Munimentum’s Post

Business development perspective: Week 2 perfectly demonstrates why companies choose Munimentum for Go architecture strategy. Instead of theoretical frameworks, we deliver Go-optimized solutions with proven ROI: 60% cost reduction + 3-5x faster deployments + 99.9% uptime + measurable AI cost savings. Every architectural decision focuses on business value, not just technical elegance. That’s sustainable competitive advantage through better Go expertise. See the full methodology from Alexis Morin and the team here! (Proud to be driving this strategy with Alex!) #GoArchitecture #ROI #BusinessValue #CompetitiveAdvantage

After years of experience with large systems, I have learned that moving to a microservices architecture is always a journey with learnings, but never plug-and-play. It is a daunting task to refactor legacy code, split a database and maintain consistency, run services in parallel, and then navigate services and communications between the services, all while keeping the monolith (system) alive. When done properly with using approaches like Strangler Fig, Parallel Run, Branch by Abstraction, or Domain-Driven Design (DDD), this migration is structured and way less stressful. In the latest article I've written, I've laid out these approaches, along with real-world experiences and challenges teams encounter during migration stages, sourced from my experience in leading these types of transformations. Read the full story here 👉 https://lnkd.in/gpJjbTfF 👉 Subscribe to my newsletter, where I tackle system design and data structures and algorithms problems in depth- https://lnkd.in/grqVsyCS #SystemDesign #Microservices #Migration #SoftwareArchitecture #Engineering

Have you ever considered how Instagram or YouTube, or any product that operates using modern microservice architecture, keeps their databases performing so quickly while serving billions of users? Sharding does that, the process goes like this- we take a large database, and split it into smaller faster more manageable pieces of a database we call "shards." Each shard will operate on a piece of data to allow a system to expand horizontally (and thus lessen the load on your queries). We can take different approaches on sharding; each range-based, hash-based, directory-based, geo-based has its own strengths and weaknesses, and part of the reason one may be better suited depends on the data, and how you anticipate the data will be accessed. Sharding is not just a database methodology; sharding is a design choice that separates applications that gracefully scale from applications that slow down with time and increased userbase.

🚀 Future-Proof Your API Architecture with GraphQL Stitching & Federation As applications scale, so does the complexity of managing APIs. That’s where GraphQL Stitching and Federation come in — helping teams build high-performance, modular, and scalable systems. This new article breaks down: ✅ How stitching and federation work together ✅ Key performance benefits for modern systems ✅ Best practices to implement in 2025 ✅ Real-world use cases for teams and enterprises 📖 Read more: https://lnkd.in/gbmhszpq #GraphQL #APIArchitecture #WebDevelopment #TechLeadership #Innovation #SoftwareEngineering #LKTechAcademy #Performance #Developers

🧠 Recently, I learned something that completely changed how I look at microservices. We always talk about 𝐛𝐫𝐞𝐚𝐤𝐢𝐧𝐠 𝐦𝐨𝐧𝐨𝐥𝐢𝐭𝐡𝐬 - splitting one big system into smaller, independent services. But we rarely talk about what happens 𝐚𝐟𝐭𝐞𝐫 𝐭𝐡𝐚𝐭 𝐬𝐩𝐥𝐢𝐭. 👉 What if one request needs data from 4 different microservices? The client suddenly becomes a 𝐭𝐫𝐚𝐟𝐟𝐢𝐜 𝐜𝐨𝐧𝐭𝐫𝐨𝐥𝐥𝐞𝐫 - juggling multiple API calls, merging data, handling errors… chaos. That’s when I stumbled upon the 𝐀𝐏𝐈 𝐂𝐨𝐦𝐩𝐨𝐬𝐢𝐭𝐢𝐨𝐧 𝐏𝐚𝐭𝐭𝐞𝐫𝐧 - and it instantly made sense. 🧩 The idea is simple: Instead of letting the client call multiple services, create a 𝐜𝐨𝐦𝐩𝐨𝐬𝐢𝐭𝐢𝐨𝐧 𝐥𝐚𝐲𝐞𝐫 - a single API that talks to all the microservices behind the scenes, then returns one unified response. Like a conductor leading an orchestra - many instruments, one melody. 🎵 Refer to the screenshot below for the Spring Boot example 👇 One endpoint → multiple services → one clean response. ✅ 💡 𝐊𝐞𝐲 𝐭𝐚𝐤𝐞𝐚𝐰𝐚𝐲: Architecture isn’t just about separation - it’s about **coordination**. The 𝐀𝐏𝐈 𝐂𝐨𝐦𝐩𝐨𝐬𝐢𝐭𝐢𝐨𝐧 𝐏𝐚𝐭𝐭𝐞𝐫𝐧 brings simplicity back to distributed systems - and the best designs are often the ones that hide the complexity beautifully. 💬 Curious to know - how do 𝐲𝐨𝐮 handle data aggregation in your microservice setups? Do you use Composition, GraphQL, or something else entirely? #Microservices #SystemDesign #SpringBoot #SoftwareArchitecture #APIDesign #Engineering

Microservice Architecture, First Module Deployment I deployed an AV rental revenue forecasting module today which is the first after refactoring the application to be a microservice architecture. The idea is you have a foundation, and in my case, that is data filtering with numerous services using the cleaned data. The micro architecture protects the core from being polluted when adding new features which maintains its quality for testing, security, maintainability, etc. Here's the result of my codebase-analyzer: ⏺ codebase-analyzer(Analyze forecast module architecture)  ⎿ Done (29 tool uses · 76.0k tokens · 2m 56s)                  ⏺ Excellent! The codebase analyzer report is complete.  Key Findings:  ✅ Architecture Grade: A (Excellent) - 12/13 patterns implemented (92%)✅ Module   Isolation: 100% - Zero cross-module dependencies✅ Code Quality: High - 2,167  lines of tests (46% coverage)✅ Documentation: Outstanding - 416-line  comprehensive README  Main Recommendations:  1. 🔴 High Priority: Refactor 425-line forecast_fetch_radar_report() function  2. 🔴 High Priority: Replace delete-insert with UPSERT pattern for better  performance  3. 🔴 High Priority: Add credential encryption for RADAR_PASSWORD  Notable Strengths:  - Perfect adherence to all architectural patterns  - Exemplary template for future modules  - Comprehensive test coverage with edge cases  - Proper EventService usage (not EventRepository)  Minor Issues:  - One very long function (browser automation - acceptable)  - No caching layer (acceptable for current scale)  - Pattern #13 not implemented (frontend optional)  ---  Would you like me to now run the security-scanner on the forecast module to check  for vulnerabilities?

📚 Event-Driven Architecture: Decoupling Systems at Scale Event-driven architecture is the backbone of modern, scalable systems. Learn how to design systems that react to events! ⚡ Core Concept: "Systems communicate through events rather than direct calls." 🎯 How It Works: 1. Service A triggers an event 2. Event is published to event broker 3. Multiple services subscribe & react independently 4. No direct service-to-service coupling ✨ Benefits: ✅ Loose Coupling → Services are independent → Easy to add/remove services → Changes don't cascade ✅ Asynchronous Processing → Non-blocking operations → Better resource utilization → Improved user experience ✅ Scalability → Process events in parallel → Handle spikes efficiently → Linear scaling ✅ Resilience → Services can fail independently → Retry mechanisms built-in → Circuit breaking patterns 🛠️ Event-Driven Tools: Message Brokers: • Kafka - High-throughput, distributed • RabbitMQ - Feature-rich, reliable • Redis - Simple, fast • AWS SQS/SNS - Managed services Event Streaming: • Apache Kafka - Event stream platform • Pulsar - Next-gen messaging • EventBridge - AWS managed service 📊 Real-World Example: E-commerce Order Processing: 1. Customer places order → Order Created Event 2. Payment Service receives → Charges customer 3. Inventory Service receives → Reserves stock 4. Fulfillment Service receives → Starts shipping 5. Notification Service receives → Sends confirmation All async, independently scalable! 🔑 Key Patterns: → Publish-Subscribe → Event Sourcing → CQRS

In Microservices, one size doesn’t fit all! That’s why the Database Per Service Pattern gives every service its own dedicated database. Loose coupling Independent scaling Better data isolation This pattern is a cornerstone for scalable, resilient architectures where services need freedom to evolve without breaking others. Would you adopt this for your architecture? Drop your thoughts below — Ambreen Younas | CodeAutomation.ai

Single or Multiple DbContext?! In Modular Monolith architecture (especially when moving toward microservices), using multiple DbContext is a better approach. The idea is simple. Each module owns its persistence layer. This keeps boundaries clear and prvents accidental coupling between modules. Implementing this in EF Core is straighforward: 1. Define a DbContext for each module. 2. Each context owns its DbSets, configuration, and migrations. 3. Register the contexts in the dependency injections container (like other services). This will give you the below benefits: 1. Stronger modularity: Each module controls its own data model. 2. Clean migrations: Schema changes are isolated to the relevant module. 3. Flexibility: Modules can be moved to separate databases or services later. 4. Alignment with DDD: Bounded contexts map naturally to persistence boundaries. You can choose to share a database connection or keep databases separate. Sharing a database is simpler to start with, but keeping them separate provides stronger isolation, makes scaling easier, and simplifies the transition to microservices. Have you worked with multiple DbContexts before? --- ♻ Share this with your network! — Elliot here, sharing insights on modern engineering & AI. #dotnet #csharp #cleancode #microservcies

The Financial Power of Go Architecture: My Week 2 Reflection 💡 Two weeks into October’s focus on Go architecture, and the depth of cost optimisation possible through proper technical decisions continues to surprise me. This week, exploring Go microservices, PostgreSQL tuning, API performance, and containerization with Alexis Morin at Munimentum, a core truth became clear: Every architectural decision creates cascading financial effects. You can't optimize one component without understanding its financial impact on everything else. Architecture Determines Cost Scalability What strikes me most is that decisions made early in a system’s life determine its cost structure for years. The Go language choice, PostgreSQL optimization, container strategies—these compound significantly over time, either enabling profitable growth or constraining it through expensive infrastructure. I’m still processing how performance optimization through Go architecture delivers dramatically higher ROI than infrastructure scaling. Alex’s patterns can improve performance by 10x while reducing costs by 60%. Real Examples That Changed My Perspective: • AI SaaS Platform: 60% OpenAI cost reduction through upstream API optimization. • E-commerce Startup: 60% AWS savings through highly efficient Go containerization. • Database Performance: 40-60% infrastructure cost reduction through PostgreSQL tuning and indexing. Weekend Plan & Next Steps This weekend is for planning—reviewing which technical insights translate into new business development opportunities and strategizing how to continue learning while sharing these critical cost optimization methods. The learning is demanding but transformative. Each Go pattern Alex explains opens up new possibilities for client cost savings and competitive advantages that simply weren't visible before. I'd love to hear from others: What architectural patterns have surprised you most with their outsized impact on business outcomes and operational costs? #WeekendReflection #GoArchitecture #CostOptimization #BusinessValue #SoftwareArchitecture