Otavio Santana’s Post

Many resources focus on basics, but enterprise challenges are different—data modeling, integration with frameworks, and aligning design with NoSQL principles are often misunderstood. Read more 👉 https://lttr.ai/AqUsw #mongodb #java #career

💡 Small backend improvement → Big performance impact While working on backend services, one thing that consistently helps improve performance is caching. Instead of hitting the database for every request: → We cache frequently accessed data (using Redis) Benefits: • Reduced DB load • Faster response time • Better system scalability Simple change, but huge impact in high-traffic systems 🚀 Tech: Java | Spring Boot | Redis #Backend #Java #Microservices #SystemDesign

Something shifted in the Java ecosystem over the last 12 months and most developers have not fully processed it yet. The reactive programming debate is largely over. Not because WebFlux lost. But because virtual threads made the argument irrelevant for most use cases. For 12 years I watched teams wrestle with the reactive programming decision. WebFlux gave you non-blocking throughput but the learning curve was steep, stack traces were painful to read, and onboarding new developers onto a reactive codebase added real friction. Most teams chose it because they felt they had to, not because they wanted to. Virtual threads changed the calculus. One property in your application.yml and your Spring Boot service handles I/O-bound concurrency at WebFlux scale while your team keeps writing the blocking imperative code they already understand. Simpler code. Easier debugging. Fewer ThreadLocal memory leaks. Better tail latencies. Java 26 just dropped. Spring AI with MCP is moving fast. Agentic architectures are making their way into production Java systems. Records and sealed classes have stopped feeling new and started feeling normal. The Java ecosystem in 2026 is genuinely exciting in a way it has not been for a long time. Not because it is chasing trends. Because it is solving real production problems that teams have been working around for years. The developers who are thriving right now are the ones who kept building and kept shipping while everyone else debated whether Java was still relevant. It was. It is. The platform just caught up to where the problems actually are. What is the one Java or Spring Boot change in the last 12 months that has had the most impact on how you build systems? #Java #JavaDeveloper #CoreJava #Java21 #Java26 #SpringBoot #SpringAI #Microservices #VirtualThreads #ProjectLoom #BackendDevelopment #CloudNative #DevOps #Docker #Kubernetes #RESTAPI #Kafka #PostgreSQL #Oracle #MongoDB #Redis #MCP #AgenticAI #LLM #C2C #CorpToCorp #ContractJobs #ContractToHire #ITContracting #ITRecruiter #TechRecruiter #Hiring #Recruitment #TechJobs #ITJobs #TalentAcquisition #Careers #JobSearch #RemoteWork #usjobs #opentowork #DeveloperLife #SoftwareEngineering #ITStaffing #HiringDevelopers #ConsultingJobs #RemoteContractor #FreelanceDeveloper #ContractEngineer Amazon Web Services (AWS) Beacon Hill CVS Health Dexian Insight Global TEKsystems eTeam

💡 How we improved API performance in a high-traffic system Recently, I worked on optimizing backend APIs in a microservices setup. Some key improvements that helped: Added Redis caching → reduced DB load significantly Optimized SQL queries → faster response time Introduced async processing for heavy operations Result: Better performance and improved system reliability under load 🚀 Tech: Java | Spring Boot | Kafka | Redis #Backend #SystemDesign #Java #Microservices

Several years of Java full stack engineering. Now building cloud-native systems on Microsoft Azure. 𝗔𝘇𝘂𝗿𝗲 𝘀𝘁𝗮𝗰𝗸 (𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝘃𝗲𝗿𝗶𝗳𝗶𝗲𝗱): → Azure Kubernetes Service (AKS) for container orchestration and auto-scaling → Azure API Management as the secure gateway layer with OAuth2 + JWT → Azure Service Bus for async event-driven microservice communication → Cosmos DB for globally distributed, low-latency NoSQL data at scale → Azure SQL + Azure Cache for Redis for structured data and high-speed caching → Azure Entra ID (formerly AAD) for enterprise authentication, SSO, and RBAC → Azure DevOps Pipelines for end-to-end CI/CD — build, test, containerize, deploy → Azure Monitor + Application Insights for observability, distributed tracing, and alerting → Azure Blob Storage + CDN for static assets and document management → Terraform + Bicep for Infrastructure as Code across all environments 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗮𝗻𝗱 𝗳𝘂𝗹𝗹 𝘀𝘁𝗮𝗰𝗸 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻: → Java 21 with virtual threads · Spring Boot 3.x · Spring Security 6 → React 18 · Angular 17 · TypeScript for frontend delivery → PostgreSQL · MongoDB · Redis · Kafka for data and messaging → JUnit 5 · Testcontainers · WireMock for test coverage that actually holds in prod I do not just deliver features-I own reliability, scalability, and team growth. I write architecture decision records before writing a single line of code. I instrument every service with Application Insights traces and custom metrics before it ships to prod. I treat security, observability, and documentation as engineering requirements not tickets to create later. #OpenToWork #Azure #JavaDeveloper #SpringBoot #AzureKubernetesService #FullStackDeveloper #C2C

⚠️ Java was fast… until it wasn’t. Everything looked normal. No deployment changes. No traffic spike. But suddenly… 👉 API latency doubled 👉 Kafka consumers slowed down 👉 System felt… heavy No errors. No crashes. Just silent degradation. 🔍 Where do you even start? Logs? Clean. Database? Healthy. CPU? Not maxed out. But something was off. ⚡ The real culprit? JVM Garbage Collection. A subtle GC misconfiguration was causing: Frequent minor GCs Occasional long pauses Thread blocking under load 👉 The system wasn’t failing… It was pausing. 🔧 What we fixed: ✔ Tuned heap size & GC parameters ✔ Switched to optimized GC strategy ✔ Reduced object creation in hot paths 🚀 Result: Latency dropped back to normal Kafka lag disappeared System became stable again 💡 Lesson: You don’t always fix Java performance in code. Sometimes, 👉 It’s JVM tuning 👉 Memory behavior 👉 Garbage collection patterns 🔥 Real backend engineering is about: 👉 Understanding what happens inside the JVM 👉 Not just writing APIs 👉 Designing systems that survive under pressure 💬 Have you ever debugged a JVM issue that wasn’t obvious? #Java #JVM #SpringBoot #Performance #Microservices #Kafka #BackendEngineering #SoftwareEngineering #TechCareers #JavaDeveloper #OpenToWork

One thing I’ve realized recently: Backend engineering is not about knowing tools like Kafka or Redis. It’s about fundamentals. Most experienced engineers I spoke to emphasized: - Core Java (collections, multithreading) - How Spring actually works internally - Database understanding Frameworks make things easy. Fundamentals make things scalable. Right now, I’m focusing on: → Writing better logic → Understanding request flow → Thinking beyond just "working code" Curious — what fundamental concept helped you the most early in your career?

🚀 Exploring Serverless Java ⚙️ Lately, I’ve been diving into the world of Serverless architecture with Java, and it’s fascinating how development is evolving. Traditionally, we focused a lot on managing servers, scaling infrastructure, and handling deployments. But with serverless computing, that responsibility shifts—allowing developers to focus purely on writing business logic. 🔹 What is Serverless Java? It’s about running Java applications without managing servers, using platforms like AWS Lambda and Azure Functions. 🔹 Why it’s trending: ✔️ No server management ✔️ Auto-scaling based on demand ✔️ Pay only for what you use ✔️ Faster time to market 🔹 Where it fits: Serverless works great for event-driven systems, APIs, background jobs, and microservices. 🔹 Key learning: While Java is traditionally seen as heavy, modern improvements (like faster startup times and optimizations) are making it more efficient in serverless environments. 💡 As a Java developer, adapting to cloud-native and serverless approaches is becoming essential. Excited to explore more in this space and understand how it can improve scalability and efficiency in real-world applications. #Java #Serverless #CloudComputing #AWS #Azure #Microservices #BackendDevelopment #Learning #TechTrends #Angular #TypeScript #Azure #FrontendDevelopment #Agile #SpringBoot #Servers

Java 26 didn’t arrive with hype. It arrived with something better: changes that matter in real systems. For teams building microservices, healthcare platforms, banking workflows, or high-traffic APIs, the most valuable upgrades are often the ones users never notice directly, but engineers definitely do. This release is one of those moments. What stood out to me in Java 26: Native HTTP/3 support in the built-in HttpClient, which is a real win for low- latency systems. • Performance and GC improvements that matter when you’re running heavy enterprise workloads. • A cleaner, stricter runtime direction that keeps the platform more reliable and modern over time. • A signal that Java is still evolving for cloud-native and high-scale backend development, not just staying static. As someone who has worked across Java, Spring Boot, microservices, REST APIs, cloud platforms, and performance-sensitive enterprise systems, I see this kind of release as a reminder that Java is still one of the most practical choices for production engineering. The best technology isn’t always the loudest. Sometimes it’s the one that helps systems run faster, safer, and at scale. #OpenToWork #Hiring #NowHiring #JobSearch #JavaDeveloper #FullStackDeveloper #SpringBoot #Microservices #RESTAPI #BackendDeveloper #SoftwareEngineer #CloudComputing #AWS #Azure #GCP #Docker #Kubernetes #Kafka #CICD #DevOps #Angular #NodeJS #ExpressJS #Hibernate #JPA #Oracle #MySQL #PostgreSQL #MongoDB #NoSQL #EnterpriseApplications #TechJobs #DeveloperJobs #SoftwareDevelopment #ITJobs #LinkedInPost #CareerGrowth #OpenToWork2026 #AvailableForWork #NoSQL #EnterpriseApplications #TechJobs #DeveloperJobs #SoftwareDevelopment #ITJobs #LinkedInPost #CareerGrowth #OpenToWork2026 #AvailableForWork

Want to be a great backend engineer? Learn: 1. Programming Fundamentals Language depth, data structures, algorithms, concurrency, memory basics 2. APIs and Contracts REST, gRPC basics, versioning, idempotency, pagination, error handling 3. Databases SQL, joins, indexes, transactions, query optimization, schema design 4. Caching Redis, cache invalidation, TTLs, read-through vs write-through, hot key problems 5. Async and Messaging Queues, Kafka/RabbitMQ, retries, DLQs, at-least-once delivery, idempotent consumers 6. Distributed Systems Basics Replication, partitioning, consistency, leader-follower, network failures, backpressure 7. Reliability Engineering Timeouts, retries, circuit breakers, rate limiting, graceful degradation 8. Observability Logs, metrics, tracing, p95/p99 latency, debugging production issues 9. Infrastructure and Deployment Docker, CI/CD, Linux basics, cloud services, rollbacks, blue-green/canary deploys 10. Security Auth, authorization, secrets management, input validation, encryption, secure defaults Preparing for interviews? Start revising these today 𝗜’𝘃𝗲 𝗽𝗿𝗲𝗽𝗮𝗿𝗲𝗱 𝗶𝗻 𝗗𝗲𝗽𝘁𝗵 𝗝𝗮𝘃𝗮 𝗦𝗽𝗿𝗶𝗻𝗴𝗯𝗼𝗼𝘁 𝗯𝗮𝗰𝗸𝗲𝗻𝗱 𝗚𝘂𝗶𝗱𝗲, 𝟏𝟬𝟬𝟬+ 𝗽𝗲𝗼𝗽𝗹𝗲 𝗮𝗿𝗲 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝘂𝘀𝗶𝗻𝗴 𝗶𝘁. 𝗚𝗲𝘁 𝘁𝗵𝗲 𝗴𝘂𝗶𝗱𝗲 𝗵𝗲𝗿𝗲: https://lnkd.in/dfhsJKMj keep learning, keep sharing ! #java #backend #javaresources