Name: Unlock Trusted Workday Data in Snowflake – Without Building Another Pipeline
Uploaded: 2026-08-18T17:19:22.643Z
Duration: 57 min 26 s
Description: Unlock Trusted Workday Data in Snowflake – Without Building Another Pipeline

Transcript for "Unlock Trusted Workday Data in Snowflake – Without Building Another Pipeline": Hi. Thanks for joining, us today. We are excited, Workday and Snowflake, to be running a webinar together on unifying people and money data for analytics, decisions, and agents. I'm excited to be joined today by Chandra Nayak, principal partner solution engineer at Snowflake, Arshana Bonzel, senior manager of Workday data cloud product at Workday. And my name is Brian Maribel. I'm one of the principal product marketing leads here at Workday on Workday data cloud. Just wanted to give a quick, you know, insight into that. The the Workday Data Cloud and some of the products we're talking about today are currently in EA, and and some of the release dates are subject to change. So what we will cover today, we're excited to introduce, give some early introductions into Workday data cloud. We are going to talk about some of the top use cases for Workday data cloud, both date in and data out with Snowflake. And we're gonna go through some demos and see it in action, and then how to how to get started in EA, and then also a a bit of q and a at the end if we have time. So with that, I'm gonna hand it over to Archana Bansal, and we're excited to have you here today. Thank you so much for joining us. And Archana, take it away. Thank you, Brian. Alright. I'm gonna start with oh, let me get my slides right. Hi, everyone. I'm Archana Bansal, and I'm from Workday Data Cloud product and very excited to share all about the product today. Let's start with how the work of IT teams has changed over time. So when we speak to a a lot of the IT teams, there are three consistent themes that emerge. The first, and which is apt with the latest AI emergence, is I need to safely adopt AI. Everyone has an AI mandate. There is immense pressure to deploy AI, but it has to be done safely. The concern I hear most often is governance. How do I adopt AI? Access, data needs to come together with context. And the third one is I need to simplify my tech stack, legacy architectures or creating operational friction. The goal is to free the IT teams from managing the technical debt so they can partner with the equation. The role of the IT teams has evolved from managing rigid technology stacks to architecting agility. The pressures are real, but what's important is how Workday is responding to this shift. Welcome to Workday Data Cloud. What you can see here is Workday Data Cloud is fundamental evolution in the Workday platform. It's about taking your most trusted people and money data in the enterprise and making it safely usable beyond the core system. On the right, you see Workday Data Cloud playing a key role. From there, your trusted Workday data connects seamlessly to the existing data ecosystem, whether it's analytics platforms, the data lakes, or external BI tools of your choice. This is not about pulling data out and losing control, really. It's all about connecting Workday into the enterprise in a governed, intentional way. So here's a high level view of all of the different components and the partners we support, which we will be supporting in the GA like Brian mentioned. This new product suite is built on zero architecture that connects Workday with your modern data stack. This makes your Workday data securely accessible for AI and analytics with zero data application. Our current list of trusted partners is Snowflake, Databricks, AWS, Google Cloud, and Salesforce. And with the zero copy architectures, those little dotted lines that you still see flowing back and forth between Workday and the data platforms, you'll be able to query not just Workday data, but external data that you can read into Workday data cloud from these data platforms. All of your most important data will be connected inside of Workday and outside of Workday. Today, we are focused on Snowflake. So what what does the zero copy architecture bring to you, really? It brings reduced risks, no copying of the data. There is lower integration complexity, which means no custom integrations and ongoing maintenance of those integrations. Faster time to value, so no ETL, shorter path from data to decisions, and greater value from your existing investments. So you've already invested in these data platforms, your BI tools of your choice, and we just make all of it work together, by bringing the Workday data cloud zero copy architecture to you. At the end of the day, this is about contextualized data connected across your enterprise. So like I said, we're focused on Snowflake today, so I'm gonna we are privileged to have Chandra with us from Snowflake. He's gonna talk about Snowflake a little bit, and then I'm gonna go we're in the components of Forte data cloud. Chandra. Thanks, Ajahn, for this opportunity, and, happy to be here. For those who are completely new to Snowflake, I just wanted to give a brief idea of what is Snowflake a cloud data platform. What does it really, bring to the table? Snowflake started as a cloud data warehouse about twelve years back, which can run on any of the hyperscale cloud. Whether you are on AWS, Azure, or Google, you can run, Snowflake data warehouse there. In fact, you can have a situation where your production is running on a particular cloud and region, and your Doctor could be running on a completely different cloud solution, from the hyperscaler. Now how is Snowflake different than other data warehouses, and what really is the value that Snowflake brings to the table? Unlike the databases prior to Snowflake, which had compute, storage, and memory all coupled together, we're limiting customers in terms of concurrency. You could not bring a whole lot of users to run their workloads on the database because, of the strong coupling between, the resources that are required, to, respond to the, customer queries. Now what Snowflake did differently is decouple the storage and the compute. This allows, each of them to individually scale elastically to, like, infinite scaling of compute and storage becomes possible. Now with that, it allows the customers to continuously bring any amount of data without really paying anything more for the compute, the or the software that runs, that's meaningful for your business application. And, also because the decoupling allows compute to access this data and you can choose different sizes of compute, customers now have the price versus performance control. They can, run different workloads all on the same data without impacting each other. Additionally, Snowflake manages all the admin services for you. Authorization, perform authentication, performance, indexing, All of this is done under the hood for you. So you as a, user of the database don't really have to worry about, in managing indexes and partitions. All of that is done out of the box for you. So you just, like, push the data into Snowflake or share the data with Snowflake and then just run your queries and, it delivers, to you. Next slide, please. Next. Okay. So one of the other things that actually, really was of high value for customers is, essentially, the ability to, share data between various stakeholders. It Snowflake, allowed customers to, share data between data providers and consumers irrespective whether the providers and our consumers are within the four walls of an organization or outside the organization. You don't have to move files anymore or be worried about ETL pipelines breaking or be worried about who's, taking that data and exfiltrating and using it for nefarious reasons. You can govern as to who gets to see what data, and all of this data is live. This allowed thousands of companies to essentially share their data to their vendors, to their partners, and data providers come out of Snowflake Marketplace to monetize that data by sharing that data for interesting parties. You now have the ability to, give access to real time governed ax data to all the stakeholders. Let's go to the next, slide. Yeah. So, Snowflake platform has now evolved from the original, data warehousing platform. It's now a very robust AI data cloud platform. It allows customers to not only have native table format, but also you can now, bring your iceberg table formats, and Snowflake will manage that data for you. Snowflake does provide storage for iceberg data as well. Also, you can, bring the external data through various streaming pipelines that we provide so you can bring data of any variety, of any velocity as well. With the larger business solutions like SAP, one day Salesforce, we have partnership that allows, customers to share the data in both directions, with Snowflake and access Snowflake data back into this application so you can drive your intelligent workflows. You can bring data from traditional, OLTP databases like Postgres and so on. You can also bring semantic understanding of your data, which you probably have stored in your Tableaus and Power BIs. You can actually bring that semantics into Snowflake so you can interact with the data with your business terminologies rather than, like, creating those terminologies again at a data layer and again at the BI layer application a BI layer. They are interchangeable now. You can access Snowflake, data and the context of all that data in your, through your SQL or if you choose to use it, on Python based applications or Scala based applications or you're using AI on top of Snowflake or you're using agents and connecting to external agents using MCP, your data becomes available anywhere and everywhere in within the applications of your choice. With that, I wanna pass it back to Archana to talk about, like, what Bay Area Cloud and how the integration works between the two platforms. Chandra. Alright. You're back with me. So let's look under the hood. What are the different, components of Workday Data Cloud and what what the whole product, or the spiel is all about? So here's a very detailed architecture showing all of the different components that come together to make Workday Data Cloud a reality. And don't worry, I will go deeper into some of these components so you understand them really well. Let's start from the left in this diagram. And this includes the data platforms that support open formats for Workday to be able to establish that zero copy connection to data to these platforms, and Snowflake being one of them. These links to tables from Snowflake then become just like any other tables in Workday Prism product, which you're seeing in the center, except there was absolutely no need to do any tail to get it inside Workday or to see that data inside Workday. From then on, of course, you can join that data to Workday data and do all of the analysis that you want. You can join worker data with commissions data and so on and so forth. We're gonna see some of these examples, today in some of the demos that we have planned for you. So I'll do a few back and forth between screens, but bear with me on that. Let's continue to move to the center from the left here. In the middle, you're seeing components like work core Workday data, which is nothing but all of the core products Workday has, like workforce management, payroll, talent, financials, spend, all of the things that you're very familiar with. And on top of that, we have unified cat data catalog, which is towards the right within the center box, which is a carefully curated list of objects or tables from the core products that we serve in the catalog for all of you to use just like any other tables. So we were able to establish connection inside Snowflake to the data in Workday. So this is the catalog that Snowflake, customer or user would see. And the reverse of what I explained of bringing Snowflake data inside Workday happens here, which means you're able to access Workday tables inside Snowflake through the iceberg, format that Chandra mentioned earlier. That completes the picture of bidirectional zero copy architecture. You can access external data inside Workday, do your analysis here, do your actions on top of that data inside Workday. You can access Workday data in external data platforms, do your analysis there, and do the body of work that you do in those systems. I'm sure you have thousands of questions, and we will take them. But first, let's double click a little on this. I'll walk through each of the some of these main components and demo three main components that we have in Workday Data Cloud. We're gonna start with Workday Data Lake. So in the data lake, you will find the curated tables of the most critical people and money data from Workday, the the curation that I talked about earlier. You can think of, let's say, worker, payroll tables, journal lines, your payments, and invoices tables, all which are already structured, governed, and ready to be queried. That data is stored in the public cloud as files. Think Amazon s three, think GCS storage. And then they are exposed via Apache Iceberg tables, the metadata on top of these files. Apache Iceberg is a groundbreaking technology. It's the open standard for managing big data, and it powers the biggest technology infrastructure on the planet today, including Workday Data Cloud. By embracing this open standard, we get interoperability across all of our partner platforms. Iceberg allows for queries to be quickly and consistently pinpoint to the exact data that you need before you do the work. It's incredible. And what that unlocks is astonishing performance gains. The query gets just what it needs just in time. So for performance at scale, inside Workday or outside Workday, this is going to be a huge game changer. When you only get what you need, you don't have to play the ETL game anymore. Now the trade off is that the data in the data lake is materialized. What that means is from the curated catalog that I talked about earlier, you get to choose what tables you want to put in your data lake, and you get to choose what refresh schedule you want. We do all the work up front after that configuration is done on behalf of the customers to make sure those tables of data are structured, governed, ready to be queried, and they are coming to the data lake at the refresh frequency that you asked for. It could be a week. It could be a day. It could be several hours. You get to pick that schedule. So for exploratory AI and analytics, this is the way to go. So I'm gonna stop here for a demo. So let me switch my screens. Alright. Hopefully, you're seeing my screen. I'm gonna start the demo. Hi. In this demo, I'm gonna be showing you how you can access Workday data in Snowflake. I'm in Snowflake's homepage, and here I have an option to go and explore the catalog. If I go click on the catalog, I have options where I can go see the Workday data. It's connected right then and there. When I click on the Workday code, and I see a list of all tables that you can access. Again, this is just read only access, zero copy, the data sits in your Workday instance. Now let's dive into one of these tables. Once you click on one of these tables, you can go to a data preview, you can look at the lineage, and all information. Now, more importantly, you can actually run queries against this. So I'm here trying to spin up Snowflake's cocoa environment. And I'm trying to ask the question, give me all of the compensation related tables from what they've called. And what it does, magically, goes against this catalog and gives you the list of all of these factors. There you go, the power of accessing Workday data in Snowflake using Workday Data Cloud. Thank you. Isn't that amazing? And, one of the things that you saw there was a little link icon on top of, on top of the tables, and that really was the was showing that this is zero copy connection to iceberg tables inside of Snowflake of the Workday data. Let's switch to the next important component. Let's say for some reason your use case doesn't want the data to be old, a day old or several hours old, and that's not going to cut it for you. That's where another data Cloud product really shines. Workday Live Data Query is basically a SQL interface. It sounds complicated, but it's not. It's just SQL. For our customers, though, it's going to be a revelation because we've not done SQL before. We've not done relational tables before. SQL is the language of analytics. SQL is the language of agents, and we're stepping into the agents' world. And for the very first time, we're gonna speak the same language as everybody else. We're opening up a standard SQL connection to Workday transactional data. So the same catalog that I talked about earlier is also accessible through SQL through our Workday LiveData query product. This is live real time data, not the materialized data that was in the lake. So when a live data query runs, it runs directly against your tenant. You send that query back to us, we run it live, and we send back the results. Honoring all the contextual security, which is a must for most of our Workday customers. When freshness matters more than volume, things like operational dashboards or workflows that need to be up to the second up to date, with the full power of Workday contextual security on a per user basis, this is the fast lane. This is achieved via different drivers that we're gonna provide to customers. Think JDBC drivers, think Python drivers, which are also open standards. And what that means is you can't just plug in these drivers into your BI tools of choice. Maybe you use Tableau or Power BI or you're reporting inside Snowflake directly. You want those tools and those users to stay in those, you know, that environment and leverage Workday data right there. With Workday data cloud, you can. It's just SQL. It's live data. No clunky pipelines here honoring the contextual security down to the user level for access control. You choose a tool and we bring the data to you. That's the power of the open AI ready platform that we're building. Now what's really cool about both these technologies, the data lake as well as, data live data query, is JDBC and Apache Iceberg is that the data flows out, but it also flows in. So we'll see that in the next, Prism side of the world, but that's gonna open up some amazing new possibilities for you. I'm gonna step out for a demo again, and this time I'm gonna demo live data query product for you. Let's go. What you're seeing right now is, I'm inside a Snowflake environment and this is live data query. So remember I talked about the drivers? What I have here is a script, a Python script, where I've brought in the Python driver and I've put it inside Snowflake. So don't worry. This is a one time setup what which you will do. That That is you bring the driver, you put it in the system, and then you establish a connection with your Workday tenant. So all of these information are through registering an API service inside Workday, and you provide all of the Workday tenant information here. And once done, that's it. You connect to it. You do everything that you want to do with live transactional data. This is up to the minute fresh from the workday transaction system. So here I'm querying workday, the worker table from the workday live data query connection that I established in the previous steps. I'm seeing worker data. Specifically in this one, I am seeing I I'm querying, a worker with their years of service along with their location in a query. And, really, with Python, sky's the limit. You can go as deep into building your dashboards and KPIs. So I have some examples here. What I've done is built a, a few KPIs and a few dashboards. You can see I've built workforce KPIs here, how many total workers, how many are active, terminated managers, etcetera. And then I have an example of a a dashboard that I've built using Python library. I have hiring and termination trends here. I have labor supply over time, tenure distribution. You name it. You can do whatever you want. And what what additional stuff is, Snowflake has this Cocoa agent where you can query live with the connection you've established. I've already run one query here where I've asked it how many, how many employees do I have in my organization and brought back the number using the LiveDataQuery connection I have in this Python script. So there you go, LiveDataQuery at your service using SQL or using Python, libraries that you are very familiar with. So I'm gonna stop this one, switch back to the last component that we have in Workday Data Cloud. Actually, I lied. I might have two components, but I wanna talk about Prism data management. Sorry. So Prism is a product you're likely familiar with already, that lets you do, ingestion of data, transformation of that data, and bring it to readiness for data cloud. So remember when I said you could bring back to connect to external data inside Workday? This is the product that you do with. So you establish external, connection to a Snowflake table or multiple tables, and then you transform that within, Workday by joining it with, let's say, worker data and doing some analysis on top of that in, a Workday core report or a Workday discovery board of your choice. So all of that is really geared towards, empowering your HR IT teams or your FIN IT teams to create and manage GoWAN datasets as trusted sources of truth, reducing your reliance on central IT. So all of the goodness of Vertech contextual security on top of the Prism data, which exists today, comes to play here as well. Here, I'm gonna switch back again into a demo. So let's do that quickly. Alright. So Hi. it's now Here is a quick demonstration of how you can access Snowflake data within Workday. Just to give you a quick background, with Workday data cloud, you will be able to connect to systems like Salesforce, Snowflake, Databricks, and Google Cloud, which is our tech partners in a zero copy mechanism. In this use case, what I'm going to be showing you is we have employee stock plan information data sitting in Snowflake, And I'll show you how you can access it in Workday and build your analytics. So in Workday, you come in and you'll be able to set up connections to the Snowflake instance and access tables. We call this external tables. And as you can see here, we can refer to all of the employee stock pay data sitting in Snowflake, all in a read only manner with zero copy. Once you access this data, that's pretty much it. You can build your own dashboards and discovery boards. This shows you how you can build a quick discovery board, which is a drag and drop visualization tool with the data that you just referenced in Snowflake. So it shows you things by levels and you can build your own visualization. It's that simple. Here, what we saw is how you can access Snowflake data specifically related to stock plan. It might be for any of your use cases, all in Workday using Workday Data Cloud in a zero copy mechanism. Thank Alright. So what you saw there was the power of bringing all the external data inside Workday and leveraging that to build analytics inside Workday as well. So that completes the picture of externally being able to do things, internally being able to do things, all with no detail at all. So I'm gonna cover the last component really quickly and do one last demo before I hand it over to my friend, Chandra. So we've done all this, like, connection. We've established data. We have all the connections lined up. And but we don't want the agents inferring that context from raw data alone. That's gonna be probabilistic. That's not a good agent. So what Workday Data Cloud's context and knowledge layer is gonna do is it'll encode the relationships between all of these tables. So people, money, art, financials, roles, skills, etcetera. And I when when I said that at the beginning, Workday data models exactly how your organization is run. So these are setups that you have done. This is what I'm talking about. The semantic context or the knowledge layer will be a series of purpose built models that provides the ground truth, the meaning behind all of your data based on about two decades of business logic that we've baked into Workday. The business logic you trust to manage your most important assets, your people and your money. That's the context you quite literally can't find anywhere else. So let's see one last demo, which is to show a, show a agent working inside Workday. So I'm gonna switch. Okay. Alright. So I'm here inside Workday in, Workday Extend Pro. It's a small pitch for extend product where you can build agents inside Workday. I'm in a developer agent where you can put natural language and, it builds the agent behind the scenes for you. So I want to build a sales performance agent. I'm going to build, give it a prompt saying I want an agent that fetches data from Snowflake, blends it with worker data, worker information, generates a data source that is used to answer questions like identify my top sales performers. Can you tell me the skills of top performers and assign those learnings to those, workers in Workday? What it does now is goes behind the scenes and builds that agent for me. So the ones who are savvy enough in the audience would be able to see the left hand panel shows the skills that this developer agent built behind the scenes for my sales performance agents. So I can now see it can blend data. It can identify all of the things that I asked for it to do. Once I've done that, I can see all of this agent in action inside Workday. So I'm gonna deploy with the deploy button at the top. It's deploying to the tenant. And once it's deployed, I can come into Workday as myself and interact with the sales performance agent, of course, if I have access to it. So I'm gonna ask it a few questions here. I'll say, can you find a sales rep that have low CSAT and low commission attainment this quarter? So it's gonna bring back the results to me. So I've I'm seeing Pedro, Jacqueline, and Betty are somewhat low on CSAT and attainment rate. So I'm gonna ask you to follow-up question. For each of these, can you tell me what learning modules have they completed and what are recommended one? Now note that these recommended one are ones are coming from the top performing agents. So it's gonna bring back that information to me as well. It's gonna say Pedro did this, recommended next would be nice. Same thing for Jacqueline and Betty. And what this then does is just assigns these learnings to these workers inside Workday without me having to do anything manually to assign these learnings or figuring out what they should be doing. So here's the power of, creating an agent with Workday data along with the Snowflake data that I could access from within Workday using the Workday data cloud product zero copy architecture. So let's see all of this at action in Snowflake now. I'm gonna hand it over to Chandra to talk through a use case in Snowflake and maybe demo it live. Chandra? Thanks, Archana. One quick second. Okay. Customers have been able to bring various forms of data at various velocities within Snowflake and have been able to create customer three sixty to drive, business insights to take actions. However, now being able to directly connect business applications to Snowflake with Zio ETL, all of that is, like, is in the agentic AI world. We can do it in real time. We are hearing very interesting use cases. Customers are thinking about being now being able to bring first party operational data, customer data, people data, finance data, all on a single platform or being able to share this data across platforms, and drive insights while also drive actions back into these applications opens up many interesting use cases. Specifically in the space of people and finance, we have heard these use cases from our top customers for both Workday and Snowflake. What customers are now thinking about is, how do I combine, my first party data and third party data that's available from the marketplace or web behavioral data to identify and, hire the best talent for the organization. And, customers can customers are also thinking about being able to predict which employee is likely to churn and and come up with effective policies to avoid employee churn. Customers can proactively connect, customer sentiments about an organization with the team performance and identify scope of improvement for the employees so that you can improve customer sentiment as well. We see, many business up businesses are trying to improve their sales strategy by matching their talent to the specific product and performance and by the region, by the region. We we see, businesses being able to identify value a a employee brings to the business and identify opportunities for providing additional education for, certain employees just like Archana showed in the demo. The outcome all these biz businesses are now trying to drive is to unify the data that is generated within the organization and data that's available in the marketplace and and which, understand create a digital three sixty of the business and improve, the customer, customer responses by not just having a traditional dashboard, but also now AI enabled, business actions that provide a better ROI for, for the organization. With that, I wanted to show a quick demo of a resort that, actually saw very high, busy activity during the World Cup soccer, tournament. And let me share my screen to make that, show this quick demo. This is a live demo, and I'm hoping you're now able to see my screen. Okay. Here is a Snowflake co work interface. Essentially, Snowflake agents can be accessed through multiple different product interfaces. A i.snowflake.com is, our, like, chat interface through which we can interact with these agents. You can access first party data, third party data. You can access structured or on you can ask questions about your structured and unstructured data. So in this case, we have, data about a resort, which actually has, a resort company which has results across The US, and they are seeing increased activity because of the World Cup soccer matches happening around them. And, we'll see, we they they're bringing customer data, sharing customer data to Snowflake. They're sharing supply chain data to Snowflake, and, also, they are sup sharing what they, employee data to Snowflake. And, they're they're, bringing all this data together to be able to derive insights and give access to the people managers, to the supply chain managers so that, they can offer a memorable experience for, their guests. So here, I'm basically gonna ask some of the questions that a supply chain manager would ask because he's concerned that they're they're able to meet the requirements and, like, do have, drive experience for the, for the guest. So, first thing from a supply chain perspective, the result is worried about, like, are there any delays in terms of, like, is the supply chain delivered on time, and are they able to okay. I want this is setting the okay. So here you go. I've asked a question to an agent, and the agent is verifying whether this question can be responded successfully. It uses the semantic views to map what you're asking to the data that's available, and then it's essentially going and check, converting your ask into a SQL query. And you can see it actually identifies that these are the results, and, there are certain delayed shipments, across all these results. You can ask any questions in the live fashion. So I can say, okay, which, which, products are getting delayed? So, not only does Snowflake, gets the data in, like, a readable table format, it also provides explanation on how actually it came about with a response and gives you insights on Waco, where you can immediately take actions. And I can also see here what specific equipments are getting delayed and, what might be the impact of it to the organization. I can again, because of this increased activity, this particular result will now have to hire new people. So, I might want to ask, like, what is the hiring policy for temporary workers when we are seeing increased demand? Now the hiring policies could be stored as PDF documents in, like, some Microsoft SharePoint. I should, you can bring you can share those files to Snowflake. Snowflake can index those, and you should be able to ask, natural language questions to that as well. And here, it's now generating a response based on the unstructured data that's being analyzed and vectorized. And it comes up with the the policies that we have to enter to hire these. And finally, I might wanna say, okay. Hey. Go find me all this employ find me this candidate, who satisfy these conditions so that I can quickly go and hire and ramp up my, ramp up my workforce. And this data could be coming from Workday. So so now this is essentially going and, accessing the data that is is shared through the Workday data cloud, which Ajna was earlier talking about. So the data still remains in in Workday data cloud. It's not we are not making a second copy of that data. We directly just go directly access that data. And in case if your question is not complete, codecs Snowflake Cocoa is gonna basically ask you additional questions so that it can contextualize your question and give an appropriate response for you. So, what you essentially saw is the ability to bring data from various business applications, your first party operational data, your unstructured data. All of that becomes available through, to Snowflake. You can create new additional semantic, views. You can create new harmonization layer and essentially create that analytical layer that can be now, providing, insights to the business, while these insights also can be surfaced back into applications like Workday because you have to take those actions within these applications. With that, I'll hand it over back to, Archana. Actually, give it to Brian. Awesome. Awesome. Well, thank you so much, Chandra and Archana. That is I I am always excited when I see the live demos with Workday data cloud and agents both in the Workday side and on the Snowflake side. One of the things I'm actually even really excited about is the is the integration with with Cowork and Coco on the Snowflake side. So please do, if you are a current Snowflake customer, please do play around with that because that is very cool. I'm gonna post some links here for resources. On both our Workday community page, we have some immediate kind of resources that can get you started. If you're interested in learning more, I'm posting them there on the chat. I believe they came through. Yep. My my my first link just came through at the top. And then if you would like to, like, submit an application for EA because we are in early adopter with Snowflake. We have limited spots, so we can't guarantee that you'd be in in EA, but I've just also posted a couple links. So the first is the community page where you can learn more, and then the second is the EA interest form. And like I said, we will be we we are planning for GA here in round two of 2026. Anything subject to change, in in the in the day and age of AI and and the fast innovation we're all experiencing. But please feel for please go and reach out to those links and and learn more. And, also, what I wanted to share is is let's say, you know, you're excited about this. You wanna get started. You're a Workday customer. So what are the paths to data cloud? Well, right now, there are two paths to it. If you're already on the public cloud, this is means you're you're you're primed for being able to adopt Workday data cloud. So it's it's an upgrade here in the midterm through, a SKU upgrade and, like, speak with your account team, and they can, one, get started quoting and pricing with you. But then they can also, help help navigate even some use cases that you could you could, use Workday data cloud with. So maybe you're you're interested in this, but you're kinda going, okay. We've I think we've got some use cases for this. Not sure. You know, speaking with the account team, we're also built great partnerships across with Snowflake. So we can we can even, together with Snowflake, come up with come up with some use case ideas that this can help you, your business, and your innovation in AI. Let's say you're not yet on the public cloud. Well, the exciting thing is that, getting started or just reaching out to your account team and interested in Workday data cloud creates a faster path to the public cloud migration. So what you'll wanna do is if you're on the private cloud, same thing, reach out to your account team and just let them know, hey. You know, like, really interested in this. We'd like to pursue the public cloud migration because of what Workday data cloud provides, and your account team can navigate kind of a faster path to migrating to the public cloud. With that, we're gonna kick it off on q and a. So I'm gonna invite Archana and Chandra back on here, and we've got quite, an active q and a. So we've got some time to to dig into some of these things. So, Archana and Chandra, if you're ready, I'm gonna I'm gonna fire some questions off. Let's let's first get started on, you know, unpack a little bit, you know, for those I I know a lot of our audience here is familiar with zero copy. But for those who who might not be as familiar with zero copy, can you unpack the security and governance, kind of construction that we have here and and maybe speak a little bit to how that's happening, how we keep the data residency, how we keep governance and access controls in place for data cloud. So I'll I'll feed it to either one of you to to sort of kick that one off. Yeah, I'll I'll take a pass and Chandra can chime in. So like I said, there are three broad products that we talked about. Workday live data query is. Will honor all of the contextual security that you have in Workday, meaning you could log in as yourself using that product and it'll show you exactly what you should be seeing inside a workday system. So that's that's for live data query. For data lake, we materialize the data lake using a more open, open access, and then you can then further use, I'm gonna get a little technical here. We have Polaris catalog as our catalog that supports the data lake Apache Polaris. And then you can do some table level access control within Polaris, the user management. Once that is done, that actually guards which tables get access to in the downstream data data platforms and Snowflake in this case, once the user logs in, but then further on on top of the table level access, controls that the Polaris provides, users or data admins can do RBAC inside Snowflake. So whatever role based access control mechanisms are present in the data platforms will further be the guardrail of who gets to access what, in the data lake system, on top of the data lake product. And the last one, Prism, is not a brand new thing. All of the Workday, security, the configurable security that Prism product provides, the same applies to all of the things that we bring in from external data platforms inside Prism, as part of the bringing data inside of the world. And, Ajna, you are on point there. This is an evolving space, so, there are many interesting things that, both there and Snowflake can work together, and and for the enhanced capabilities in this, space. Oh, thanks so much for both of you. You know, it's you spoke Archana, you spoke a little bit about the the zero copy and direct access. So I'm gonna move over to some architectural questions around around zero copy. So, maybe maybe unpack a little bit, like, does does zero copy mean we can't materialize the data from Workday in Snowflake? If I if I read this correctly, meaning, you know, the the the data from Workday on the Snowflake side, is it able to be used into Snowflake in any way, or is it just able to be seen? And then if so, how do you account for, like, the performance of of reporting models where it needs to report on those sources, like, where it's getting the data from? And maybe, Chandra, this is a question from you on the. Snowflake side. Yeah. Totally. And, given I have worked on, other zero copy integrations with the major SaaS application providers, I can bring, that experience since hundreds of customers are on GA with zero copy with other major SaaS applications. What we have seen is the performance on shared iceberg tables is within one or two percentage points compared to Snowflake nature tables. So the performance of data that is being shared from Workday to Snowflake will be very, very close to having that data copied over to Snowflake, and you run the queries, directly there. The reason because the data is generally on this hyperscale storage, and you're accessing them through, extremely fast fiber backbone. So, the performance is really good. The second question regarding, can I materialize that data? Can I use that using various tools Snowflake offers? Absolutely. You should be able to create dynamic tables. You should also be able to create streams so that you have changed the capture from workday data in Snowflake. You should, be able to use, whether you wanna use, Snowflake data in your silver layer or gold layer. You want to, you wanna, I guess, access using machine learning notebooks, you should be able to use any tool that's available on Snowflake to, access Workday data, whether it's available through, Workday, LiveData query or whether it's accessible through Workday data cloud. You know, that. that and oh, Good. Arshorna, Just just it. No. Go for it. just gonna just gonna add on. We talked about iceberg as a technology, open standard. That really is meant for petabytes of data. It it performs really, really well because of all the metadata that Iceberg makes you create on top of the data hyperscaler storage that Chandra mentioned. But no surprise, we are not talking about petabytes of data. So that that just speaks to how well the performance should be using Iceberg, as the ground technology we're using. Well and and on that, one of, one of the folks in our audience posted this question. I think this is a really good one because it can speak to where we are, moving in this agentic era. But the bidirectional nature of zero copy brings forth a lot of overlap or at least what what's seen is a lot of overlap in terms of capabilities and use cases. Like, for instance, between Snowflake, Prism, and agentic features both in Workday, both in the Snowflake side. How should leaders be thinking about differentiating these? And and when to use that data, say, in Workday, when to use that data in Snowflake. Yeah. This is a philosophical question. Right? It's it's it's really, and and not trying to be diplomatic, but but really the answer is where are your users? Where are your users using the system? If they're using the system inside Workday, you get to keep them inside Workday by bring by connecting to the data in Snowflake in in the product that we talked about in detail today? Are your users sitting in an external BI tool and doing a lot of analysis there? You get to connect to Workday data there and do your analysis on top of all of the transformations that you would like to do there. Are your users sitting in Snowflake and doing a lot of their analysis and then further consuming that data? You stay in Snowflake. The the the beauty of the product is or the openness of the standards and the fact that it eliminates ETL is that you can let your users be in the tools of their choice and let them do their work there. If you're building an agent inside Workday, you can. You don't have the restriction of building an ETR and, oh my god, I have to bring all this data to make sure the agent works well. None of that. That that's what zero copy bidirectional empowers the enterprises to do. And and by no means the materialization goes away. If you want to, go for it. But why would you do that? Then zero copy exists. Yeah. I think that's that's the exciting part about the age that we're in is that we can we can really tailor where are our customers, where are our users, and really help bring that that agentic experience to them by having this data available, secure, governed, and and zero copy in both in both places. Okay. Moving on to a little bit of some next step questions here. Is Prism a prerequisite? Like, do you have to be a Prism customer to use data cloud? If you are a Prism customer, how should you think about data cloud? That's a great question because Prism is is an existing product. A lot of our customers are on it. A lot of our customers are maybe not on it. Workday Data Cloud is a brand new SKU, which already bundles the Prism capabilities required for it to work. So you don't have to buy two different SKUs. You buy one SKU, or you invest in one SKU and then take the product as a whole. Now what happens if you're already a Prism customer? There is some path that we will enable for you to get to data cloud if you're very if you're interested, which I believe you would be, to move from your current investment in Prism to Workday data cloud investment. So all of the paths are possible. It's just that you what you wanna take back is you're not gonna buy you're not gonna have to buy two different SKUs. You're not gonna have to be on a particular product to be able to use Workday data cloud. You can start on data cloud. You can start from Prism, move to data cloud. All all combinations Yeah. Yeah. And that, I think, moves possible. Assam folks to, like, questions around pricing. You know, right now, we're currently in EA with with Workday data cloud. So our pricing model is a hybrid pricing model right now with a mixture of, like, number of users plus flex credits. So our our strong advice right now on the pricing front is really reach out to your account teams. They are actively helping helping customers quote and think about pricing for data cloud. That being said, as as as we all know in in early adopter phase, like like, things are things are able to to, you know, move fluidly as as our innovation moves forward. But, yeah, definitely reach out to your account teams on pricing and just, you know, start looking at use use cases and unpacking that. With that, I will also wanted to say, you know, we noticed Chandra left, like, he had he had an engagement. He had to jump off this, but wanted to say thanks to Snowflake for being on it. And with that, we just wanted to say thank you, for joining us today. Thank you, Archana and Chandra, for really diving deep and and showing us, like, what the capabilities are with Workday data cloud both in Workday and in Snowflake, with with Workday in the the AI and agents and with Snowflake in in Cowork and Coco. And we just wanna really encourage everyone, reach out to to account teams, reach out to those links on both sides. Even if you reach out to your Snowflake account team, we've been working closely with them to help to help kind of support along this process and especially in EA. And thank you so much everyone for joining us today. See Yeah. Thank you..