(gentle music) >> Welcome everyone to theCUBE's presentation of the AWS Startup Showcase. The theme is Data as Code; The Future of Enterprise Data and Analytics. This is the season two, episode two of the ongoing series of covering the exciting startups in the AWS ecosystem around data analytics and cloud computing. I'm your host, John Furrier. Today we're joined by great guests here. Three guests. Wen Phan, who's a Director of Product Management at Ahana, Satyam Krishna, Engineering Manager at Blinkit, and we have Akshay Agarwal, Senior Engineer at Blinkit as well. We're going to get into the relationship there. Let's get into. We're going to talk about how Blinkit's using open data lake, data house with Presto on AWS. Gentlemen, thanks for joining us. >> Thanks for having us. >> So we're going to get into the deep dive on the open data lake, but I want to just quickly get your thoughts on what it is for the folks out there. Set the table. What is the open data lakehouse? Why it is important? What's in it for the customers? Why are we seeing adoption around this because this is a big story. >> Sure. Yeah, the open data lakehouse is really being able to run a gamut of analytics, whether it be BI, SQL, machine learning, data science, on top of the data lake, which is based on inexpensive, low cost, scalable storage. And more importantly, it's also on top of open formats. And this to the end customer really offers a tremendous range of flexibility. They can run a bunch of use cases on the same storage and great price performance. >> You guys have any other thoughts on what's your reaction to the lakehouse? What is your experience with it? What's going on with Blinkit? >> No, I think for us also, it has been the primary driver of how as a company we have shifted our completely delivery model from us delivering in one day to someone who is delivering in 10 minutes, right? And a lot of this was made possible by having this kind of architecture in place, which helps us to be more open-source, more... where the tools are open-source, we have an open table format which helps us be very modular in nature, meaning we can pick solutions which works best for us, right? And that is the kind of architecture that we want to be in. >> Awesome. Wen, you know last time we chat with Ahana, we had a great conversation around Presto, data. The theme of this episode is Data as Code, which is interesting because in all the conversations in these episodes all around developers, which administrators are turning into developers, there's a developer vibe with data. And with opensource, it's software. Now you've got data taking a similar trajectory as how software development was with code, but the people running data they're not developers, they're administrators, they're operators. Now they're turning into DataOps. So it's kind of a similar vibe going on with branches and taking stuff out of and putting it back in, and testing it. Datasets becoming much more stable, iterating on machine learning algorithm. This is a movement. What's your guys reaction before we get into the relationships here with you guys. But, what's your reaction to this Data as Code movement? >> Yeah, so I think the folks at Blinkit are doing a great job there. I mean, they have a pretty compact data engineering team and they have some pretty stringent SLAs, as well as in terms of time to value and reliability. And what that ultimately translates for them is not only flexibility but reliability. So they've done some very fantastic work on a lot of automation, a lot of integration with code, and their data pipelines. And I'm sure they can give the details on that. >> Yes. Satyam and Akshay, you guys are engineers' software, but this is becoming a whole another paradigm where the frontline coding and or work or engineer data engineering is implementing the operations as well. It's kind of like DevOps for data. >> For sure. Right. And I think whenever you're working, even as a software engineer, the understanding of business is equally important. You cannot be working on something and be away from business, right? And that's where, like I mentioned earlier, when we realized that we have to completely move our stack and start giving analytics at 10 minutes, right. Because when you're delivering in 10 minutes, your leaders want to take decisions in your real-time. That means you need to move with them. You need to move with business. And when you do that, the kind of flexibility these softwares give is what enables the businesses at the end of the day. >> Awesome. This is the really kind of like, is there going to be a book called agile data warehouses? I don't think so. >> I think so. (laughing) >> The agile cloud data. This is cool. So let's get into what you guys do. What is Blinkit up to? What do you guys do? Can you take a minute to explain the company and your product? >> Sure. I'll take that. So Blinkit is India's biggest 10 minute delivery platform. It pioneered the delivery model in the country with over 10 million Indian shopping on our platform, ranging from everything: grocery staples, vegetables, emergency services, electronics, and much more, right. It currently delivers over 200,000 orders every day, and is in a hurry to bring the future of farmers to everyone in India. >> What's the relationship with Ahana and Blinkit? Wen, what's the tie in? >> Yeah, so Blinkit had a pretty well formed stack. They needed a little bit more flexibility and control. They thought a managed service was the way to go. And here at Ahana, we provide a SaaS managed service for Presto. So they engaged us and they evaluated our offering. And more importantly, we're able to partner. As a early stage startup, we really rely on very strong partners with great use cases that are willing to collaborate. And the folks at Blinkit have been really great in helping us push our product, develop our product. And we've been very happy about the value that we've been able to deliver to them as well. >> Okay. So let's unpack the open data lakehouse. What is it? What's under the covers? Let's get into it. >> Sure. So if bring up a slide. Like I said before, it's really a paradigm on being able to run a gamut of analytics on top of the open data lake. So what does that mean? How did it come about? So on the left hand side of the slide, we are coming out of this world where for the last several decades, the primary workhorse for SQL based processing and reporting and dashboarding use cases was really the data warehouse. And what we're seeing is a shift due to the trends in inexpensive scalable storage, cloud storage. The proliferation of open formats to facilitate using this storage to get certain amounts of reliability and performance, and the adoption of frameworks that can operate on top of this cloud data lake. So while here at Ahana, we're primarily focused on SQL workloads and Presto, this architecture really allows for other types of frameworks. And you see the ML and AI side. And like to Satyam's point earlier, offers a great amount of flexibility modularity for many use cases in the cloud. So really, that's really the lakehouse, and people like it for the performance, the openness, and the price performance. >> How's the open-source open side of it playing in the open-source? It's kind of open formats. What is the open-source angle on this because there's a lot of different approaches. I'm hearing open formats. You know, you have data stores which are a big part of seeing that. You got SQL, you mentioned SQL. There's got a mishmash of opportunities. Is it all coexisting? Is it one tool to rule the world or is it interchangeable? What's the open-source angle? >> There's multiple angles and I'll let definitely Satyam add to what I'm saying. This was definitely a big piece for Blinkit. So on one hand, you have the open formats. And what really the open formats enable is multiple compute engines to work on that data. And that's very huge. 'Cause it's open, you're not locked in. I think the other part of open that is important and I think it was important to Blinkit was the governance around that. So in particular Presto is governed by the Linux Foundation. And so, as a customer of open-source technology, they want some assurances for things like how's it governed? Is the license going to change? So there's that aspect of openness that I think is very important. >> Yeah. Blinkit, what's the data strategy here with lakehouse and you guys? Why are you adopting this type of architecture? >> So adding to what... Yeah, I think adding to Wen said, right. When we are thinking in terms of all these OpenStacks, you have got these open table formats, everything which is deployed over cloud, the primary reason there is modularity. It's as simple as that, right. You can plug and play so many different table formats from one thing to another based on the use case that you're trying to serve, so that you get the most value out of data. Right? I'll give you a very simple example. So for us we use... not even use one single table format. It's not that one thing solves for everything, right? We use both Hudi and Iceberg to solve for different use cases. One is good for when you're working for a certain data site. Icebergs works well when you're in the SQL kind of interface, right. Hudi's still trying to reach there. It's going to go there very soon. So having the ability to plug and play different formats based on the use case helps you to grow faster, helps you to take decisions faster because you now you're not stuck on one thing. They will have to implement it. Right. So I think that's what it is great about this data lake strategy. Keeping yourself cost effective. Yeah, please. >> So the enablement is basically use case driven. You don't have to be rearchitecturing for use cases. You can simply plug can play based on what you need for the use case. >> Yeah. You can... and again, you can focus on your business use case. You can figure out what your business users need and not worry about these things because that's where Presto comes in, helps you stitch that data together with multiple data formats, give you the performance that you need and it works out the best there. And that's something that you don't get to with traditional warehouse these days. Right? The kind of thing that we need, you don't get that. >> I do want to add. This is just to riff on what Satyam said. I think it's pretty interesting. So, it really allowed him to take the best-of-breed of what he was seeing in the community, right? So in the case of table formats, you've got Delta, you've got Hudi, you've got Iceberg, and they all have got their own roadmap and it's kind of organic of how these different communities want to evolve, and I think that's great, but you have these end consumers like Blinkit who have different maybe use cases overlapping, and they're not forced to pick one. When you have an open architecture, they can really put together best-of-breed. And as these projects evolve, they can continue to monitor it and then make decisions and continue to remain agile based on the landscape and how it's evolving. >> So the agility is a key point. Flexibility and agility, and time to valuing with your data. >> Yeah. >> All right. Wen, I got to get in to why the Presto is important here. Where does that fit in? Why is Presto important? >> Yeah. For me, it all comes down to the use cases and the needs. And reporting and dashboarding is not going to go away anytime soon. It's a very common use case. Many of our customers like Blinkit come to us for that use case. The difference now is today, people want to do that particular use case on top of the modern data lake, on top of scalable, inexpensive, low cost storage. Right? In addition to that, there's a need for this low latency interactive ability to engage with the data. This is often arises when you need to do things in a ad hoc basis or you're in the developmental phase of building things up. So if that's what your need is. And latency's important and getting your arms around the problems, very important. You have a certain SLA, I need to deliver something. That puts some requirements in the technology. And Presto is a perfect for that ideal use case. It's ideal for that use case. It's distributed, it's scalable, it's in memory. And so it's able to really provide that. I think the other benefit for Presto and why we're bidding on Presto is it works well on the data lakes, but you have to think about how are these organizations maturing with this technology. So it's not necessarily an all or nothing. You have organizations that have maybe the data lake and it's augmented with other analytical data stores like Snowflake or Redshift. So Presto also... a core aspect is its ability to federate or connect and query across different data sources. So this can be a permanent thing. This could also be a transitionary thing. We have some customers that are moving and slowly shifting their data portfolio from maybe all data warehouse into 80% data lake. But it gives that optionality, it gives that ability to transition over a timeframe. But for all those reasons, the latency, the scalability, the federation, is why Presto for this particular use case. >> And you can connect with other databases. It can be purpose built database, could be whatever. Right? >> Sure. Yes, yes. Presto has a very pluggable architecture. >> Okay. Here's the question for the Blinkit team? Why did you choose Presto and what led you to Ahana? >> So I'll take this better, over this what Presto sits well in that reach is, is how it is designed. Like basically, Presto decouples your storage with the compute. Basically like, people can use any storage and Presto just works as a query engine for them. So basically, it has a constant connectors where you can connect with a real-time databases like Pinot or a Druid, along with your warehouses like Redshift, along with your data lake that's like based on Hudi or Iceberg. So it's like a very landscape that you can use with the Presto. And consumers like the analytics doesn't need to learn the SQL or different paradigms of the querying for different sources. They just need to learn a single source. And, they get a single place to consume from. They get a single consumer on their single destination to write on also. So, it's a homologous architecture, which allows you to put a central security like which Presto integrates. So it's also based on open architecture, that's Apache engine. And it has also certain innovative features that you can see based on caching, which reduces a lot of the cost. And since you have further decoupled your storage with the compute, you can further reduce your cost, because now the biggest part of our tradition warehouse is a storage. And the cost goes massively upwards with the amount of data that you've added. Like basically, each time that you add more data, you require more storage, and warehouses ask you to write the data in their own format. Over here since we have decoupled that, the storage cost have gone down. It's literally that your cost that you are writing, and you just pay for the compute, and you can scale in scale out based on the requirements. If you have high traffic, you scale out. If you have low traffic, you scale in. So all those. >> So huge cost savings. >> Yeah. >> Yeah. Cost effectiveness, for sure. >> Cost effectiveness and you get a very good price value out of it. Like for each query, you can estimate what's the cost for you based on that tracking and all those things. >> I mean, if you think about the other classic Iceberg and what's under the water you don't know, it's the hidden cost. You think about the tooling, right, and also, time it takes to do stuff. So if you have flexibility on choice, when we were riffing on this last time we chatted with you guys and you brought it up earlier around, you can have the open formats to have different use cases in different tools or different platforms to work on it. Redshift, you can use Redshift here, or use something over there. You don't have to get locking >> Absolutely. >> Satyam & Akshay: Yeah. >> Locking is a huge problem. How do you guys see that 'cause sounds like here there's not a lot of locking. You got the open formats, and you got choice. >> Yeah. So you get best of the both worlds. Like you get with Ahana or with the Presto, you can get the best of the both worlds. Since it's cloud native, you can easily deploy your clusters very easily within like five minutes. Your cluster is up, you can start working on it. You can deploy multiple clusters for multiple teams. You get also flexibility of adding new connectors since it's open and further it's also much more secure since it's based on cloud native. So basically, you can control your security endpoints very well. So all those things comes in together with this architecture. So you can definitely go more on the lakehouse architecture than warehousing when you want to deliver data value faster. And basically, you get the much more high value out of your data in a sorted template. >> So Satyam, it sounds like the old warehousing was like the application person, not a lot of usage, old, a lot of latency. Okay. Here and there. But now you got more speed to deploy clusters, scale up scale down. Application developers are as everyone. It's not one person. It's not one group. It's whenever you want. So, you got speed. You got more diversity in the data opportunities, and your coding. >> Yeah. I think data warehouses are a way to start for every organization who is getting into data. I don't think data warehousing is still a solution and will be a solution for a lot of teams which are still getting into data. But as soon as you start scaling, as you start seeing the cost going up, as you start seeing the number of use cases adding up, having an open format definitely helps. So, I would say that's where we are also heading into and that's how our journey as well started with Presto as well, why we even thought about Ahana, right. >> (John chuckles) >> So, like you mentioned, one of the things that happened was as we were moving to the lakehouse and the open table format, I think Ahana is one of the first ones in the market to have Hudi as a first class citizen completely supported with all the things which are not even present at the time of... even with Presto, right. So we see Ahana working behind the scenes, improving even some of the things already over the open-source ecosystem. And that's where we get the most value out of Ahana as well. >> This is the convergence of open-source magic and commercialization. Wen, because you think about Data as Code, reminds me, I hear, "Data warehouse, it's not going to go away." But you got cloud scale or scale. It reminds me of the old, "Oh yeah, I have a data center." Well, here comes the cloud. So, doesn't really kill the data center, although Amazon would say that the data center's going to be eliminated. No, you just use it for whatever you need it for. You use it for specific use cases, but everyone, all the action goes to the cloud for scale. The same things happen with data, and look at the open-source community. It's kind of coming together. Data as Code is coming together. >> Yeah, absolutely. >> Absolutely. >> I do want to again to connect on another dot in terms of cost and that. You know, we've been talking a little bit about price performance, but there's an implicit cost, and I think this was also very important to Blinkit, and also why we're offering a managed service. So one piece of it. And it really revolves around the people, right? So outside of the technology, the performance. One thing that Akshay brought up and it's another important piece that I should have highlighted a little bit more is, Presto exposes the ability to interact your data in a widely adopted way, which is basically ANSI SQL. So the ability for your practitioners to use this technology is huge. That's just regular Presto. In terms of a managed service, the guys at Blinkit are a great high performing team, but they have to be very efficient with their time and what they manage. And what we're trying to do is provide leverage for them. So take a lot of the heavy lifting away, but at the same time, figuring out the right things to expose so that they have that same flexibility. And that's been the balancing point that we've been trying to balance at Ahana, but that goes back to cost. How do I total cost of ownership? And that not doesn't include just the actual querying processing time, but the ability for the organization to go ahead and absorb the solution. And what does it cost in terms of the people involved? >> Yeah. Great conversation. I mean, this brings up the question of back in the data center, the cloud days, you had the concept of an SRE, which is now popular, site reliability engineer. One person does all the clusters and manages all the scale. Is the data engineer the new SRE for data? Are we seeing a similar trajectory? Just want to get your reaction. What do you guys think? >> Yes, so I would say, definitely. It depends on the teams and the sizes of that. We are high performing team so each automation takes bits on the pieces of the architecture, like where they want to invest in. And it comes out with the value of the engineer's time and basically like how much they can invest in, how much they need to configure the architecture, and how much time it'll take to time to market. So basically like, this is what I would also highlight as an engineer. I found Ahana like the... I would say as a Presto in a cloud native environment, or I think so there's the one in the market that seamlessly scales and then scales out. And further, with a team of us, I would say our team size like three to four engineers managing cluster day in day out, conferring, tuning and all those things takes a lot of time. And Ahana came in and takes it off our plate and the hands in a solution which works out of box. So that's where this comes in. Ahana it's also based on open-source community. >> So the time of the engineer's time is so valuable. >> Yeah. >> My take on it really in terms of the data engineering being the SRE. I think that can work, it depends on the actual person, and we definitely try to make the process as easy as possible. I think in Blinkit's case, you guys are... There are data platform owners, but they definitely are aware of the pipelines. >> John: Yeah. >> So they have very intimate knowledge of what data engineers do, but I think in their case, you guys, you're managing a ton of systems. So it's not just even Presto. They have a ton of systems and surfacing that interface so they can cater to all the data engineers across their data systems, I think is the big need for them. I know you guys you want to chime in. I mean, we've seen the architecture and things like that. I think you guys did an amazing job there. >> So, and to adding to Wen's point, right. Like I generally think what DevOps is to the tech team. I think, what is data engineer or the data teams are to the data organization, right? Like they play a very similar role that you have to act as a guardrail to ensure that everyone has access to the data so the democratizing and everything is there, but that has to also come with security, right? And when you do that, there are (indistinct) a lot of points where someone can interact with data. We have... And again, there's a mixed match of open-source tools that works well, as well. And there are some paid tools as well. So for us like for visualization, we use Redash for our ad hoc analysis. And we use Tableau as well whenever we want to give a very concise reporting. We have Jupyter notebooks in place and we have EMRs as well. So we always have a mixed batch of things where people can interact with data. And most of our time is spent in acting as that guardrail to ensure that everyone should have access to data, but it shouldn't be exploited, right. And I think that's where we spend most of our time in. >> Yeah. And I think the time is valuable, but that your point about the democratization aspect of it, there seems to be a bigger step function value that you're enabling and needs to be talked out. The 10x engineer, it's more like 50x, right? If you get it done right, the enablement downstream at the scale that we're seeing with this new trend is significant. It's not just, oh yeah, visualization and get some data quicker, there's actually real advantages on a multiple with that engineering. So, and we saw that with DevOps, right? Like, you do this right and then magic happens on the edges. So, yeah, it's interesting. You guys, congratulations. Great environment. Thanks for sharing the insight Blinkit. Wen, great to see you. Ahana again with Presto, congratulations. The open-source meets data engineering. Thanks so much. >> Thanks, John. >> Appreciate it. >> Okay. >> Thanks John. >> Thanks. >> Thanks for having us. >> This season two, episode two of our ongoing series. This one is Data as Code. This is theCUBE. I'm John furrier. Thanks for watching. (gentle music)

>>Yeah, yeah. >>Hello and welcome back. You're just in time for our third session spot. I Q amplify your insights with AI in this session will explore how AI gets you to the why of your data capturing changes and trends in the moment they happen. >>You'll >>start to understand how you can transform your data culture by making it easier for analysts to enable business users to consume insights in real time. >>You >>might think this all sounds too good to be true. Well, since seeing is believing, we're joined by thought spots. Vika Scrotum, senior product manager. Anak Shaped Mirror, principal product manager to walk you through all of this on MAWR. Over to you actually, >>Thank you. Wanna Hello, everyone. Welcome to the session. I am Action Hera, together with my colleague because today we will talk to you about how spot I Q uses a. I to generate meaningful insights for the users Before we dwell into that. Let's see why this is becoming so important. 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I'm super excited to announce Partake you is now available on embrace so our automated A insights engine can run queries life and in database on these datasets so you do not need to bring your data to thoughts about as you connect your data sources. Touch Part performs full indexing value to the data you have selected, not just the headers in the material and as you run sport in Q, it optimizes and run efficient queries on your data warehouse on. I am super pleased to introduce you. This new spot like you monitor the spot that you monitor will enable all your users to keep track of their key metrics. Spartak, you monitor will not only provide them regular updates off their key metrics, but we also analyze all the underlying data on related dimensions to help them explain. What is leading to the change of a particular metric monitor will also be available on your mobile app so that you can keep track of your metrics whenever and wherever you go, because will talk for further detail about this during the demo. So now let's see Spartak in action. But before we go there, let's meet any. Amy is an analyst at a global retail about form. Amy is preparing for her quarterly sales review meeting with the management, so Amy has to report how the sales has meat performing how, what, what factors lead to the change in the sales? And if there are any other impressing insights, which everyone should off tell to the management? So but this Let's see how immigrant use part like you to prepare for the meeting. So Amy goes to that spot, chooses the sales data set for her company. But before we see how many users what I Q to prepare for the meeting. I just wanted to highlight that all this data which we're going to talk about is residing in Snowflake. >>So >>Touch Part is going to do a life query on the snowflake database on even spot. A Q analysis will run on the Snowflake databases, so we'll go back and see how you can use it. So Amy is preparing for the sales meeting for 2019. We just ended. So images right Sales 2019 on here. She has the graph of the Continent tickets, >>so >>what she does is immediately pence it >>for >>the report. She's creating Andi now. This graph is available >>there now. >>Any Monnet observed >>that >>the Q four sales is significantly higher than Q >>three, so >>you she wants to deep dive into this. So she just select these two data points and does the right click and runs particularities. So now, as we talked earlier, Spartak, you recommends which columns Spartak Things Will best explains this change >>on. >>Not only that, you can look that Spartacus automatically understood that Amy is trying toe identify what led to this change. So the change analysis we selected So now with this, >>Amy >>has a bit more business context when he realizes that she doesn't want to add these columns. So she's been using because she thinks this is too granular for the management right now. >>If >>she wants, she can add even more columns. All columns are available for her, and she can reduce columns. So now she runs 42 analysis. So while this product Unisys is running, what the system will do with the background, this part I Q will drill across all the dimensions, which any is selected and try to explain the difference, which is approximately $10 million in sales. So let's see if Amy's report is ready. Yeah, so with this, what's product you has done is protect you has drilled across all dimensions. Amy has selected and presented how the different values in these dimensions have changed. So it's product. You will not only tell you which values in these dimensions have changed the most, but also does an attribution that how much of this change has led to the overall change scenes. So here in the first inside sport accuse telling that 10 products have the largest change out of the 3 45 values and the account for 39% increase. Overall, there has been look by the prototype category. It's saying that five product types of the largest change out of the 15 values, and they account for 98.6% of total increase. And they're not saying the sailors increased their also demonstrating that in some categories the sales has actually decreased to ensure the sales has decreased. Amy finds this inside should be super useful so immediately pins this on the same pain, but she was preparing for and she's getting ready with that. Amy also wants to dig deeper into this inside. My name goes here. She sees that spot. I Q has not only calculated the change across these product types, but has also calculated person did change. So Amy immediately sorts this by wasn't did change. And then she notices that even though Sweater as a category as a prototype, was not appearing in the change analysis but has the most significant change in terms of percentage in comparison to Q two vs Q four. So she also wants to do this so she can just quickly change the title. And she can pin this insight as well under spin board for the management to look at with this done. Now, Amy, just want to go back to this sales and see if she can find anything else interesting. So now Amy has already figured out the possible causes. What led to the increase in sales? So now, for the whole of 2019, as this is also your closing, Amy looks, uh, the monthly figures for 2019, and she gets this craft now. If Amy has to understand, if there is an interesting insight, she can dig into different dimensions and figure out on her own or immigrant, just click on this product analysis. That's product immediately suggest all the dimensions and measures immigrant analyze sales by Andi many. We will run this What will happen is this barbecue system will try to identify outliers. The different trend analysis Onda cross correlation across different measures. So Amy again realizes that this is a bit too much for her. So she reduces some of these insights, which she thinks are not required for the management right now from the business context and the business meeting. And then she just immediately runs this analysis. So now, with this, Amy is hoping to get some interesting insights from Spartak, which immigrant present to her management meeting. Let's see what sport gets for her. So now the Alice is run within 10 seconds, so spot taken started analyzing. So these are the six anomaly sport like you found across different products, where their total sales are higher than the rest. He also founded Spot. I just found eight insights off different product types which has tired total sales and look across these enemy sees that oh jackets have against the highest sales across all the categories in December as well. Amy wants toe been this to the PIN board on M. It moves further now. Amy's is that it has also shown Total Country purchased their product a me thinks this is not a useful insights. Amy can get this feedback. The system and system asked, Why are you saying you don't find this useful so the system can remember? So you can also say that anomalies are obvious right now and give this feedback and the system will remember. In addition, Amy finds that the system has automatically correlated the total sales in total contrary purchase. Amy Pence this as well to the pin board. Andi. She loves this inside where she she is that not only the total sales have increased, but total quantity purchases have increased a lot more on their training, opposed as well. So she also opens this now anything. She is ready for her meeting with the management. So she just goes and shares the PIN board, which she just created with the management. And you know what happens immediately? The jacket sales category Manager Mr Tom replies back to Amy and says in the request, Any d really like this? So now we will see how Spartak you can help any educators as request doesn't mean really need to create these kind of reports every month to cater toe Tom's request. So with this, I will handle it because to take us walk us through How spot that you can cater this request. Hi, >>everyone. So analysts like Amy are always flooded with such requests from the business users and with Spot and you monitor. Amy can set up everyone who needs updates on a on a metric in just a few simple steps and enable them to drag these metrics whenever and wherever they want. And north of the metrics, they also get the corresponding change analysis on the device off their choice with hot Spot. What I give money being available on both Web and the mobile labs. So let's get started with the demo will be set up a meet and go to the search tab and creator times we start for the metrics you want to monitor, right? And please know if the charges already created is already created. All is available is, um, usually a section in a PIN board. Also dancer. Then there's no need to create a new child. She can simply then uh, right click on the chart and select moisture from the menu, which then shows, which then shows the breakdown off the metric he's going to monitor, including the measure. What it's been grouped by on what it is filtered on. Okay, and also as this is a weekly metric, all the subscribers are going to get a weekly notification for this metric had been a monthly metric. Then the notifications would have been delivered on a monthly cadence. Next she can click on, continue and go to the configure dimensions called on Page. Here A is recommending what all dimensions could best being the change in this metric, she can go ahead with default recommendation, or she can change the columns as she seems very she can click, she conflict, continue and go to the next page, which is the subscriber stage. It is added by default to the subscriber, but she can search everyone who needs update on this metric and add them on this metric by clicking confirmed, she'll see a toast message on the bottom of the page, taking on which will take a me to this page, which is a metric detail page On the top of this page, we can see the movement of the metric and how it is changing over time, 92 you can see that the Mets jacket, since number has increased by 2.5% in the week off 23rd of December has compared toa the week off 16th of December and just below e a has invaded the man is generated in sites which are readily available for consumption. Okay to discharge. Right here says that pain products have the largest change out of all the 28 values and contributes to the 88% of the total increase in the same. And this one right here is that Midwest is the larger Midwest has the largest change and accounts for 55.66% off the total increase. Now, all this goodness is also available on the mobile lab. Right? So let me just show you how business users are going to get notified on the based. On this metric, all the business users who are subscribed to this metric are going to get a regular email as well as push notifications on the mobile lab. And when the click on this, they line on a metric detail page which has all the starts, which I just showed you on the on the bed version, okay. And one cyclic on back burden. They land on this page, which is a monitor tab, and it summarizes all the metrics Which opportunity monitoring and gives them a whole gave you to stay all I want to stay on top of their businesses. Okay. Eso that folks was monitor. Now I'll search back to slaves and cover. Summarize the key takeaways. From what? That she and I just don't know. So it's part of you wanted, uh, Summit Spartak you. It automatically discovers insights and helps you unless the full potential of your data and that's what I do is comprehensive set off analysis. You can answer your advanced business question in just a few simple steps and the end speed of your time. Bring state. And with a new support for embrace, you can run sport like you on your data in your data warehouse and with spotted you monitor, you can monitor all the business metrics and not just died. We can also understand that teaching teaching drivers on those metrics on the platform of your choice. So with that, I'll hand over toe, you know. >>Thank you so much. Both of you That was fantastic. Um, I just love spot like, because it makes me look like much more of a rock star with data than I really am. So thank you guys for that fantastic presentation. Um, so we've got a couple of minutes for a couple of questions for you. The first one is for action. Um, once spot I Q generates a number of insights. Can you run spot I Q again on one of those insights? >>Yeah, As a philosophy off Spiric, you sport like you never takes the user to the dead end Spartak. You also transparently shares the calculation. So user can not only the keeper that on edit Understand how this product you inside has been calculated, but user can also run us for like you analysts is honest for data analysis as well. Which music? And continue to do not on the first level. Second level in the third level as well. >>That's cool. Thank you. Actually on then The next one is for because for spot ik monitor is it possible to edit the dimensions used for explaining the factors to change that was detected? >>Yes. It's an owner of the metric you can change the dimensions whenever you want and save them for everyone else. >>Okay, well, I think that's about all we've got time for in this session. So all that remains is for me to say a huge thank you to Because an Akshay Andi, we've got the last session of this track coming up in a few minutes. So grab a snack. Come right back and listen to an amazing customer story with Snowflake on Western Union, they're up next.