Summit Live: Partners - Hear From Key Companies on Adding Value

Key Takeaways

[Music] [Applause] Hi, I'm Holly from developer relations at Data Bricks. This year is going to be my fourth data and AI summit and I can't wait to see everyone there. I know what we're announcing and I'm pretty sure you're going to love it. Fun fact about me, I once reviewed a machine learning model and could tell instantly it was utter rubbish as it had a category for people born in Antarctica. Yeah. Hello, my name is Jason Paul. I'm a senior director of data management here at Data Bricks and I was also the founder of Data Bricks Labs. I've been at Data Bricks for about 10 years now and I was one of the first 10 solution architects on the team. This is going to be my 13th data and AI summit and I'm super excited because uh summit time is like Christmas for data engineers and AI engineers. We get to learn about all the new products the data bricks has been working on and I'm especially excited to hear what we're going to be doing to help AI engineers build AI agents in a production grade way and make it easy for them to do it. I'm also excited to hear how data bricks apps has been evolving ever since we announced it earlier this year. Fun fact is that 10 years ago is when Spark first introduced its type system with what was called schema RDDs at the time later became dataf frames. Fast forward to today and now data brick SQL which is based on Apache Spark is now the fastest growing data warehouse on the market. Thank you for joining us again and we have some new hosts. Uh I'm Holly and this is Jason. Hello. And with us uh we have Naveen Punjabi from Google Cloud. Hello. Thank you very much for joining us today. Thank you for having me. I understand you spend a lot of your time on partnerships. Uh you are the director for analytics AI ASV partnerships. I believe you got it right. And uh it's great to be in front of the data bicks community. Me and my team work on all our strategic analytics and AI partnerships. Data bricks is a very important partner for us. Yes. And it's a very important partnership for us as well. So um can you tell us a little bit you know we've got data bricks. We've got them on different clouds. They're slightly different flavors. What is the flavor of Google cloud for data bricks look like? So when we think about working with data bricks um the one thing that we look at is how can we make Google cloud as the best platform for customers to run their data and AI workloads that are running on data bricks and and essentially what that means is bringing the power of Google cloud on the AI infrastructure side the storage side the networking work that we are doing how do we bring all of that into data bricks so customers have the greatest experience and also if you're running data bricks on Google cloud you get access to our native Google AI services. A lot of innovation happening there. Okay. And um yeah, the customers uh get to experience a seamless integration all the way through. Okay. Then so you you threw a lot of products at me there and a lot of kind of technologies. Can you maybe give us an example where there's been like doubling down on efforts to make that integration really clean? We are innovating with data bricks across the stack. So I'll start from the foundation layer. On the AI infrastructure side, there is a lot of differentiation Google cloud has on the compute layer. You can think about some of the work that we have done around custom machine types where customers can actually say and configure the exact vcpus and the memory that is needed for a data bricks workload. It makes the workload run much more efficiently cost cost benefit for the customers there. So that's one key example. The other one is and we have a talk we did just did a talk about it is when you run data bricks on Google cloud it's running on Axion which is Google uh custom ARMbased processors it is 10% more performant than anything out there that's impressive and 60% more energy efficient so it's a benefit for our customers there's an also one of the unique things about Google infrastructure is live migration so if you're running your data bricks workload and there's a system maintenance you don't even know about it the machine gets swapped underneath without any downtime and that is a lot of uptime higher uptime of data bricks on on Google cloud same with storage we have a session later in the day today about things that we are doing on GCS because data bricks uses GCS on Google cloud uh Google cloud storage and what are we doing there in terms of you know innovations like running open lake architectures effectively on GCS so there's things like hierarchal namespaces anywhere caching coming in multi-reion buckets lot of interesting stuff there and then when you run data bricks on Google cloud it's using our all the security innovation around how do you access Google resources in the most effective way over private network so not public yes and um then you have unity catalog and all of that brings that together for customers to be able to run their data workloads securely on Google yeah I got to say like the access management one it does tend to get forget forgotten about a little bit and I think that's kind of the big problem with it really because people like they love putting all their tech together and making all this thing work and then they you know they finish the project and then they go for their review and it's like oh but what about access management and it's all gone completely wrong. So I think yeah it might not be the sexiest thing it might not be AI but it is important and and with the world of agents that access management is becoming even more critical like when an agent is calling into a resource is it on whose behalf is it calling? Yeah and that comes into play in a big way. So you got to get it get that right at the foundation layer. Yeah, we have a we have a lot of excitement from customers about GCP and running data bricks on GCP. Do you have any um special customer stories you want to share and talk about? We have a lot of customers running data bricks on Google cloud. Very happy customers. But one of them that I can highlight is Uplight. Uh they are in the clean energy space trying to get data from smart systems, smart thermostats, usage meters, EV chargers and then use that data to optimize grid performance. So they're working with a lot of our utility companies in that sense and they were running uh legacy infrastructure which didn't allow them to run spark jobs effectively. So what they've done is they're using data bricks on Google cloud. They're using our native service pubsub to move that data into data bricks process that data 30,000 jobs per month that they run very very efficiently that's a lot on data bricks on Google cloud and the beauty of it all is then what they're able to do is leverage MLflow for creating ML models and then serve it with vertex AI which is the best of breed like they're using cloud technology along with data bricks and that's very very useful so they were able to reduce their development time by 50% % because of this efficiency that came into being. That's phenomenal. Uh especially when it comes to migrations. No one loves spending time on migrations. So I wanted to quickly faster the better. Yeah. Uh so I know we've got a little bit of time left. If you had to pick one feature in the road map that you're excited for, what would it be? Oh, there's a lot of work that is happening on the AI space. Uh we are doing a lot of work in terms of TPUs coming out with agent space model development on the Gemini side. So that's work in progress with data bricks on that one. So you'll see a lot of things coming on that one. Unity catalog is another one where there now is a standardization at the storage layer. How do you bring that standardization at the catalog layer? Make it simpler for customers. Few things to look out for. Excellent. Okay then. Wonderful. Okay then. Thank you Naveen so much for joining us. It's been an absolute honor to have you here and I'm really excited for these things that are coming out. Jason, thank you so much Naveen. Thank you so much. And uh let's head to our next interview. Hi, I'm Ari Kaplan, head of evangelism at data bricks and I'm here with Vshaw. Why don't we uh start off talking about what you do here. Thank you Ari and it's great to be here. By the way, the energy in Moscone Center is off the charts and if there's one place that you would want to be to understand what's happening in data and AI, this is the place to be and no better time than now. So my role at Accenture, it has two components to it. First one is creating the offerings that we take to our clients in the area of technology and the second one is leading the go to market teams that activate these offerings tailing tailoring them to our client specific needs. Very cool. Yeah. So we we've been hearing about the Accenture research on the front runners guide to scaling AI. Uh why don't you tell us what that's all about? Sure. So this is an awesome piece of study that we've done and let me first start by explaining what scaling AI means. So when an enterprise leverages its AI capabilities across every initiative in the firm in a way that delivers impactful and broader outcomes for the business. That's our definition of skating AI. Let me give you an example of how I relate to it in simple terms. So I know my son has a guitar at home. I know there's a lot of street bands and then I also know that I've gone and experienced an orchestra and when the percussion, the brass instruments and the string instruments come together that's when magic happens in the theater that is an orchestra coming to life. So for me when you put that in an enterprise context every single AI capability that you invested in all working in harmony in a seamless integrated fashion releasing that magic for the business that is scaled AI for you. So we went and did a research speaking to about 2,000 clients uh across 15 countries, industries and we spoke to specifically data and AI sea level executives and we wanted to find out how they were scaling their strategic bets to deliver this business value for themselves and we described the five imperatives that we think are necessary for them to achieve the scale success. First one being focus on value. So lead with value. Second one is reinvent talent and ways of working. Third one being builds an AI enable secure digital core. Fourth one being uh close the gap on responsible AI and drive continuous reinvention. And then we categorized the responses that we got into four phases. First one being front runners, second fast followers, then uh sort of progressing with AI and experimenting in in AI. And we found out that of all the conversations that we had only 8% of our clients are actually front runners. And these are the ones that are investing in building a strong data and AI foundation, a digital core with relentless focus on getting to Agentic from day one. That is what differentiated the front runners from the lagards. And 94% of these were investing in at least three new gen relevant data and AI capabilities. 67% of these were investing in skilling in autonomous AI agents that are tailored to their industry context. So we've now in this report published 108 such strategic bets that our clients can take which can all by the way be enabled by data bricks and I would encourage everyone to go and read this report. It's available on our website. You can scan the QR code for it. So you know that's the research in its summary. That's amazing. I love how you broke it into front runners to lagards and different advice and scaling depending on what uh maturity level you are in your journey. So Accenture worldclass organization probably everyone on the planet knows uh but how are you working with data bricks? So first and foremost thank you. I say thank you because you know for the seventh time we've cut the global partner of the year award and then we've won five other awards here in the industries that we serve in the markets uh that we serve. While we are doing a lot with data bricks the one area that we are hyperfocused on and the singular purpose that we have is how do we leverage data bricks as technologies to bring to life industry solutions that make sense for our clients. So deliver the use cases that our clients can see value from. And our entire focus with this is to build the right level of industry talent. Um uh surround them with technology uh folks that understand the data bricks technologies and serve our customers in a way that solves their problem. So a good example of this is anyone that has experienced a call center. You know, I know when there is an agent on the other side, I cringe because I'm trying to think is is this company now the front runner or is this the one experimenting in in AI because I know the front runner will deliver the experience that matters to me and will understand my context for the call that I'm making. So, you know, leveraging technologies like uh LLMs and mosaic uh open source and and lang chain we have actually helped clients significantly improve the contextual relevance in the conversations that their agents are having. uh with their customers uh reducing lag times or weight times and significantly improving operational efficiency. So this is just some of the ways in which how our industry experts our geni experts working with data bricks is delivering the right solutions to our clients in a way that uh is relevant for them. Great. Well Vishall thank you so much and thank you for the partnership as well. Greatly appreciate it. I love hearing the stories and uh adding value together to the customers. Well I'm glad to be here. So thank you all. Thank you. Yes, we've got another video coming up for you right now. So, this is uh continuing the theme of working with partners and this next partner is Deote and we can't uh you can't implement data bricks without a lot of people on keyboard and deote helps deliver those people to help make data bricks successful. All right, we are so excited to have Michelle from Deote here with us. Uh why don't you say a quick introduction? All right, thank you so much. My name is Michelle Gochce. I run our US banking and capital markets practice with Deote. So responsible for all of our solutions and services. Excellent. Why don't we start off with the main question? How does data bricks and deote collaborate? I have been so impressed with our you said the word for me collaboration. I really think our cultures between deote and data bricks are very very similar focused on the customer really intellectually curious and also very very innovative. I'm also very appreciative with data bricks how industry focused we are and I know that's kind of why we're here Ari but really going deep especially for me in our banking and capital markets customer outcomes what they care about that joint accountability between us has been really powerful yeah and it's been so powerful you just won a partner of the year award right not excited just kidding super excited about that yeah congratulations welld deserved and thank you yeah and like some of the uh business models like especially in banking. Uh I know there's a lot of industries we both play in, but you're you're the expert in banking. Tell us about the business model there. Something that's kept me in banking for the past 20 years is banking and capital markets entities, they have 30-year-old systems and they have three-month-old systems. And so what that means for the banks is with hundreds of platforms in those institutions, data really does become kind of like the key asset that they have to unlock when they think about making their operations more efficient, more safe from a fraud perspective. Um, understanding all of the information about those customers so that they can grow products and services. So I think that's really what's key between us as we think about our partnership and what we're doing with clients is how do we accelerate that for the banks? How do we let them unlock all of that data to not only meet regulations, be safe, but also grow their client base um with insights? Yeah, incredible. Now, we were also chatting beforehand uh data as a service. So, I wanted to hear more about that. That's really cool. Data as a service is a deote solution that we partner with data bicks and sell with data bricks. And what it means is is it allows us to have a business attribute listing understanding all of the different data elements and how they're defined. The second piece in data as a service is a smart map. And what the smart map does is the smart map allows all of those data elements to be integrated with different platforms. Again, speeding up innovation for the banks, which is our goal. Um, another attribute that data as a service has is a genai glossery. Cool. And that Genai glossery allows us to have business definitions so that it's really clear, especially where there's parts of data in platforms that the banks can't really interpret, but what are the business definitions of that data? What are the lineage around it? So again, all for the purposes of using it towards innovation. Yeah. And that's super cool. I love all the phrases you're saying like lineage and uh just discoverability since banks have like tons of tables, tons of column names, they all have like sales this that the other region XYZ and to have like Gen AI to help users uh all different personas find the data is uh super important. And you think about the types of customers that banks have. They have retail consumer customers. They have massive fluent wealth customers. Then they have all the way from wholesale customers and we're talking billion-dollar plus companies and the hierarchy of those data is it becomes more and more complex. So again all coming down to a bank being able to understand where the data is, what is it and how do they use it to funnel growth and ultimately new services for their clients. Exactly. And all with like the lineage and governance uh especially in the banking where you have you know you know clear-cut regulations. That's right. That's right. regulations and then also fraudsters out there and how do you how do you have a handle of trying to use data to predict what their next move is going to be so you can protect the bank and the customer before that? Oh great well in the the final seconds any any extra thing you wanted to add? I mean I just want to be thank data bricks we are so excited about the partnership Ari and then everything is about scaling and driving more business outcomes for our clients. Great well great note and we're thankful as well and thanks everyone for listening. Thank you. [Applause]