REST is standard, but is gRPC the future of Microservices? 🚀 Choosing between REST and gRPC isn’t just about "which is better"—it’s about choosing the right tool for the specific architectural challenge. In my 10+ years of building enterprise systems for the Banking and Healthcare sectors, I’ve seen the shift firsthand. While REST remains the undisputed king for public-facing APIs due to its simplicity and browser compatibility, gRPC is rapidly becoming the gold standard for internal microservice communication. Why the shift? 🔹 Efficiency: gRPC uses Protobuf (binary) vs. REST’s JSON (text), significantly reducing payload size. 🔹 Speed: Built on HTTP/2, gRPC allows for multiplexing and bi-directional streaming. 🔹 Strict Contracts: With .proto files, you get native type safety—no more guessing if the client and server are in sync. However, REST still wins on caching and universal browser support. In high-frequency trading or real-time patient monitoring systems, every millisecond counts—that's where gRPC shines. But for a public developer portal? REST is still my go-to. #Microservices #API #SoftwareArchitecture #REST #gRPC #BackendDevelopment #WebDev #SystemDesign #Programming #Coding #CloudNative #FullStack #TechComparison #DevOps #Java #Python #Golang #Protobuf #JSON #SoftwareEngineering #DataTransfer #RealTimeData #Infrastructure #TechTips #ByteByteGo

Why REST APIs Still Matter in 2026 GraphQL is powerful. gRPC is fast. But REST? Still everywhere. After 10+ years in enterprise systems, one thing is clear: Simplicity scales better than complexity. REST works because: • Easy to understand & implement • Universally adopted across teams • Strong ecosystem (Swagger, Postman, curl) • Perfect for most real-world apps (CRUD, dashboards, APIs) Yes, GraphQL & gRPC have their place. But not every problem needs a complex solution. REST hits the sweet spot simple, scalable, reliable. Good engineering isn’t about trends. It’s about what works. Thanks & Regards, Harshavardhan Sakhamuri  314-690-7292  harshasakhamuri.work@gmail.com #SoftwareEngineering #BackendDevelopment #JavaDeveloper #Microservices #RESTAPI #APIDesign #CloudComputing #SystemDesign #DeveloperExperience #TechLeadership #ScalableSystems #GraphQL #gRPC #FullStackDeveloper #TechCommunity #Programming #CodingLife #ModernDevelopment #DevOps

I built a system that listens to everything — and never acts twice. 🔁 Let me explain. Most backend systems break under one simple condition: The same event fires twice. Double email sent. ✅✅ Duplicate file uploaded. 📂📂 Lambda invoked twice. 💸💸 So I built a Go-based webhook toolkit that bridges Appwrite → AWS — with idempotency at its core. Here's how it works ⚙️ Appwrite triggers a webhook (file upload, DB write, function exec) 📡 Our Go server catches it on port 8080 📦 Webhook Parser breaks down the JSON payload 🔒 Idempotency Store checks: "Have we seen this event ID before?" 🔀 Event Router sends it to the right adapter:   → S3 (PutObject / DeleteObject)   → SES (SendEmail)   → CloudWatch (batched log events)   → Lambda (InvokeFunction) One webhook. One action. Every time. No exceptions. The part most engineers skip? The idempotency layer. It's not glamorous. It's not on any architecture diagram tutorial. But it's what separates a prototype from a production system. 💬 Are you handling duplicate events in your system? Or just hoping they don't happen? Drop your approach below 👇 #Programming #SoftwareEngineering #AWS #GoLang #BackendDevelopment #SystemDesign #CloudComputing #DevOps #TechTwitter #100DaysOfCode #OpenSource #WebDevelopment #Appwrite #Engineering #Tech

Shall we dockerize? 🐳 Best practices Part III/IV Clean Dockerfiles save time, reduce image size, and make deployments far more reliable. In this part, we cover practical Docker best practices including caching, multi-stage builds, lighter runtime images, and smarter Dockerfile structure. ⚙️📦🚀 Written by Mohamed El-Zomor 👉 Read the full article: https://shorturl.at/LfJhD #KitesSoftware #dockers #DevOps #containerization #softwareengineering

Building a strong backend career is like stacking the perfect burger 🍔 Choose your base language, add frameworks, databases, APIs, caching, testing, CI/CD, containerization, and architecture patterns — every layer matters. No shortcuts, just skills layered with consistency. Keep learning. Keep building. Keep scaling. 🚀 #BackendDevelopment #SoftwareEngineering #TechSkills #CareerGrowth #Programming #WebDevelopment #LinkedInLearning

Hot take: most backend complexity is self-inflicted. You don't need: → 15 microservices for a CRUD app → Event sourcing for a todo list → GraphQL when REST would suffice → Kubernetes for 3 servers → Kafka for a queue that handles 10 messages/sec I've seen startups collapse under the weight of infrastructure they didn't need. The complexity tax is real — every service boundary is a potential failure point, a latency addition, an operational burden. Simplicity scales farther than most people think. Complexity scales poorly and fails in mysterious ways. Start simple. Add complexity only when data proves you need it. Questions I ask before adding any backend complexity: → What's the actual problem this solves? → What's the failure mode if I'm wrong? → How much operational overhead does this add? → Can my team debug this at 2 AM? If you can't justify it with data, you probably don't need it. What's the most over-engineered system you've had to maintain? 👇 #Programming #Microservices #APIDesign #Backend

In 2016, I mass-produced microservices like a factory. By 2017, I was debugging them at 2 AM on a Saturday. Here's what 14 years taught me about microservices the hard way: We had a monolith that "needed" to be broken up. So I split it into 23 microservices in 4 months. Result? - Deployment time went from 30 min to 3 hours - Debugging a single request meant checking 7 services - Team velocity dropped 40% - Every "simple" feature needed changes in 5+ repos The problem? I created a "distributed monolith." All the pain of microservices. None of the benefits. What I learned after fixing it: 1. Start with a well-structured monolith. Split only when you MUST. 2. Each service must own its data. Shared databases = shared pain. 3. If 2 services always deploy together, they should be 1 service. 4. Invest in observability BEFORE splitting. Tracing, logging, monitoring. 5. Domain boundaries matter more than tech stack choices. We consolidated 23 services down to 8. Deployment time dropped to 15 minutes. Team happiness went through the roof. The best architecture is the one your team can actually maintain. Have you ever over-engineered a system? What happened? #systemdesign #microservices #softwarearchitecture #java #programming

You're still designing distributed systems with a single-machine brain. Most engineers containerize their apps and call it cloud-native. But they never upgrade the mental model they learned writing monoliths. Kubernetes isn't a deployment target — it's a distributed runtime with its own primitives, lifecycle rules, and failure boundaries. Classes became Container Images. Objects became Containers. Constructors became Init Containers. The JVM became the entire cluster. If you're fighting Kubernetes instead of leveraging it, this is the article that fixes the gap. 👇 Full breakdown below.

Stop using REST like it’s the only option. For years, REST APIs have been the standard: Structured endpoints JSON responses Predictable patterns But as applications scale, REST often leads to: Over-fetching or under-fetching data Complex endpoint management Slower iteration Modern approaches are changing this: GraphQL → flexible, efficient data fetching Server Actions in Next.js → simpler backend interaction without traditional APIs The shift isn’t about abandoning REST completely. It’s about choosing the right tool for the right problem. Simpler architecture. Smarter data flow. What are you using today — REST or something else? #WebDevelopment #SoftwareEngineering #API #GraphQL #NextJS #FullStack #Programming #TechTrends #DeveloperLife

gRPC (Google Remote Procedure Call) is a modern framework designed for fast, efficient communication between services — especially in distributed systems and microservices architectures. Why gRPC 1️⃣ Ultra-fast performance (binary vs JSON) 2️⃣ Smaller payloads = better efficiency 3️⃣ Built-in bi-directional streaming 4️⃣ Strongly typed contracts with Protobuf 5️⃣ Powered by HTTP/2 6️⃣ Works across multiple languages (Java, Go, Python, etc.) How it works 1. Define services in `.proto` files 2. Generate client & server code automatically 3. Call methods like local functions 4. Get fast, structured responses Perfect for: ✔ Microservices communication ✔ Real-time applications ✔ High-performance systems ✔ Internal APIs #gRPC #Microservices #Backend #API #SoftwareEngineering #DistributedSystems #Tech