Chapter Contents
CHAPTER 2
The Workday platform architecture.
At Workday, our mission has always been to place people at the center of the enterprise. So when we originally designed our architecture, we ensured it was flexible enough to accommodate the constantly changing world of technology and support the dynamic nature of business without extra cost or risk. It’s constantly evolving with innovative new features our customers can rely on, all while maintaining the exceptional Workday experience they expect.
To accomplish this architectural evolution, we kept the layers of our architecture insulated from each other so a change in one layer would not require a change in another. Let’s examine what that means.
We created a metadata abstraction layer to simplify development, a language we call XpressO. This means we define the functionality of our applications (forms, required fields, workflow actions, and so on) as metadata stored in our in-memory database rather than as programming code. This enables application developers to insulate themselves from deployment-specific details, such as persistence, scalability, and public cloud locations, so they can focus on the functional aspects of the applications and the user experience (UX).
CHAPTER 2
The Workday platform architecture.
At Workday, our mission has always been to place people at the center of the enterprise. So when we originally designed our architecture, we ensured it was flexible enough to accommodate the constantly changing world of technology and support the dynamic nature of business without extra cost or risk. It’s constantly evolving with innovative new features our customers can rely on, all while maintaining the exceptional Workday experience they expect.
To accomplish this architectural evolution, we kept the layers of our architecture insulated from each other so a change in one layer would not require a change in another. Let’s examine what that means.
We created a metadata abstraction layer to simplify development, a language we call XpressO. This means we define the functionality of our applications (forms, required fields, workflow actions, and so on) as metadata stored in our in-memory database rather than as programming code. This enables application developers to insulate themselves from deployment-specific details, such as persistence, scalability, and public cloud locations, so they can focus on the functional aspects of the applications and the user experience (UX).
What metadata means for developers.
The metadata object model describes the structure and behavior of Workday applications—and that’s where XpressO comes into play. There is no code-based procedural logic in a Workday application. Instead, Workday developers define the structure of an application by defining Java classes for the key business objects in the application. Classes can have relationships to other methods, classes, and attributes. Methods define the application’s behavior, while declarative relationships define the business logic—without the need to write any procedural code.
Figure 2-1. People are the focal point of Workday HR applications.
All of the object model’s parts—classes, relationships, attributes, and methods—are created through a forms-based interface. The resulting application is a collection of metadata definitions for each part of the application object model. These definitions are stored as collections of simple Java objects in the Java Virtual Machine (JVM) memory, the runtime environment for all Workday applications. While the Java Runtime Environment (JRE) is essential for Workday to function, it is the Workday application platform that interprets the metadata definitions. These are the unique aspects of the Workday architecture that go beyond the standard JRE.
Different services in the Workday architecture and technology platform collaborate to interpret the application metadata that XpressO creates when it processes application transactions and manual or automated requests. By clearly separating how applications are defined from their deployment platform details, new applications can be brought online quickly without disrupting existing customer business logic. We’ve demonstrated the strength of the Workday architecture since the early days by continually delivering new applications while improving existing ones without impacting customer operations.
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Figure 2-2. The evolution of the Workday architecture.
Workday began as a monolithic architecture with a few services for processing user interface (UI) requests and transactions. As demand for our services increased, we needed to evolve the underlying architecture to continue delivering the performance and scale our customers need. Because we had abstracted the technology platform from the applications, we could refactor individual services and adopt new technologies that brought greater scalability without disrupting existing customer instances.
For example, by splitting up our transactional service into a read service and an update service, we could optimize the services to the type of request being processed. This enabled us to move beyond the monolithic design to a services-based design, where additional services were introduced to optimize usage. As demand increased, we evolved the architecture based on the many loosely coupled distributed microservices we have today. Not only is the Workday architecture promising on paper, but it has also been tested and proven through many deployments and iterations.
The power of now: Real-time data in Workday.
Another fundamental principle of the Workday architecture can be seen in how it works with data for transactions and analysis. In traditional ERP systems, the transactional data is stored in a relational database. If a user wants to perform analysis on that data, it must first be extracted from the database, loaded into another system, and then formatted for analysis. This results in analyses based on stale data from multiple sets of data in disparate systems. Meanwhile, the transactional system continues receiving updates that are not reflected in the copy of the data used for analysis.
Unlike with traditional enterprise applications, we designed transaction processing, analysis, and reporting in Workday to happen in real time so the system works at the speed of thought. To accomplish this, we put both the application data and the applications in the same memory space, giving us instantaneous access to real-time application data, enabling transactions and analyses that operate on the same data in the same place.
Workday analyzes live transactional data to provide real-time insights. Our object model, with its rich network of relationships, enables multidimensional analysis and data presentation. Reports instantly reflect changes, ensuring your analysis is always up-to-date. For example, imagine analyzing an income statement that lists revenue and expenses. In Workday, you can drill down into a revenue balance to instantly access all related transactions. This allows for multidimensional analysis across various categories such as customer, product, location, division, project, and campaign—providing a comprehensive understanding of your financial data.
Workday believes continuously enhanced in-memory data management is no longer a problem to be solely addressed by databases on disk, serving data to applications via SQL—instead, it’s the path to scaling our applications. Our in-memory data services are essential to a highly optimized, multilevel caching approach to managing application data.
A deeper dive into the Workday architecture.
Let’s dig deeper into Workday architectural principles and why they’re so important for giving customers an effective long-term solution. We explore several underlying services that support Workday core technology and applications and our customers’ experiences.
Foundational principles of the Workday architecture.
The Workday architecture is built on core principles that guide our development and long-term strategy. From the beginning, we’ve aimed to overcome the limitations of traditional enterprise software by drawing inspiration from the consumer internet. This means prioritizing user experience, flexibility, and innovation in everything we do. These principles continue to shape Workday technology today.
Let’s begin by looking at the five fundamental principles of the Workday architecture:
- Fully utilize the best practices of cloud development with a true cloud implementation.
- Design products that continuously adapt to constant changes at all levels.
- Enable transactions and analytics to utilize a centralized source of data as a single source of truth.
- Manage access to all data by employing a uniform approach to security.
- Ensure all Workday customers use the same product version and benefit from ongoing innovation—no one is left behind.
Let’s dive into the “how” behind the Workday architecture and its interwoven services.
Workday is an architecture of services.
Workday architecture: The illustrated city map metaphor.
It is challenging to visually represent our sophisticated architecture, which continuously adapts and scales for over 10,500 customers. Traditional stack diagrams do not effectively show how the many services interact. Therefore, we prefer to illustrate the Workday architecture and its various services using a familiar metaphor: a city map.
The city map diagram illustrates how groups of related services in Workday operate and interconnect. Each colored area below represents a specific “district”—just like how a city may have a theater district, a restaurant row, and so forth. The City of Architecture represents a collection of related technology services. Each district is interconnected through a series of roadways, representing how data traffic flows between services throughout the system.
Architecture
Figure 2-3. A map of the City of Architecture.
Let’s focus on the 10 main districts that constitute this city in the cloud. While the city’s image does not fully represent every underlying service that supports the Workday experience, most core services are included. The first 7 districts will be discussed in this chapter; the remaining 3 will be covered in subsequent chapters.
01
User interface (UI) services support a true omni-channel experience, web, mobile, conversational UIs, and Workday Everywhere.
02
Transaction services—also referred to as object management services (OMS)—are a cluster of services that host the business logic for all Workday applications.
03
Persistence services include an SQL database for business objects and a NoSQL database for documents.
04
Analytics provides reporting and analysis capabilities that work closely with the OMS, providing direct access to Workday business objects.
05
Operations monitor the health and performance and increase observability of all Workday services.
06
Customer deployment tools support new customers as they migrate from their legacy systems into Workday, as well as when existing customers adopt additional Workday products.
07
Productivity tools provide specialized or alternative ways of interacting with Workday, such as conversation interactions, spreadsheets, and folders for storing documents.
The final three districts are:
08
AI (under the umbrella of Workday Illuminate™) provides a robust development and deployment architecture to expand different AI features within the Workday platform. This is covered in chapter 3.
09
Integration services provide a way to synchronize data stored within Workday with different external systems used by our customers. We explore this topic in chapter 6.
10
Extend services provides app components and development tools that enable customers to extend their Workday applications so they can be accessed from many locations within Workday, including the home page, dashboards, enterprise search, and contextual actions. These apps are available on both web and mobile clients. This is also covered in chapter 6.
Next, we walk through each of the initial seven districts, starting with the “front door”—Workday UI services.
UI services provide the human connection experience.
The Workday UX starts with our decision to generate the architecture from the definition of our applications. This approach allows technologies supporting the UX to evolve without impacting Workday applications. As a result, our customers benefit from innovations more quickly and without losing any application functionality.
Workday application developers—and Workday Extend customer and partner developers—don’t worry about coding for pixel-perfect screen layouts or specifying exactly where a given field appears on a page. Instead, they’re free to focus on their application functionality, such as defining the needed fields in a transaction and developing how fields get grouped and ordered. It’s the job of Workday UI services to generate (render) the presentation of the transaction using this information.
Several architectural benefits come from abstracting the UX from our application functionality.
- Consistency. Workday applications and ecosystem applications built on Workday Extend—in addition to packaged solutions—generate a UX that delivers a consistent look and feel, lowering the learning curve and increasing productivity.
- No functional regression. Application developers don’t have to refactor each time a technology evolves, so application functionality can keep moving forward.
- Faster innovation. We stay current with the latest UX technologies, and our UI services support a wide array of channels including conversational, mobile, and browser-based interactions.
A better approach to developing the best user experience.
As discussed earlier, developers design and deploy Workday using XpressO, a metadata-driven programming language that runs in transaction services. Let’s take a deeper dive.
Transaction services respond to requests by providing both data and metadata. UI services then use the metadata from transaction services to render the user’s view, selecting the appropriate layout for the target client device—such as a laptop or mobile device—and ensuring the experience is always the best fit for the target device technology and form factor. We also use JavaScript-based widgets to display certain data types and enhance the user experience.
This architecture enables XpressO application developers to separate their development focus from UI concerns. In parallel, Workday JavaScript and UI services developers focus on building UI components and UI-platform shared services. This has enabled Workday to radically update the UI over the years while still providing a consistent user experience across all applications without refactoring application logic. As new UI frameworks and technologies emerge, Workday architecture continuously adapts.
Exploring UI services on our city map.
Outlined on the architecture map is the UI services district with many services to explore.
Architecture
Figure 2-4. UI services on the city map.
The UI server scales, extends, and manages services.
The core UI service—the UI server—handles all user-generated requests and is foundational to the Workday architecture. As Workday applications have evolved and our customer and developer communities have requested to open Workday up, we’ve added services to extend, scale, and manage various content types.
The original UI server is still in place and supports the majority of traffic, but we’ve advanced its architecture for scalability and performance. This includes surrounding it with an increasing number of minor services, each performing a specific role, such as running background jobs, tenant management, error detection, logging, and session management.
Alongside the core UI server, Workday has added visual presentation services supported by the canvas design system, prebuilt UI components, SDKs, Workday Everywhere, media content, and conversational UI.
We dive deeper into some of these user experience topics in chapter 4. In chapter 6, we cover Workday integration services.
Transaction services manage business logic in real time.
At the heart of the Workday architecture–and center of the city map–is transaction services, also known as object management services (OMS). They’re a cluster of services that act as an in-memory data store and host the business logic for all Workday applications.
Today, the OMS cluster is implemented in Java with HTTP requests handled by Apache Tomcat. The OMS provides the runtime for XpressO. Workday reporting and analytics capabilities are provided by the analytics service that works closely with the OMS, giving it direct access to Workday business objects. Additionally, each tenant has an object transaction service (OTS) responsible for processing requests from user-triggered actions, such as tasks. The OTS uses an in-memory representation of the tenant’s data and executes the needed request logic.
Architecture
Figure 2-5. Object management services (OMS) on the city map.
This architecture ensures that transactions and analytics always operate based on the same source of truth, are governed by the same security model, and ultimately close the data divide that often exists in legacy implementations reliant on extract, transform, and load (ETL) tools to synchronize transactional and analytics data.
Workday is an in-memory application that has always kept core customer data in the memory space of our Java-based transaction service.
However, greater scalability has been required as functionality has expanded and our customers have increased in number. In response, we’ve evolved our architecture from a single transaction service into layers of OMS consisting of transactions, data management, and compute services. This evolution takes advantage of the benefits of in-memory data services, which have become essential to managing application data.
Initially, Workday relied on a single SQL database for both business data and document storage. However, as document volume grew, we introduced a dedicated NoSQL document store for improved performance and scalability. We explore our persistence services in more detail later in this chapter.
When a task request comes into Workday, it’s handed off to the class within the object model that knows how to process the update. The appropriate methods are called to process validations on the updated data. If the validations pass, the updates are made to in-memory data and then recorded in the SQL database via persistence services. The application must receive a commit from the SQL database update for the new values of the in-memory data to become visible to other transactions in all active tenant-compute services—ensuring the integrity of the data at all times. The database is the system of record for all application data, even though it is rarely needed for reporting and querying, as those resource-intensive activities act efficiently on the data in-memory.
Over time, as larger customer organizations have brought more users to Workday, the load on the OTS has increased—so we introduced the object reporting service (ORS) to handle read-only transactions and better distribute the load.
These services are part of Workday analytics services and act as in-memory databases that load all data on start-up. We also introduced a cache to support efficient access to the data for both the OTS and ORS and made additional efficiencies by moving the indexing and search functionalities out of the OTS and into the cache. We enhanced the ORS to handle additional tasks, such as payroll calculations and tasks run on the Workday job framework. We also moved both query and search functions out of the cache to independent services, so the transaction server and ORS now interact with these functions directly over HTTP.
As a side effect of the transactions performed within the OMS servers, updates flow across the Apache Kafka message bus to both query and search. These updates keep the index and search data up-to-date. Query and search use the MySQL database to store transaction snapshots.
Search is a critical function in Workday. Our global search box is the most popular way to access data across the entire system. This, along with prompts that help people quickly find information from vast datasets, highlights the importance of a powerful and efficient search experience.
However, different Workday applications present unique search challenges. For example, recruiting often requires sorting through a large number of candidates, where relevance ranking is crucial. To address these needs and maintain high performance with ever-growing datasets, we’ve introduced a new search service powered by Elasticsearch and machine learning. This intelligent service includes a query intent analyzer that learns from your searches and data to deliver highly relevant results tailored to your specific needs.
Scaling the OMS is an ongoing development process as we take on more customers with large numbers of employees. The beauty of our architecture is that these evolutions happen seamlessly without impacting applications and customers.
Persistence services keep information flowing.
The next district we visit is persistence services that manages application access to data requiring persistence and time durability. Multiple services within the Workday architecture support persistence. The MySQL database, which is used for transactional data, was the first persistence service built into the Workday architecture. Unstructured data—such as resumes, business-process documentation, and photographs—is stored in a NoSQL data store. The Workday system also supports customers uploading large external datasets to our cloud using a Hadoop service (HDFS). Workday selected HDFS for its ability to flexibly accommodate large volumes of data in any form, and with HDFS as part of our persistence services, we support external data correlated with data from Workday applications. This drives powerful insights and superior analysis and reporting.
There are also different cloud storage tools used in public cloud deployments:
- Amazon S3 for AWS deployments and media content
- Google Cloud Storage for Google Cloud deployments
- Spark for Workday Prism Analytics
The Workday approach to persistence offers multiple benefits.
Durable updates: We use a simplified schema to store transactional data so we don’t have to manage schema changes for all of our customers when we release new features and perform weekly upgrades.
Fast data load: Using the Hadoop schema-less approach makes it easier and faster for customers to load their data into our cloud.
Architectural flexibility: By not tying ourselves too closely to any storage standard (such as SQL), we can select the best storage technology for any given need, such as Amazon Aurora for AWS and Google Cloud SQL for Google Cloud deployments.
Future-proof data architecture: By not being tied to one standard, we keep our options open for switching to emerging storage technologies without disrupting customers or application developers—an approach that Martin Fowler, an industry thought leader on object-oriented design, calls “polyglot persistence.” With storage technology innovation at an all-time high, this architectural approach creates significant value for Workday and our customers.
Architecture
Figure 2-6. Persistence services on the city map.
Persistence services store multiple rich data types.
There are several primary persistence services utilized in Workday, each offering support tailored to the type of data stored and its processing requirements. Here are some common use cases:
- Business data is stored in an SQL database, which enables tenant management operations such as backup, disaster recovery, tenant copying, and point-in-time data recovery.
- Documents are stored in a NoSQL database, providing a distributed document store and disaster recovery. A document storage gateway connects the NoSQL database with other Workday systems, providing tenant-level encryption and linking documents to business data.
- Using HDFS and Spark, large data files uploaded by customers are stored in HDFS, Amazon S3, or Google Cloud Storage and processed where they are instead of being moved to where compute resources are located.
The Workday architecture also includes several data enrichment services that use a variety of sources. For example:
- GeoTag provides geolocation services, such as mapping zip codes to addresses
- Gladly provides auto-completion and suggestions for street addresses all over the world
- IP-Lookup returns the geographic location from IP addresses
GeoTag, Gladly, and IP-Lookup all use datasets licensed from third parties to support Workday applications.
Other persistence services in Workday support essential business processes.
- Performance statistics, stored in a dedicated installation of HDFS and separate from the big data store, are based on HDFS diagnostic log files and stored in Elasticsearch.
- The integration supervisor manages the queue of integrations in a MySQL database.
- The Workday worksheets feature stores user-created spreadsheets in a MySQL database.
- UI services access shared session data in a Redis in-memory cache; the OMS also uses a Redis cache to manage user sessions and coordinate some activity at a tenant level.
- Media content, such as video for products such as Workday Learning, and benchmarking data are stored in Amazon S3.
Now let’s take another look at the city map and make our way over to the analytics district.
Workday: Transforming data into actionable insights.
Architecture
Figure 2-7. Analytics on the city map.
The next district to explore is analytics. For most organizations, using analytics is a critical component of adapting and growing at speed and scale. IT leaders are asked to provide actionable data and insights supporting data-driven decision-making as their companies grow. This leads to several complex challenges.
For example:
- Larger amounts of data being generated and captured
- Siloed data that’s difficult to access and connect to other applications
- Patchwork architecture that lowers the quality of and trust in data
- Analytics resources and manual processes that don’t scale
- Complex and inconsistent data security and governance
Regarding HCM and finance data, the right information needs to reach the right people at the right time without latency, gaps, or inconsistencies. Organizations are under increasing pressure to harness business process data to fuel adaptability, keep up with rapidly changing customer requirements, and create new innovations with Gen AI.
Workday architectural principles help organizations overcome these challenges by:
- Creating a single source of truth across the organization, enabling data consistency through interoperability and with third-party applications
- Choosing open, cloud-native solutions where a consistent security and governance model governs core data
- Driving data democratization with integrity by keeping data close to where analytics and reporting are done and where decisions are made
Key benefits of Workday analytics for customers.
Workday analytics and architectural decisions benefit customers by enabling them to:
Build on a strong foundation of clean, trustworthy, and secure data
Tailor analytics to their organization type and its processes
Integrate non-Workday data sources
Enable self-service analysis, freeing IT to focus on strategic work
Provide proactive compliance with regulations
Reduce security and privacy risks
The Workday foundational platform architecture and our intelligent data core enable customers to unify, secure, and democratize trusted people and finance data across the enterprise.
The intelligent data core in Workday.
With its intelligent data core, single analytics engine, and data access layer, Workday customers can access all of their data in Workday—whether it’s planning data, budgets, forecasts, actual financial and worker transactions, data from non-Workday systems, or public data such as census data. The Workday analytics engine queries and transforms data across all these sources, serving up transactional, operational, and planning data at the right time and with background synchronization.
The intelligent data core ingests large-scale operational data and maps that data to the Workday object data graph using patented lens technology that enables the data to be continuously managed to ensure it is up-to-date. Workday OMS is the single brain that continuously manages data relationships, navigates among data objects, maintains lineage and time travel, and re-creates reality based on accurate data at any point in time.
Workday has always been an open environment. Your data flows back to your data lakes or warehouses as needed and can be blended and enriched with external data in Workday. This is how Workday resolves the biggest IT impediments to adaptability and helps achieve expected business outcomes. When HR, finance, procurement, and payroll no longer operate in a vacuum, you can combine data streams to drive better business processes and make more strategic decisions. You also benefit from a single system that understands data interdependencies and lets you plan headcount and your financial forecast to prepare you for what’s coming next quarter or next year. You can also execute your business processes in the way you like and analyze organizational performance in real time.
Imagine a future where your technology empowers growth instead of hindering it. With real-time data at your fingertips, you can anticipate challenges, seize opportunities, and confidently navigate toward a brighter future.
Integrating with and extending your data ecosystem with Workday.
Choosing the right approach to managing your data can be a complex task. Should you bring it all into Workday, or continue leveraging your existing data lake? The good news is that Workday offers flexibility to augment your current data strategy.
Integration: Workday can seamlessly integrate with your existing data lake and business intelligence tools, so you can maintain your current infrastructure while benefiting from Workday capabilities.
Consolidation: Alternatively, Workday Prism Analytics can serve as your central data hub for HR and finance data. This simplifies your technology landscape, reduces manual effort, and empowers your teams with greater self-service capabilities.
Ultimately, the best approach depends on your specific needs and how your organization consumes and uses data. Key factors to consider include:
- How people access and use analytical insights
- Where your data resides and how it’s accessed
- The timing and frequency of data needs
- The business processes you want to support with data
Because it enables analysis and action within the same system, Workday Prism Analytics is the ideal tool for distributing insights across your workforce and embedding analytics directly into HR and finance processes. Plus, it’s easier for people to access the information as they already have access to Workday—a system they’re familiar with and use regularly. The majority of your HR or finance data already lives in Workday, so there’s no need to move it and potentially compromise the security of the data.
Business intelligence tools designed for use with data warehouses or lakes are best suited for distributing insights to enterprise-wide executives, not just those in the HR and finance departments. They’re also best for performing cross-enterprise analysis, where your HR and finance teams’ inputs are only part of the picture but not the whole. This might be because the data from those other functions already lives in the data warehouse or lake, so business intelligence tools that tap into those data sources can offer a best-in-breed experience to C-level leaders and others with high-priority operational functions.
Considering the who, what, where, and why makes it clear that departmental and corporate business intelligence strategies are complementary—and that Workday Prism Analytics enriches the ecosystem.
Figure 2-8. Workday Prism Analytics and reporting enriches the data ecosystem.
Using Workday Prism Analytics with external data sources.
Workday analytics services have always enabled real-time reporting and analytics on transactional data. With the launch of Workday Prism Analytics in 2017, we expanded those capabilities to enable customers to integrate high volumes of operational, third-party applications, and historical data from legacy systems for analysis—all within Workday.
Workday Prism Analytics is a data hub for HR, finance, and third-party data, ultimately providing more granular and contextual detail to business users. Workday Prism Analytics enables customers to easily ingest external data, manage and transform it, then blend with their Workday data to provide richer reporting.
Workday Prism Analytics provides a self-service way for HR and finance to load non-Workday data into Workday, enhancing downstream decision-making within the context of their Workday experience. Workday Prism Analytics, combined with native reporting and analytics in Workday, provide a rich, secure, end-to-end reporting experience for HR and finance in their system of record. Workday Prism Analytics also works seamlessly alongside existing data strategies and tools, making it easier and more secure for IT to bring data closer to HR and finance teams and serve trusted, aggregated HR and finance data to other data tools for non-HR and non-finance use.
Workday Prism Analytics enables rapid analysis of vast amounts of data. This is because it leverages the power of Hadoop, a technology for handling massive datasets. By using Hadoop's YARN (Yet Another Resource Negotiator) for managing resources and HDFS (Hadoop Distributed File System) for storing data, Workday Prism Analytics ensures efficient data processing. Workday Prism Analytics also employs Spark, which performs lightning-fast queries and data preparation. Spark’s use of Parquet’s columnar storage format further optimizes analytical queries.
Workday Prism Analytics is not meant to replace the enterprise data warehouse or corporate data lake. Instead, it empowers HR and finance teams to manage their own use cases without “getting in line” for IT resources. It’s the HR or finance data hub for analytics and reporting. Ideally, Workday Prism Analytics-published data stays in Workday and is viewed using discovery boards and reports. Select data can also be seamlessly extracted to visualization tools such as PowerBI, Tableau, Qlik Sense, and Looker.
Workday Prism Analytics benefits IT by:
Reducing security and privacy risks by ensuring all HR and finance data is consistently governed by the trusted Workday security model with complete data lineage
Freeing IT talent to focus on strategic work by removing HR and finance from the IT request queue through organization-friendly data management tools embedded in its system of record
Ensuring the reliability and quality of data in enterprise data tools by delivering only the most relevant and accurate aggregated HR and finance data to your analytics strategy through a single open source for trusted HR and finance data
Connecting data strategy to business value by making granular data contextual and actionable, embedded in processes, and accessible by purpose-built reporting and extensibility tools
Workday Prism Analytics delivers value when coexisting with a customer’s existing environment by:
- Bringing in data from external systems using prebuilt connectors for popular platforms such as Snowflake and Salesforce or using standard methods such as SFTP, CSV files, or APIs
- Using auto data type detection to ingest data into Workday Prism Analytics, enabling intelligent data input into Workday
- Extracting data after blending and transforming using Workday reports to keep data secure and contained in the Workday environment
- Exporting data to external systems with Workday query language
Workday Prism Analytics also includes scheduling functionality for managing data loads. It maps external data to Workday transactional data based on selections and loads it as a data source into the data catalog, which can be refreshed on an ad hoc or scheduled basis.
Figure 2-9. Workday Prism Analytics draws insights from a single source of truth.
Workday core reporting and analytics: Unlocking the power of your people data.
Workday Human Capital Management (HCM) has powerful, built-in reporting and analytics tools that empower you to make data-driven decisions. These tools provide self-service access to real-time data while maintaining security, enabling your team to explore, analyze, and visualize information with features such as dashboards, reports, and interactive worksheets.
Discovery boards are the key to making secure, scalable, self-service ad hoc analysis capabilities accessible across the organization. Instead of waiting on other teams for insights, managers and business partners can create data visualizations and drill down into any area of interest using intuitive drag-and-drop features. In addition to Workday transactional data, discovery boards enable Workday Prism Analytics customers to analyze their blended Workday and non-Workday data sources.
Report writer enables customers to quickly create reports by leveraging the rich Workday library of over 5,000 prebuilt, configurable reports. While the number of available reports is notable, the real value lies in their quality, configurability, and ability to meet diverse reporting needs, including composite, matrix, and trending. People can perform multidimensional drill-downs into data with charting and filtering capabilities and then act directly from the report. For example, users can click on a number and see the underlying detail as well as slice and dice that data along multiple dimensions.
Dashboards make it easy for managers to monitor business performance and topics of interest, such as headcount movement, headcount plan to pipeline, time to fill open positions, and performance by hiring source. Over 120 prebuilt, configurable dashboards provide leaders with the visibility they need to act quickly.
Worksheets facilitate secure data sharing for ad hoc analysis and collaboration in a familiar Excel-like setting.
Mobile access enables employees to view reports and dashboards from their mobile devices.
Extensibility across the Workday ecosystem enables you to embed reports and dashboards into business processes, plans in Workday Adaptive Planning, and custom applications built with Workday Extend.
Figure 2-10. Embedded Workday reports and dashboards.
Workday People Analytics uncovers meaningful workforce insights.
With its built-in AI capabilities, Workday People Analytics is a self-service, prebuilt analytics application that quickly sifts through large amounts of data to identify meaningful workforce insights, trends, and opportunities automatically. All 70+ predefined metrics and KPIs, 23+ business questions, and 43+ visualizations have built-in security, data integrity, and compliance using the consistent security model in Workday.
Workday People Analytics operates a robust analysis engine on workforce data, utilizing our next-gen AI—Workday Illuminate™. Augmented analytics automates the discovery of insights for a diverse range of the workforce by using:
Pattern detection to identify significant changes that a human might overlook
Graph processing to uncover connections across extensive datasets
AI to prioritize the most critical issues
Natural language generation to clarify what’s occurring in a clear and straightforward narrative
With augmented analytics, business users minimize the time spent on manual data exploration, enabling them to uncover otherwise elusive insights.
Business managers and leaders can self-serve, access, and act on the most pertinent insights for rapid and confident decision-making and more immediate results.
The Workday storyteller engine is the augmented analytics engine that searches millions of data combinations, then makes connections between those combinations and surfaces the most significant results in story form. Here are a few of its functions:
- Focus insights
- Pattern detection
- Graph processing
- Machine learning
- Natural language generation
Figure 2-11. Workday People Analytics provides contextual insights.
Figure 2-12. Workday Illuminate and the storyteller engine.
Packaged solutions in the Workday Marketplace address specific business operations.
Packaged solutions from Workday, available through the Workday Marketplace app store, are designed to address emerging market trends, industry-specific requirements, cross-process orchestration, and unique regional demands beyond our standard product offerings. These solutions include content, configurations, and templates that customers can easily deploy through a Workday or partner services engagement. Examples of solutions available on the Workday Marketplace include:
- Employee document management that automates the creation and storage of employee documents
- Interview scheduling that streamlines the interview scheduling process
- Expense reporting that simplifies expense submission and approval
- Skills management that helps organizations identify and manage employee skills
- Performance feedback that facilitates continuous feedback and performance reviews
Analytics services leverage OMS to scale.
Analytics services work closely with the OMS to access Workday business objects directly. Whether reports run interactively with user input or in the background, they run in the same OMS JVM as all online requests. As the amount of data processed during reporting increases, more OMS resources are consumed.
To handle the increase in report processing, Workday added a dedicated read-only service, the ORS. The ORS is an up-to-date shadow of the transaction service, where updates occur. The reporting service is built to scale as Workday adds reporting servers to handle report processing for our large-enterprise customers.
Reporting servers are responsible for processing reports that run as background processes. No updates will take place on a reporting server. At the tenant level, Workday operations can determine whether background reports should be processed in the OMS or on a dedicated reporting server. Depending on that setting, the enterprise service bus (ESB) directs background reports for processing. The reporting servers will stay aligned with the OMS and operate with the most recent data, consistently ensuring that transactional and analytics data remain in sync.
Workday Prism Analytics manages user access to the HDFS big-data store. Users load data into the store using a retrieval service, which is then enhanced with data from the transaction service. A regular flow of data from the transaction service keeps the big-data store current.
Workday Prism Analytics provides exceptional performance and resilience.
Workday Prism Analytics is built for scale and performance that you can count on.
Customers
Monthly active users
Monthly processed data rows
Customers
Monthly executions
Monthly active users
Workday has also significantly invested in the performance and resilience of its analytics offerings. By increasing concurrency (threads per report run, distributed operations); improving indexing and caching; and optimizing BI runtimes, serialization, and method bindings, the analytics and reporting processes will continue to perform rapidly and scale as your organization grows. Customers have seen some impressive results, including:
- A 55% improvement in time spent performing drilldowns for matrix, trending, and composite reports
- Improvements for reports that return more than 1,000 results, such as:
- A 30% to 50% improvement in performance of interactive, advanced reports
- Over 60% improvement in the performance of advanced reports for Workday Prism Analytics data sources
The Workday platform leverages innovation and configurability to support the ever-changing nature of business without causing disruptions, incurring extra costs, or introducing risks. It enables rapid and scalable innovation developed by Workday, its customers, and its partners, fostering an ecosystem for creating and distributing unique applications. This is built on a trusted technological foundation that is highly resilient, secure, and reliable.
Operations using DevOps and agile practices.
Operations is the next district in our tour. Workday uses DevOps and agile practices and has established a culture of product and feature ownership by development teams throughout the lifecycle. We continuously enhance our data center and public cloud infrastructure as we expand our products and customer base. Data center infrastructure is a core part of the development organization, and DevOps projects are integral to every new Workday update.
Increasingly, technology that embodies the concept of “configuration as code” makes it possible to treat our public cloud as a software resource. We’re making an ongoing investment in increasing the automation and intelligence of our infrastructure services and the virtualization of compute and networking services to allocate and share infrastructure resources more efficiently for a growing customer base. We accomplish this while predicting and preventing performance, availability, and security incidents.
Workday operations teams leverage best-in-class DevOps toolsets to collect metrics, event logging, health, and performance information across the entire Workday environment. This information provides real-time visibility for our network operations center via dashboards and alerts. It enables auto-scaling and self-healing of services across Workday services, reacting to shifts in demand and overloaded services. Records are preserved, and long-term performance trends are analyzed and used to drive product enhancements.
Architecture
Figure 2-13. Operations services on the city map.
The observability platform provides continuous monitoring of operations.
To ensure that Workday operates efficiently for all customers, our operations team continuously monitors the health of our services. The team utilizes an observability platform built with Prometheus and Grafana, two leading open-source tools. This metrics monitoring and analytics platform handles our large-scale requirements and offers real-time visibility into the performance of all Workday-distributed services. The operations team also receives alerts through BigPanda, a SaaS provider that consolidates notifications from our monitoring systems.
Each service emits health metrics using Prometheus, which are sent over a RabbitMQ message bus to the metric processing back end. This backend then feeds metrics to Grafana and alerts the framework with BigPanda.
BigPanda correlates and consolidates related alerts into logical incidents, allowing Workday operations to see, understand, and resolve critical issues more quickly. Additionally, BigPanda enables teams to establish their own custom environment—such as a collection of related incidents—so they can focus on the most pertinent incidents relevant to their area of responsibility.
The metrics and logs collected provide valuable insights into the health of Workday services, workload trends, and emerging risks. This enables us to remediate issues before they manifest into broader service disruptions. Remediation can consist of several options: service restart, service migration, service scale-out, or throttling by placing workloads into lower-priority queues.
Continuous logging and monitoring to ensure availability.
As previously stated, diagnostic logs are collected through an Apache Kafka message bus and stored in Elasticsearch, which can be queried using Kibana. Kafka also collects performance statistics stored in HDFS and queries them using Apache Hive, Apache Zeppelin, and other data analytics tools.
In addition to continuous monitoring, events, alerting, and diagnostics services, Workday operations oversees several automated systems that support Workday services, including:
- Workday-specific configuration management systems
- Service discovery based on Spring Cloud ZooKeeper, which allows services to publish endpoints and discover other services—a key aspect of a service-oriented architecture
- A key management system (KMS) that supports encryption of traffic and data at rest
- The tenant supervisor, which aggregates the health information from services and reports availability metrics on a per-tenant basis
Overall, Workday continuously assesses and applies best-of-breed solutions to drive minimal downtime and deliver against the industry’s most rigorous service-level agreement (SLA) standards.
Customer deployment tools and processes.
Next up in our city tour is the district for customer deployment tools. Workday deployments require very large datasets, so deploying, migrating, or integrating into a Workday tenant can be challenging. The existing data from legacy systems may be fragmented, disjointed, or in different structures. Using data this way requires work to ensure that clean, valid, and properly formatted data is available. A host of tools enable customers to migrate data in a way that provides a cleaner, more useful production deployment. The following tools are helpful for new and existing customers that are adding new Workday products to their deployment:
- The cross-tenant data migration tool enables complex configurations to be migrated across different tenants
- The cross-tenant bulk data migration tool supports the migration of tenant datasets across production tenants
- Data editor transforms, cleans, and validates customer data to ensure it is usable in the new tenant
- Data loader loads the customer data into the tenant
Architecture
Figure 2-14. Deployment and adoption on the city map.
Tools for boosting productivity in Workday.
The final district we’ll review in this chapter is for Workday productivity tools that provide specialized or alternative ways of interacting with Workday. These include conversation interactions, spreadsheets, and folders for storing documents. Many services in this section are used across Workday products.
Some tools—such as Workday Assistant and conversational UI—are discussed in more detail in chapter 4.
- Content generation evaluates, formats, and validates a wide variety of sources for display across the Workday UI.
- Collaboration UI APIs provide connections used by web clients across collaboration services.
- Conversational UI enables people to chat with each other and with Workday Assistant within the Workday UI.
- Document management allows people to find, organize, and share their documents across the Workday platform.
- Campaigns provide tools to help distribute content across different groups in the Workday platform.
- Natural workspaces provide conversational clients across the Workday platform.
- Workforce scheduling creates workforce schedules based on worker availability, labor demand, and scheduling preferences.
Public and private cloud architecture.
From a customer’s perspective, the Workday application is the same across the entire platform, but the underlying architecture below the private and public cloud differ in some essential ways.
The private cloud platform is balanced between two core layers: application services and persistence services. The applications services layer includes everything needed to operate the platform’s runtime environment. It consists of the OMS system, Workday Extend, the elastic grid, and other core services discussed throughout this chapter. Along with the persistence services layer, which manages all data storage, including databases, media storage and search services, these two layers enable the Workday architecture to provide services to thousands of users.
But there’s more to the Workday platform on the public cloud, which adds a new dimension to the platform’s long-term evolution. The public cloud platform also includes a platform services layer, which includes a host of tools used to operate the platform in a much more dynamic way—such as Kubernetes for service management, Istio for inter-microservice communication, Argo and Jenkins for deployment orchestration, and other tools for observability, analytics, and self-healing.
The platform services layer creates a more robust and dynamic platform. It enables near-zero downtime for upgrades, as a standard update can be built in isolation directly alongside the currently live version. Once installed and tested, the platform switches over to the new version. In the private cloud, the active version of the application must be shut down first, and the new version must be rebuilt in the same runtime space before turning the platform back on after the updates are installed and tested. Key to achieving this is the dynamic scalability that public cloud providers offer, and Workday has integrated provider-specific tools to leverage this dynamic.
While it’s essential to prevent any reporting and scheduled tasks that may occur during the changeover from the old version to the new version, end users can continue to operate on the platform without interruption throughout the process.
Tools such as Prometheus, Cortex, Grafana, and Fluent Bit monitor service capacity and load, resource costs, and service health. Leveraging these tools enables the platform to be proactive in-service management, with capabilities such as auto-scaling (when demand rises or falls), self-healing (replacing services that become overloaded or faulty, preventing cascading failures), and transaction prioritization (ensuring smooth operations when specific services create a bottleneck). This ensures a more dynamic, efficient, and stable deployment.
Figure 2-15. Workday on public cloud platforms.
Public cloud deployments also leverage the scale of the cloud provider’s network to provide a more resilient deployment by spreading application services across multiple data centers. While a given tenant will exist in a specific cloud region, three locations will have shared application services and networking space. With a backup/failover location, all share a single persistent storage space. This ensures that if a given tenant or data center goes down, a quick rebuild can occur with minimal downtime or data loss.
The next chapter explores our ongoing architectural innovation, focusing on how AI—specifically our Workday Illuminate services—enhances, speeds up, and supports user experiences.
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