Saurabh Singhvi

🛑 If You’re Only Studying Theory, IBM Will Reject You. ---------------------------------- Many Salesforce Developers with years of experience fail to get past IBM’s real-time technical rounds. Why? Because they weren’t trained to think like a problem-solver under pressure. 🔍 SCENARIO-BASED QUESTIONS @IBM 1️⃣ Trigger Handling with Multiple Conditions Q: A trigger must update child records only if a specific field on parent changes. How will you handle this in a bulk-safe way? ✅ A: Use Trigger.oldMap to compare field changes and loop only when conditions are met. Use Maps to collect related records efficiently. 2️⃣ Platform Events for Cross-System Sync Q: IBM requires syncing Salesforce data with an external system in near real-time. How would you do this? ✅ A: Use Platform Events or Change Data Capture with middleware (like Mulesoft), and handle event publishing in Apex triggers. 3️⃣ Dynamic Querying & Object Detection Q: You’re building a generic data loader that works across objects. How will you dynamically fetch field values based on object name? ✅ A: Use Schema.getGlobalDescribe() and dynamic SOQL using Database.query() with input sanitization. 4️⃣ LWC Reusability Q: How do you create a reusable LWC component for different objects with similar UI? ✅ A: Use @api decorators to accept inputs, design flexible templates, and control dynamic rendering based on passed parameters. 5️⃣ Test Class for Callout with Retry Logic Q: You have an Apex class with callout and retry mechanism. How do you test all conditions? ✅ A: Use HttpCalloutMock for different responses—success, failure, and timeout. Assert retry logic, status flags, and response handling.  6️⃣ Governor Limit Spike in Batch Execution Q: You wrote a Batch Apex class to process 50,000 records, but it's failing with CPU time limit exceptions. How do you debug and fix it? ✅ A: Analyze the logic in execute() method for nested loops or heavy processing. Use Limits.getCpuTime() to debug, break logic into smaller Queueables if needed, and reduce heap/memory usage. 7️⃣ Integration Failure with Retry and Logging Q: An external API occasionally fails. How would you build a retry + logging system without losing failed records? ✅ A: Use Queueable or Batch Apex with custom logging object to track failed attempts, retry up to a limit, and send alerts on permanent failure. Respect limits by spacing retries. ----------------------------- 🎯 PROMINENT ACADEMY – GET IBM-READY WITH US We don’t teach you just theory. We prepare you for what IBM (and top MNCs) actually ask. 🚀 What You’ll Get: ✅ IBM-style Mock Interviews ✅ Real-Time Apex, LWC, Trigger & Integration Practice ✅ Resume + LinkedIn Optimization ✅ 1:1 Mentorship Until You’re Hired 📞 Contact: +91 98604 38743 📩 DM us now to step closer to your dream job at IBM.

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Salesforce is acquiring Informatica—and it’s more than an M&A headline. It’s a signal. 1. AI is rewriting the application stack. Salesforce gets it. This move validates a shift many of us have been tracking: enterprise apps are being rebuilt around AI. And the new competitive edge isn’t features or UX—it’s data. To power co-pilots, agents, and autonomous workflows, organizations need real-time, unified, context-rich data. By acquiring Informatica, Salesforce is making a play to own more of the data foundation—because without it, the apps of the future won’t run. 2. But consolidation introduces complexity. The idea of a seamless Customer 360 has been promised before. The real question now is: does this deal accelerate that vision—or delay it behind years of integration work? And is Salesforce expanding its scope beyond customer data to become the system of record for all enterprise data? If so, what does that mean for openness, agility, and customer choice? 3. Who benefits most—customers, or Salesforce? It’s smart for Salesforce to invest in data infrastructure. But reinvention through acquisition is tough—especially with legacy architectures in the mix. Is this move about enabling enterprise agility—or cementing platform lock-in? The answer matters. Because in the age of AI, enterprises need flexibility, not friction. This moment underscores three truths: Unified data is the enterprise backbone. You can’t run agentic AI or agile ops on siloed, stale, or brittle stacks. Cloud-native matters. Retrofitting old tech isn’t the path to real-time or resilience. AI is raising the stakes. Data that’s not trusted and context-rich is a liability—not an asset. At Reltio, we’ve been building for this reality since day one. We believed early on that speed and trust shouldn’t be a tradeoff. That’s why we built the Reltio Data Cloud™—a cloud-native platform for real-time, interoperable, AI-ready data across all domains. Today, that means: ✅ Operating at cloud scale—billions of records, sub-100ms response times, and greater than 99.99% uptime ✅ Delivering continuous data quality and governance, without monolithic upgrades and zero downtime ✅ Powering smarter, AI-infused workflows with built-in intelligence, security and compliance ✅ Serving as the foundation for your core data that includes customers, products, suppliers, and beyond These aren’t just features. They’re how we help enterprises move faster—with more confidence—because they trust the data driving their business. AI is only as strong as the data beneath it. And the foundation matters more than ever. #AI #DataUnification #MDM #Customer360 #Salesforce #Informatica #CloudNative #DataStrategy #EnterpriseAI

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If you’re willing to listen.  Salesforce's $8 billion Informatica acquisition tells you exactly where enterprise integration is heading. CTOs have stopped asking "Do we need real-time?" They are asking, "Where are we still running batch, and what is that costing us?" Well, about $1.5 to $3 million annually per enterprise in lost opportunities.   Revenue leakage is 5-10% from stale data.  In e-commerce, data staleness erodes 20-30% of potential revenue. No wonder Salesforce paired Informatica with MuleSoft. No, Informatica is not replacing MuleSoft. They’re going to work together. 👉🏼 Informatica brings 30 years of batch ETL, data governance, and master data management. Historical data, compliance archives, bulk warehousing. 👉🏼 MuleSoft brings real-time API connectivity. Together, they cover "before, during, and after" AI workflows. ✅ Informatica handles the history. ✅ MuleSoft handles the now. ✅ Data Cloud connects them for AI agents that need both. You aren’t choosing batch or real-time. You’re getting the option for when each is mission-critical. 𝗦𝗼, 𝘄𝗵𝗲𝗻 𝗶𝘀 𝗯𝗮𝘁𝗰𝗵 𝗮𝗰𝗰𝗲𝗽𝘁𝗮𝗯𝗹𝗲? For nightly reporting, compliance archives, and historical analytics. If the data feeds a dashboard someone checks once a day, batch works fine. The problem is defaulting to batch everywhere because that is what you built 10 years ago. 𝗧𝗵𝗲𝗻, 𝘄𝗵𝗲𝗻 𝗱𝗼𝗲𝘀 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝗺𝗶𝘀𝘀𝗶𝗼𝗻-𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹? For customer-facing operations, dynamic pricing, fraud detection, and live personalization. Anything where minutes cost money. Real-time APIs enable sub-second insights batch cannot match. Companies adopting real-time report 15-25% higher customer retention. API-led architectures deliver 5-10x faster integrations than ETL. In AI contexts, delayed data halves model accuracy. If your AI uses last night's batch run, you are flying blind. And if you think delayed integration isn’t critical? I have seen clients lose $2 million annually because inventory updates overnight, but e-commerce pricing updates in real time. Customers see products in stock that are not. Or prices that do not reflect inventory. That costs conversions. It costs trust. It costs revenue you cannot recover. Real-time fixes this by enabling proactive decisions. Studies show 2-4x ROI in agility for API adopters. Companies with real-time APIs are pulling ahead because their decision-making is faster. When your competitor adjusts pricing based on demand in real time and you adjust overnight, you lose margin. When their AI agents have live context and yours work from stale data, you lose customers. The Informatica-MuleSoft combination gives both capabilities. The governance and reliability of batch when appropriate. The speed and agility of real-time when critical. Follow me for more on integration strategies that drive business outcomes. #APIIntegration #ETL #DataGovernance #EnterpriseAI MuleSoft Community

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Future-ready systems aren’t optional anymore. ​ Explore how Slalom helps you modernize with clarity and speed → https://slalom.ws/4mn1Glj ​ Before: ​ ❌ Siloed systems slowed delivery​ ❌ Compliance was reactive​ ❌ Mainframes limited speed & integration​ ​ After: ​ ➡️ Cloud-native platforms scale with your business​ ➡️ Security is built in—not bolted on​ ➡️ Integrated systems support AI & analytics

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MuleSoft just dropped something big. Model Context Protocol (MCP) is now live on the Anypoint Platform! Now, AI agents like ChatGPT or Copilot can securely connect to your enterprise APIs, data, and workflows, no custom code required. - Turn your Mule APIs into MCP servers - Give agents real-time context (less hallucination, more action) - Enterprise-grade security & governance This is a game-changer for building AI-native enterprises. Let’s connect to explore how Royal Cyber can help you adopt MCP, integrate AI agents, and unlock next-gen automation. Learn more: https://lnkd.in/ddzYCDKe #MuleSoft #AI #MCP #EnterpriseAI #RoyalCyber #Integration #DigitalTransformation #APIs

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Back in my Oracle CRM days, our engineering VP said something that stuck with me: “Don’t ask what customers want today, tell me what they’ll want in 12 months.” (We were shipping annually and playing catch-up with Siebel). That mindset feels even more relevant today. In the AI era, the ground is shifting rapidly. Agentic browsers are just the beginning. How do you ship for today while architecting for tomorrow? The real winners won’t just close feature gaps. They’ll anticipate behavior shifts before they happen. #AI #Disruption

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𝗢𝗿𝗮𝗰𝗹𝗲 𝗛𝗖𝗠 𝗥𝗲𝗱𝘄𝗼𝗼𝗱: 𝗪𝗵𝗮𝘁 𝗬𝗼𝘂 𝗡𝗲𝗲𝗱 𝘁𝗼 𝗞𝗻𝗼𝘄 𝗕𝗲𝗳𝗼𝗿𝗲 𝗬𝗼𝘂 𝗘𝗻𝗮𝗯𝗹𝗲 With the rapid rollout of Oracle's Redwood UX, HCM leaders and system administrators are asking an important question: “Are we really ready for Redwood?��� Based on real-world customer implementations, lessons learned, and best practices across multiple releases, here’s a practical guide to help you prepare for Redwood enablement in HCM: 1. Is Redwood just a UI upgrade? Not at all. Redwood is more than a visual redesign. It delivers a reimagined, AI-powered user experience with: - Simplified and intuitive navigation - Responsive and mobile-friendly design - Embedded analytics and smart suggestions - Guided workflows that improve efficiency The goal? Reduce training time, boost adoption, and elevate the employee experience. 2. Why enable Redwood now? - Starting in 25C and 25D, many Responsive UI pages will be deprecated - Redwood will be mandatory for several critical flows - New functionality (like AI Assist, AI Agents, Enhanced Journeys) is increasingly exclusive to Redwood 3. Do I need to redesign my personalizations? Redwood pages are built using Visual Builder Studio Express, replacing the legacy Page Composer for UI customizations. Oracle provides tools like the Redwood Personalization Helper to ease migration. 4. Common challenges when enabling Redwood (and how to solve them): 𝗠𝗶𝘀𝘀𝗶𝗻𝗴 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗣𝗿𝗶𝘃𝗶𝗹𝗲𝗴𝗲𝘀 𝗶𝗻 𝗖𝘂𝘀𝘁𝗼𝗺 𝗥𝗼𝗹𝗲𝘀: Symptoms - Blank pages, Endless loading screens, Missing links or list of values Resolution - Review the Security Reference Manual updated for each release. https://lnkd.in/esrEtiqP   𝗙𝗮𝗶𝗹𝘂𝗿𝗲 𝘁𝗼 𝗜𝗻𝗴𝗲𝘀𝘁 𝗜𝗻𝗱𝗲𝘅𝗲𝘀 𝗼𝗿 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗲 𝗔𝗖𝗟 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀: Symptoms - LOVs not populating, Pages not accessible Ensure ACL jobs are properly configured and indexes are up to date after enabling new pages. 5. What can you personalize in VB Studio Express? Show/hide cards, tabs, buttons, or fields Configure layouts by user role Customize Activity Center pages across modules (Absence, Benefits, Employment) Maintain conditional logic with minimal code 7. What support is available to help with the Redwood transition? Oracle offers several key resources: Redwood Personalization Helper Tool Customer Connect webinars and communities My Oracle Support Doc ID 2922407.1 Redwood is not just a design upgrade, it’s a strategic shift in how HR systems are experienced. Start early, review your configurations, and engage your teams to embrace the change. #OracleHCM #Redwood #DigitalTransformation #HRTech #Kovaion

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Big question for all the Pega folks out there - GenAI can already spin up case flows, write rules, and suggest data models in seconds. So where does that leave us as Pega consultants? Here’s how I see it: -> AI will take care of the repetitive builder work -> That gives us more time to focus on design, strategy, and outcomes -> The real value will be in asking the right questions, not just writing the right code To me, that feels less like a threat and more like a huge opportunity for us. What do you think? Will GenAI make us better consultants or just different ones? #Pega #GenAI #FutureOfWork #Consulting #DigitalTransformation

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8 years ago, I spent a year unraveling and re-architecting a failed Salesforce implementation that had drained $1M and led to zero adoption. Our well-intentioned IT team had outsourced most of the build and had designed the system with minimal involvement from internal SMEs. The result? A spaghetti data model that didn’t reflect the business at all and was impossibly difficult to use by reps, or report out of. Sound familiar? Our former CEO tasked me with fixing it. I had to untangle the mess, make sense of our data and rebuild from the ground up. It was painful (but I loved it) and one of the most important lessons of my career. I learned the painful way that: 1) You need a deep understanding of the business to design systems that actually work. (Our IT team didn’t.) 2) Getting your data model right and ensuring your data is trustworthy, documented, and governed isn’t optional, it’s foundational. 3) Building everything from scratch doesn’t scale. The costs of maintaining, testing, QA, reporting and more just make it impossible. We ended up buying multiple AppExchange tools to automate what never made sense for us to build or maintain internally. Why share this now? Because I fear history is about to repeat itself with the rise of DIY agents and general-purpose AI tools. You're starting to hear: “Don’t buy that tool, we can just build an agent for that.” “You can actually just vibe-code them yourselves" Whether it’s in Copilot, Gemini, Glean, or whatever’s next…We’ve seen this movie before. Most CRM teams have deep Salesforce expertise, yet improvements still take months. With agents, it’s worse. The skills are brand new, the playbooks don’t exist, and teams are learning as they go. What looks easy on day one quickly becomes a mess of rewrites, bug fixes, and frustrations. Building effective agents isn’t about clever prompts, it’s about instrumentation, QA, and telemetry all of which come with massive hidden costs: -> Manual workflow design: Every sales motion has to be hard-coded ->Constant QA and SME drain: Every change risks quality demanding nonstop testing and SMEs get pulled into debugging bad answers instead of driving growth. -> Instrumentation & telemetry overhead: IT must wire dependencies, handle edge cases, and build custom reporting just to answer: Did it work? Did it help? The result? A system that looks cheap but quietly drains time, talent, and revenue tying up multiple FTEs just to maintain brittle, homegrown workflows. Am I against general-purpose AI tools? No. But I am warning against the DIY mindset without understanding the real hidden costs and why I think vertical, purpose-built AI solutions will win long-term. My thoughts below, and the case for vertical AI solution in sales. What do you think? (link to blog in comments)

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