hours a day, seven days a week, three sixty five. So you can imagine the complexity with this because the wind doesn't always blow, the sun doesn't always shine. And so that's going to take moonshot thinking in order for us to get there. But we feel so strongly about it were so committed to this cause that we've got a dedicated team working on this right now. >> So it's not just squeezing tea. You'ii out of the data center I'm sure you're doing that, but absolutely doing >> that. Since the earliest days I've been at Google for over eleven years. From the very first day I got there, I was completely blown away with the numbers that I was seeing about the Peewee and for maybe your audience. Pee Wee's a measure of efficiency in the data center, and and at the time, like back two thousand eight, Cooper was achieving numbers that the EPA thought wouldn't be achieved until, like, twenty twenty. And so I started to dig in and look how, and it was astounding to me the lengths that the company had gone tio toe optimize every single step of the way from the high voltage transformers in our own dedicated substations. Excuse me that that are much more efficient than typical. You know, utility transformers all the way through, minimizing the number of transformations going from grid level like three hundred forty five thousand bolts down to server voltage level, minimising the number of transformations reinventing the way people think about cooling. When we when I got to Google, I was also amazed. Our data centers are running it like roughly about eighty degrees Fahrenheit most data centers run it like sixty five degrees are data centers consume about half of the energy of a traditional enterprise data center at the same size. And in addition to that, we're producing about seven times the computer capacity for the same amount of Watts that enterprise data >> centers comes from. A from a practice of engineering really purpose engineering from day one into the overall holistic plan of the building. >> It's a relentless focus on efficiency and innovation. Right from Day one, when I got there, it had already been well in motion, but it's optimizing across the entire stack. It's optimizing software to be efficient, optimizing the server architecture er, to be more efficient, optimizing the power supplies in the server's optimizing the racks. You know, designing the racks to be working with the cooling equipment, specifically, are cooling systems are unique to Google. There they're not traditional air conditioning units that you would buy for traditional data centers. Sometimes, you know, we'll sight data centers where we can use natural environment in Finland. Our data centers right on the Gulf of Finland, and we use cold seawater from the Gulf of Finland to cool the data center. >> So to be clear, you're doing quite a bit of vertical integration, whether it's your own transformers of power supplies and other equipment, right? Try >> fiberoptic across the K Atlantica, Sundar pointed out. That's what I was doing your own stuff, absolutely officious as you pass on in savings to the customers and society with the sustainability piece. That's right. You have two angles on that. >> Really, it's you know it's good business, of course, because the bottom line. But more importantly, it's also the right thing for us to do. We feel very strongly that we need to be responsible for our impact on the environment and to minimize that impact and to be accountable for it. And we realized that the only way we can truly be accountable for our impact on the environment and for our energy consumption is to have it matched with renewable energy twenty four hours a day, seven days a week, >> not take a side track you. But you know, we've been covering the tech business for many, many decades, and certainly recently tech kind of got a bad name headlines. But I always look for tech stories that you know there was a text bad for people. There's always a good story. I think this is an example of tech for good. You guys have taken real engineering, building large scale systems and facilities, have software running on it. It's really a tech for good story. Congratulations on that. That's awesome work. Now I want to kind of asked you put you on the spot here because I think one conversation we're hearing a lot and I want him Get your expert opinion on this could be Google and also a CZ a person in the industry. Security in the supply chain has come up a lot in terms of whether chips have been hacked. Wave heard things like that in the story. Some of them have proved to be misinformation. Fake news. But you gotta watch security. Google's really hard core on security because you you lived that. How do you look at the supply chain? Is if you're not just throwing contractors at this, you could thinking of a realistic ground zero engineering approach to a holistic picture. How do you guys manage security challenge in the supply chain? Throughout the facilities from chips Teo, access things of that nature. >> So there's two aspects. There's always the logical and the physical security aspect from the physical security aspect in our warehouses that we manage. Of course, we apply the same rigorous standards for physical security. That way, do it their data centers. And that's multi layer in various different types of security technologies that we apply. And but on the logical side, you know, I think you're probably familiar with our Titan chips that way developed and those tightened ships are put in all of our servers, and from the time that they're built to the time that they're in the facility, you know those those chips that's our are securing the servers and your logical side. Though the you know, my colleagues on our information security team are truly the experts that could address that. >> That's where the software shines. That's right, and this is not just one. It's not a silo. You gotta deal physical build. It's kind of a bigger is It's a holistic, any rated model >> it is, and this is, you know, from from the data center industry perspective for us. Long as there's been it, there's always been the debate between facilities and I t right. When I got to Google, I was also so relieved to see that was all technical infrastructure and the systems. The software that runs on those those data centers are all under the same technical infrastructure group. And so you know it all. The buck stops at *** >> For years, there was a discussion and generalize about those groups coming together, and I think the way they come together is the cloud. Frankly, because you haven't seen a lot of change within organizations of ight and facilities really working together, that's right. >> Well, Joe, thanks for coming on the Q. Thanks for sharing your insight. Final word. What's the thoughts folks watching out there who were trying to understand how to bring technology into facilities? In general, people still have data centers they still have on premise activity, from lightbulbs to whatever any, any learnings in parting wisdom. Folks watching there in the facilities and or physical building space on howto build out these, whether it's smart cities with its construction and experiences, you could share with folks out there looking to build a ballistic long term plan. >> Yeah, there's a there's a few things first of all, we've published all of our energy efficiency, best practices. And so I encourage everyone to take a look at those best practices because the best you know, energy savings is the energy not consumed in the first place. So do all the right things to reduce the overall energy consumption in the first place to we want to help further the transition to renewable energy. And so we've published a lot about our power purchase agreements and a lot of the policy work that enables us to do. Those is also set in place for other large energy consumers that want to do the same thing. So our policy work can help Teo allow others to do the same thing. The third part of our sustainability aspect is really a circular economy. You know, we want Teo. I have zero waste to a landfill. We've currently achieved ninety one percent diversion of all of our data center operations, so ninety one percent is diverted to landfill. But we have a objective of one hundred percent note note no waste to a landfill. And then that means you have to do smart things like better re use better recycling better reselling of products that are still good but maybe out of date for for your use and then just ended off. We've really invested in our machine learning and a intelligence both on the data center operations. We have now ml running our some of our cooling systems in fully autonomous mode and doing a much better job of matching the cooling needs to the workloads at the time. And we took that same learning with our deepmind group, partnered with them, and we've applied that Teo are a wind farm now as well, so that they can better predict what the output of wind farm is going to be thirty six hours in advance. That allows the operators of the grid to better bring on more more energy and get higher value Out of that that win dinner. >> Great engineering story at scale. Congratulations. Love the societal impact tech for good. Congratulations. Love to have you back talk about the impact of a i ot Joe, Thanks for coming on the Yeah, it's all coming together with our arms. Jean. A center is not going away. House in the cloud needs to run on servers and has to be done in a engine engineered fashion. Google's leading the charge there. It's Cube Live coverage day, one of three days of coverage will be right back after this short break.