Vijay Martin

𝗛𝗼𝘄 𝘁𝗼 𝗱𝗼 𝗺𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗽𝗿𝗼𝗽𝗲𝗿𝗹𝘆? 𝗦𝗲𝗹𝗳-𝗰𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗱 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 (𝗦𝗖𝗦) is a software architecture approach that prioritizes the decentralization of applications into independent systems, each with its domain logic, UI, and data storage. Unlike Microservices, smaller services focused solely on business logic, SCS are larger and encompass a broader scope within a specific domain. SCS are systems that represent 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝘄𝗲𝗯 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀, which include web UI, business logic and database, and it might have a service API. A single team usually owns them. The main 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲𝘀 of such systems are: 𝟭. 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆 - Each SCS operates independently with its database, business logic, and user interface. 𝟮. 𝗗𝗼𝗺𝗮𝗶𝗻-𝗮𝗹𝗶𝗴𝗻𝗲𝗱 - SCS is structured around specific business domains, ensuring each unit represents a coherent and meaningful set of functionalities. 𝟯. 𝗗𝗲𝗰𝗲𝗻𝘁𝗿𝗮𝗹𝗶𝘇𝗲𝗱 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 - Individual databases per SCS ensure data consistency within its boundary, reducing cross-service dependencies. 𝟰. 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗗𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝘆 - Allows for different technology stacks to be used across other SCS, suiting the specific needs of each domain. 𝟱. 𝗘𝘅𝗽𝗹𝗶𝗰𝗶𝘁𝗹𝘆 𝗣𝘂𝗯𝗹𝗶𝘀𝗵𝗲𝗱 𝗜𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲 - Well-defined interfaces for interactions with other systems, maintaining a clear contract while preserving encapsulation. 𝟲. 𝗜𝗻𝗱𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝘁 𝗗𝗲𝗽𝗹𝗼𝘆𝗮𝗯𝗶𝗹𝗶𝘁𝘆 - Each SCS can be deployed, scaled, and updated independently without affecting other systems. Such systems go well along with 𝗗𝗼𝗺𝗮𝗶𝗻-𝗱𝗿𝗶𝘃𝗲𝗻 𝗗𝗲𝘀𝗶𝗴𝗻 (𝗗𝗗𝗗). The first step in creating such systems is domain analysis, which can be conducted by identifying bounded contexts that align with specific business domains. Each bounded context is then encapsulated within an SCS, which comprises its own data management, business logic, and user interface, ensuring each system is autonomous yet able to interact with others through well-defined APIs when necessary. Why Self-contained Systems (SCS) has the 𝗲𝗱𝗴𝗲 𝗼𝘃𝗲𝗿 𝗺𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀: 🔹 𝗕𝗿𝗼𝗮𝗱𝗲𝗿 𝘀𝗰𝗼𝗽𝗲: SCS has a broader scope encompassing the UI, business logic, and data storage within a bounded context 🔹 𝗥𝗲𝗱𝘂𝗰𝗲𝗱 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆: Microservices can lead to a high level of operational complexity due to the management of many smaller, interdependent services, while SCS is more significant and more autonomous. 🔹 𝗗𝗮𝘁𝗮 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆: SCS manages their data, leading to better data consistency within each system, while Microservices often rely on a shared data store. Check the full link in the comments. #technology #softwareengineering #softwaredesign #techworldwithmilan #microservices

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Exploring the Microsoft Copilot Ecosystem - AI at Every Touchpoint Microsoft’s Copilot ecosystem is redefining productivity by embedding AI across its vast suite of tools and platforms. Designed to empower users—from everyday individuals to enterprise professionals and developers—Copilot integrates seamlessly into familiar environments, offering intelligent assistance tailored to diverse needs.

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🎯 Mastering Churn: The Art of Strategic Architecture Can an intelligent enterprise architecture truly reduce customer churn while strengthening AI and security foundations? 🤔 Picture this: A financial services firm was struggling with customer attrition due to outdated systems and rising security concerns. Instead of applying quick fixes, they redesigned their enterprise architecture—aligning it with modern frameworks, embedding AI/ML insights, and making security a core cultural value, not an afterthought. The impact was remarkable 💡 Customer retention soared, trust deepened, and churn began to vanish. 💡 Key Takeaways 1️⃣ Security as a Core Principle: Treat security as a living culture, not a checklist. Embed it into design, development, and operations to prevent breaches and build trust. 🔐 2️⃣ Leverage AI/ML Intelligently: Anticipate customer needs through predictive analytics. Use AI-driven insights and chatbots 🤖 to enhance engagement before dissatisfaction sets in. 🔧 How You Can Apply This: ✅ Run Security Deep-Dive Sessions: Conduct hands-on workshops on emerging threats and secure-by-design practices. ✅ Boost AI/ML Capability: Incorporate AI/ML learning paths into team upskilling programs to stay future-ready. ✅ Track Customer Signals: Use engagement metrics to detect early warning signs of churn and act before it happens. What’s your approach to blending AI, security, and architecture strategy to retain customers? Would love to hear how you’re tackling this challenge 👇 #EnterpriseArchitecture #AIInnovation #CyberSecurity #DigitalTransformation #TOGAF #CustomerRetention #BusinessStrategy #Architecture #Security

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From chaos to clarity: How API design transforms uncertainty into sustainable software architecture In today's VUCA world (Volatility, Uncertainty, Complexity, Ambiguity), sustainable software isn't a luxury—it's a necessity. 🔹 Volatility demands microservices that can be recombined faster than monolithic components 🔹 Uncertainty requires collaborative modeling to navigate unknown requirements 🔹 Complexity calls for APIs that structure and simplify our systems 🔹 Ambiguity needs a ubiquitous language that bridges IT and business perspectives APIs aren't just technical interfaces—they're the foundation for building adaptable, resilient software that evolves with your business needs. Want to learn more about crafting APIs that thrive in uncertainty? Check out our book "Crafting Great APIs with Domain-Driven Design" (Dr. Annegret Junker, Fabrizio Lazzaretti): 📚 https://lnkd.in/d2hD47GC #APIDesign #SoftwareArchitecture #DomainDrivenDesign #Microservices #VUCA #EnterpriseArchitecture #TechLeadership #SoftwareDevelopment

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We talk a lot about what AI does, but it's even more fascinating to understand how it thinks. It's easy to call the output of modern LLMs "magic," but the genius is in the engineering. The "Transformer" architecture revolutionized the field because it wasn't just a linear improvement on old tech, it was a new way of processing language. The secret ingredient is "self-attention." Instead of just looking at the last few words to guess the next one, the model looks at the entire sentence at once, weighing the importance of every word in relation to every other word. This is one of the clearest, step-by-step explanations I've seen of how it all fits together. Great post, Eduardo Ordax!

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Recently designed and implemented an end‑to‑end agentic application on AWS using Amazon Bedrock, DynamoDB, RAG, and vector search with Amazon OpenSearch. What I built: ============ 1. An AI agent that orchestrates calls to multiple backend services. 2. Product data and transactions persisted in Amazon DynamoDB 3. RAG + vector search in Amazon OpenSearch to ground responses in real product and customer context 4. A working flow where users can search, compare, and “purchase” items via natural language Key takeaway: The future looks very bright for architects. AI and agentic patterns don’t replace solid architecture; they reward it. Understanding domain modeling, integration patterns, and guardrails is more important than ever. Architects shouldn’t be afraid of AI – they should be the ones designing how it fits into the bigger system. #AWS #Bedrock #GenerativeAI #Agents #DynamoDB #OpenSearch #RAG #CloudArchitecture

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I am happy to share that our research paper has been accepted for publication by IEEE. The paper focuses on a challenge we have seen repeatedly across organizations: scaling machine learning from experimentation to real, sustainable products is still difficult, even with mature teams and modern platforms. In this work, we examine the key inhibitors to ML product scalability and highlight how enterprise IT architecture plays a foundational role in addressing them. The paper also proposes a Machine Learning Product Development Lifecycle that brings together architecture, engineering practices, and organizational design, instead of treating ML as an isolated initiative. From an Enterprise Architecture and technology leadership perspective, a few insights stood out clearly: • Scaling ML is an architecture problem before it becomes a tooling problem • Lifecycle thinking is critical to avoid fragmented pipelines and fragile systems • Architecture must evolve to support continuous learning, governance, and integration with enterprise platforms This research reinforced for me that the role of Enterprise Architecture is expanding, especially as AI and ML move from pilots into core business capabilities. Preprint available here: https://lnkd.in/gVQUh-5k I would be interested to hear how others are approaching ML product architecture and scale in their organizations. #BigIdeas2026 #BigIdeasEnterpriseArchitecture2026 #EnterpriseArchitecture #ArchitectureStrategy #TechnologyLeadership #SystemsThinking #DigitalTransformation #BusinessArchitecture #ITStrategy #LeadershipDevelopment #OrganizationalDesign #StrategyExecution #TechCommunity #InnovationLeadership #AIArchitecture #MachineLearning #MLatScale #AIOps

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