Overcoming Pega BIX Bottlenecks with Real-Time Data

𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐏𝐚𝐠𝐞𝐬 𝐢𝐧 𝐏𝐞𝐠𝐚 In many applications, business processes rely on data coming from multiple sources — databases, external services, or internal systems . Instead of repeatedly loading the same data every time it is needed, Pega provides a powerful mechanism called Data Pages. Data Pages allow applications to retrieve data once and reuse it efficiently across different parts of the application. 𝐖𝐡𝐚𝐭 𝐀𝐫𝐞 𝐃𝐚𝐭𝐚 𝐏𝐚𝐠𝐞𝐬? A Data Page is a read-only structure that retrieves data from a specified source and makes it available for use within an application. It acts as a centralized data access layer, separating how data is retrieved from how it is used in the application. This approach improves reusability, maintainability, and performance. 𝐖𝐡𝐞𝐫𝐞 𝐃𝐚𝐭𝐚 𝐂𝐨𝐦𝐞𝐬 𝐅𝐫𝐨𝐦 A Data Page can load data from different sources such as: • Database queries • Data transforms • Report definitions • External systems (REST / SOAP services) Once the data is loaded, it can be reused across UI, decision logic, and case processing. 𝐓𝐲𝐩𝐞𝐬 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐏𝐚𝐠𝐞𝐬 Pega supports three main scopes for Data Pages. 1️⃣ 𝐍𝐨𝐝𝐞 𝐋𝐞𝐯𝐞𝐥 Data is shared across all users on a system node. Example: List of bank branches or product catalog. 2️⃣ 𝐑𝐞𝐪𝐮𝐞𝐬𝐭𝐨𝐫 𝐋𝐞𝐯𝐞𝐥 Data is available for a specific user session. Example: Logged-in user profile information. 3️⃣ 𝐓𝐡𝐫𝐞𝐚𝐝 𝐋𝐞𝐯𝐞𝐥 Data is specific to a single case or process execution. Example: Customer details loaded for a particular transaction. 𝐖𝐡𝐲 𝐃𝐚𝐭𝐚 𝐏𝐚𝐠𝐞𝐬 𝐀𝐫𝐞 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 Data Pages help improve application design by: • Reducing repeated data retrieval • Improving application performance • Centralizing data access logic • Making applications easier to maintain This separation between data access and business logic is a key design principle in modern Pega applications. 𝐌𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲 Data Pages play a crucial role in building scalable Pega applications. By retrieving data once and reusing it efficiently across cases, processes, and UI components, they help create faster and more maintainable enterprise solutions. #Pega #PegaDeveloper #LowCode #DataPages #SoftwareArchitecture #WorkflowAutomation #TechLearning

𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐬 𝐢𝐧 𝐏𝐞𝐠𝐚 In many applications, data needs to be prepared, copied, or updated before it is used in a case or sent to another system. Instead of writing complex procedural logic, Pega provides a rule called a Data Transform that helps developers manage data changes in a structured way. Simply put, A Data Transform is used to modify clipboard data and map values between properties. 𝐀 𝐒𝐜𝐞𝐧𝐚𝐫𝐢𝐨 Imagine a Bank Account Opening application. When a customer submits the application form, the system needs to perform several data operations: • Copy customer details into the case • Set a default account type • Format the address fields • Prepare data before sending it to an external banking system These tasks can be handled using a Data Transform. 𝐄𝐱𝐚𝐦𝐩𝐥𝐞 Customer submits the following details: Name: Rahul Sharma City: Hyderabad The system may use a Data Transform to: Set .CustomerName = Rahul Sharma Set .AccountType = "Savings" Copy .CustomerAddress.City = .City Set .ApplicationStatus = "Submitted" This prepares the case data for the next step in the process. 𝐂𝐨𝐦𝐦𝐨𝐧 𝐀𝐜𝐭𝐢𝐨𝐧𝐬 𝐢𝐧 𝐃𝐚𝐭𝐚 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐬 Data Transforms support several operations that manipulate data on the clipboard. Some commonly used actions include: • Set – Assign a value to a property • Append to – Add data to a list • Remove – Remove items from a list • Update Page – Update values in an existing page • Apply Data Transform – Reuse another data transform These operations help organize complex data logic in a clear and maintainable way. 𝐖𝐡𝐞𝐫𝐞 𝐃𝐚𝐭𝐚 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐬 𝐀𝐫𝐞 𝐔𝐬𝐞𝐝 Data Transforms are commonly used in: • Case initialization • Data mapping for integrations • Preparing data for UI views • Updating case properties • Reusing data manipulation logic 𝐌𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲 Data Transforms provide a simple yet powerful way to manage data in Pega applications. By clearly defining how data should be copied, updated, or initialized, they help developers create clean, maintainable, and reusable data logic. Understanding Data Transforms is essential for building efficient data flows in Pega applications. #Pega #PegaDeveloper #LowCode #DataTransforms #SoftwareArchitecture #WorkflowAutomation #TechLearning

🚀 𝗛𝗼𝘄 𝗮 𝗗𝗲𝘃 𝗔𝗴𝗲𝗻𝘁 𝗖𝘂𝘁 𝗢𝘂𝗿 𝗔𝗣𝗜 𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝘆 𝗧𝗶𝗺𝗲 𝗯𝘆 𝟰𝟬% Building a standard Order API usually takes a seasoned developer 1–2 days. You know the drill: 👉 Validate payloads 👉 Map to ERP schemas 👉 Build retry logic 👉 Write MUnit tests 👉 Deploy We introduced a Dev Agent into the workflow — and the repetitive parts of integration disappeared. Here’s what changed 👇 ⸻ ⚡ 𝗧𝗵𝗲 𝗔𝗜-𝗔𝘀𝘀𝗶𝘀𝘁𝗲𝗱 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 • API Design — Generated a clean RAML spec from raw requirements in seconds. • Scaffolding — Suggested flow structure (Listener, Validation, Error Handling). • DataWeave — Handled ~80% of Order → ERP mapping. Only edge cases required manual tuning. • MUnit — Auto-generated test suites for happy paths and schema failures. ⸻ 🧠 𝗪𝗵𝗲𝗿𝗲 𝗧𝗵𝗲 𝗛𝘂𝗺𝗮𝗻 𝗦𝘁𝗶𝗹𝗹 𝗪𝗶𝗻𝘀 AI accelerates execution. Architecture still requires thinking. We led on: • Retry backoff strategy design • Security policies (OIDC / SAML) • Governance alignment • Performance tuning ⸻ 📈 𝗧𝗵𝗲 𝗥𝗲𝘀𝘂𝗹𝘁 • 40% faster initial delivery • Zero boilerplate fatigue • More time for architecture and optimization The Dev Agent didn’t replace the developer. It elevated the developer. ⸻ 💬 Are you using AI Dev Agents in your MuleSoft or integration workflows yet? #MuleSoft #AI #Integration #APIDevelopment #Automation #DataWeave #SoftwareEngineering #EnterpriseIntegration #Cognizant #CTS #MulesoftMentor

🚀Exciting News: Architecting the Future of Enterprise Intelligence! I’m thrilled to officially announce the launch of my new digital platform: BuildWithSarthak (BWS)! 🌐 As an engineer deeply passionate about the intersection of Pega Development, AI/ML, and Data Analytics, I built this platform to showcase how next-gen automation and intelligent data architectures can transform complex business challenges into scalable opportunities. What is BuildWithSarthak? BWS is more than just a portfolio—it’s a lab where I’m architecting the "Intelligence Layer" for modern enterprises. From AI-driven automation to robust healthcare solutions, BWS represents my commitment to building technology that is both powerful and intuitive. Key Features of the Platform: ✅ AI-Powered Analytics: Custom ML models for predictive business intelligence. ✅ Enterprise Architecture: Scalable Pega PRPC workflows and seamless data integrations. ✅ Interactive Roadmap: A transparent look at the upcoming Neural Core and Global Sync Network. ✅ Insights Blog: Sharing my journey from Pega development to the world of AI/ML. I’ve always believed that data shouldn't just be stored—it should be understood. Whether you're interested in enterprise healthcare systems or the latest in predictive analytics, I’d love for you to check it out! Explore the live platform here: 👉 https://lnkd.in/giksyFzj Let’s build the future, together. 🚀 #BuildWithSarthak #EnterpriseSolutions #AI #MachineLearning #DataAnalytics #PegaDeveloper #Innovation #TechLaunch #DigitalTransformation #SoftwareEngineering

𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝗡𝗼𝘄 𝗔𝗜 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀 𝘄𝗼𝗿𝗸 𝗯𝗲𝘀𝘁 𝘄𝗶𝘁𝗵 𝗢𝗢𝗧𝗕 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀. Custom schemas require additional configuration steps. Here's how to assess your readiness before rollout. Every ServiceNow team is under pressure to adopt AI features. Leadership sees the headlines, sets the timeline, and expects results. Your team just enabled Now Assist - and the AI recommendations are incomplete, missing custom fields your incident process depends on. Here's why: ServiceNow AI features are pre-configured for OOTB schema patterns. The more you've customized, the more configuration work required to achieve AI effectiveness. 𝗧𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺: 𝗰𝘂𝘀𝘁𝗼𝗺 𝘀𝗰𝗵𝗲𝗺𝗮𝘀 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲 𝗔𝗜 𝗰𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗮𝘁𝗶𝗼𝗻 𝘄𝗼𝗿𝗸 • Predictive Intelligence requires you to explicitly select which custom fields to include in model training - defaults assume OOTB fields • Now Assist skills ship with fixed input fields tuned to OOTB. Including custom fields means cloning skills via the Skill Kit (NASK) and manually adding fields - admin work beyond the default setup • AI Agents (Yokohama+) use tool-calling and can reason about table/field names - but there is no automatic schema discovery. Admins pre-configure which tables and fields each tool acts upon The more custom fields and tables you have, the more configuration effort required. 𝗛𝗼𝘄 𝗲𝗿𝗺𝟰𝘀𝗻 𝗵𝗲𝗹𝗽𝘀 𝘆𝗼𝘂 𝗮𝘀𝘀𝗲𝘀𝘀 𝗿𝗲𝗮𝗱𝗶𝗻𝗲𝘀𝘀 • Compare your schema against OOTB baselines to see exactly which customizations require AI configuration attention • Visualize table inheritance to spot modifications to core tables that AI features assume follow OOTB structure • Data density shows fields with low or zero population that can safely be removed, reducing configuration scope • Track schema changes across releases to separate recent additions from legacy drift 𝗪𝗵𝗮𝘁 𝘁𝗵𝗶𝘀 𝗹𝗼𝗼𝗸𝘀 𝗹𝗶𝗸𝗲 𝗶𝗻 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲 An architect assessing AI readiness opens erm4sn and compares the incident table against OOTB. She sees 47 custom fields - but data density reveals only 23 are actively populated. Custom CI tables extending 𝘤𝘮𝘥𝘣_𝘤𝘪 have bypassed inherited fields with custom alternatives - AI features won't recognize them without configuration. In 4 hours instead of 2 weeks, she has a clear picture: which customizations justify AI configuration effort, which are legacy clutter, and a realistic timeline for stakeholders. See exactly where your schema diverges from OOTB - explore the comparison demo: https://demo.erm4sn.com/ #ServiceNow #AIReadiness #CMDB #DataModeling #SchemaManagement #PlatformEngineering

Anthropic’s blog post about Claude Code analyzing and refactoring COBOL may have shaken the market and eaten into IBM shares, but the idea of AI transforming legacy code isn’t new. AWS and others have long offered tools to extract locked-up processes, data, and logic from mainframes into cloud-friendly languages. But the real challenge isn’t just figuring out what’s in the box. It’s deciding how that work should run in a cloud-native, AI-led world. In other words: don’t just rewrite - reimagine. Clunky green screens as mobile-friendly, adaptive UIs 📱 Manual handoffs as intuitive automations 🤖 Opaque processes as repeatable, scalable workflows 🔁 And bring that work into an agentic AI platform built for end-to-end governance and control 🛡️ With Pega, every agent is anchored to a workflow that defines exactly how it behaves. The result: faster outcomes, predictable execution, and AI you can trust. Pega Blueprint ingests legacy artifacts - requirements docs, demo videos, BPMN files, data schemas, and more - and generates agentic workflows for modern platforms in minutes. Reimagine legacy apps on a platform built for the enterprise. https://lnkd.in/eNs-ubAF

A few thoughts about the new Record-Triggered Automation architect guide 🤔 1. Back in 2020, best practice was one large flow per object. However, this caused DevOps, bottleneck of work, and extremely complex flows. When formula entry criteria for flow + Flow Trigger Explorer came out, it then became best practice to have different flows with very specific entry criteria. This produces lots of bite sized flows, organized in the exact order they should fire. Now, it sounds like one entry point (apex or flow) is best. I can see that being useful as you really only need one before save, and one after save automation. I One large orchestration flow calling subflows, acting like an apex trigger, is a very cool concept that I want to dig into more. 2. I really like where it talks about 'Record-Triggered Flow with Invocable Apex'. I think thats a very cool hybrid approach as simple business processes could be called as a subflow from an after save orchestration flow, whereas more complex processes could be invoked apex, giving flexibility. 3. One thing Flow has going for it that Apex does not is scheduled paths. There seems to be no parity there for apex. Wonder if there is a plan for that in the future. 4. This article also does not talk about how AI affects these decisions. I could see a world where Flow completely goes away, in favor for apex that is written by agents with full org context. Since Apex is faster and more flexible, if agents can enable almost everybody to code, that would be a brainier. Of course, that only works if agents are smart enough to follow best practices and have full org context. Link to article: https://lnkd.in/gjYN6jD7 #Salesforce #SalesforceFlow #SalesforceApex #SalesforceArchitect

BPM & Constellation Migration Specialist | Helping Pega Teams Ship Faster Pega says Constellation is the future. They’re right. But nobody’s talking about what happens between “run the Modernization Assistant” and “go live.” I’ve been studying every PegaWorld session, community thread, and support case I can find on Constellation migrations. The same problems keep surfacing: → Teams think UI-first (old habit). Constellation demands data-model-first. The mental shift takes longer than the technical work. → Years of UI Kit customization become instant blockers. One team at PegaWorld had to strip everything custom and start from OOTB guidelines. → The debugging toolchain is completely different. No more Clipboard. No more Tracer as you knew it. It’s Chrome DevTools and JSON payloads now. → Blended UI (running Traditional + Constellation in one portal) sounds simple in a slide deck. In practice, teams are filing SRs and calling Lighthouse to get it working. → Government orgs want Constellation features but can’t go to Pega Cloud. They’re stuck finalizing Kubernetes platforms while the feature gap grows with every release. The Modernization Assistant is a great starting point. But it gives you a list of issues — not a strategy. What teams actually need: • Which issues are quick fixes vs. custom DX component rebuilds • How long this will realistically take (not the “2 weeks” heuristic) • Whether blended UI, full migration, or SDK approach fits their situation • A budget they can defend to leadership That’s exactly what I’m building at VertexFlow Technologies — a fixed-scope Constellation Migration Readiness Assessment. You get a prioritized roadmap, not a spreadsheet. If your team is in the middle of this right now, let’s talk. DM me or comment below. #Pega #Constellation #Migration #BPM #Automation

Most teams connect Pega to an external system the wrong way. They drop a REST call directly inside the case flow. It works. Until it doesn’t. Then debugging becomes a nightmare. In Pega 24.2 Constellation, the clean way to handle an external System of Record is simple: Use a Savable Data Page with a proper Save Plan. Think of it like this. Your UI is the front desk. Your external system is the warehouse. You don’t let the receptionist run into the warehouse every time someone clicks Save. You give the receptionist a form. That form tells the warehouse exactly what to do. That form is your Save Plan. Here’s how it works. One Data Page. Multiple actions. When the user clicks: Create → send POST Update → send PUT or PATCH Delete → send DELETE You map simple conditions to each action. No guessing. No messy logic inside the case. Important detail most people miss. If the data lives outside Pega, you must enable: “Is this page used for alternate key storage?” Why? Because Pega needs the external record’s ID to know which row to update or delete. No ID. No control. Now here’s the best part. Constellation handles the UI for you. Go to App Studio. Enable Create. Enable Edit. Enable Delete. That’s it. Buttons appear automatically. Sorting, filtering, grouping — already built in. You can even expose this data on a landing page so business users manage it directly. No developer intervention required. Best practice I enforce on every project: Never call Connect-REST directly from the case flow unless you absolutely have to. Keep integration inside the Data Page. Keep workflow clean. Your case logic should focus on business steps. Your Data Page should focus on talking to the outside world. Separate responsibilities. Clean architecture. Easier upgrades. Easier debugging. Faster delivery. This is how modern Constellation apps should integrate with external systems. I share real-world Pega architecture lessons like this on my YouTube channel TechWorldWithAbdul. Pegasystems ♻️ Repost and share this learning.