Otavio Santana’s Post

🚀 Result Management System | Java + MongoDB Here’s a quick walkthrough of my **Result Management System** project built using **Java** and **MongoDB**. The system is designed to efficiently manage student records, calculate results, and provide structured data storage with a scalable NoSQL database. 🔹 Add & manage student details 🔹 Store results using MongoDB 🔹 Automated result calculation 🔹 Simple and user-friendly workflow This project helped me understand database integration, backend logic, and real-world academic system implementation. Would love your feedback and suggestions! #Java #MongoDB #ResultManagementSystem #BackendDevelopment #StudentProject #Database #Programming #LinkedInProjects

My favorite content is the one that helps senior and staff engineers design scalable systems using MongoDB within real-world architectures. Read more 👉 https://lttr.ai/Ap1Rm #mongodb #java #career

Running Scala and Java Spark jobs often requires managing clusters and handling runtime upgrades. Serverless JARs remove that overhead. Jobs start in seconds and run on the latest supported runtime without cluster management. With Databricks Connect, you can build and test from your IDE against real data and production-like environments, then deploy with Databricks Asset Bundles. You only pay for the compute you use. https://lnkd.in/g-4maxVp

Big update for Scala and Java Spark developers! 🙌 Serverless JARs are finally in public preview! Thanks you Edward Feng, Imran Hasan, Garland Zhang, Shiyu Wang, Herman van Hövell tot Westerflier, Zhong Chen, Jakob Mund, Niranjan J., Haiyang Sun, Vsevolod Stepanov, Xianzhe Ma, Martin Grund, Stefania Leone, Jakob Mund, Lionel Montrieux, Othon Crelier and all others for making it happen!

Reflecting on the evolution of backend engineering, it's evident that the right technology stack can significantly enhance system reliability and speed. Over the past few years, I have explored remarkable technologies while developing high-throughput distributed systems. Here are the core technologies I currently leverage to build scalable, production-grade architectures: 🏗️ Distributed Microservices & Messaging Building services that handle over 100,000 daily requests requires a resilient communication layer. - Java (Spring Boot) & Python (FastAPI/Flask): My preferred choices for creating modular, high-performance services. - Apache Kafka & RabbitMQ: Crucial for event-driven architectures, I recently observed a reduction in message delays from 8 minutes to 90 seconds using Kafka. - gRPC & REST: Facilitating seamless service-to-service communication. ⚡ Performance & Data Persistence Efficiency lies in the details of the database and caching layers. - PostgreSQL & MySQL: Optimizing complex queries to decrease execution time from seconds to milliseconds. - Redis: My top choice for caching, significantly cutting latency and reducing repeated database reads by tens of thousands per day. ☁️ Cloud & Reliability Scalability is only as effective as the infrastructure that supports it. - AWS (EC2, S3, Lambda, RDS): Utilizing cloud-native tools for global deployment and scaling. - Kubernetes & Docker: Standardizing environments and automating container orchestration. - Prometheus & ELK Stack: Implementing real-time monitoring to establish circuit breakers and prevent hours of potential downtime. As technology continues to evolve, the objective remains consistent: to build systems that are both reliable and fast. #SoftwareEngineering #BackendDeveloper #Java #Python #Microservices #CloudComputing #Kafka #SystemDesign #TechStack #DellTechnologies

🚀 Solving a Hidden Tech Debt Problem in MongoDB-backed Microservices If you’ve worked with MongoDB aggregation pipelines in microservices, you’ve probably seen this pattern: complex, multi-stage queries hardcoded as raw strings inside Java code. It works… until it becomes painful to maintain. Here’s what we started running into: ❌ Pipeline stages built by manually concatenating strings with dynamic values ❌ Repeated boilerplate across multiple services ❌ Fragile string-based injection (special characters breaking queries silently) ❌ No clear visibility into what queries were actually running ❌ Onboarding pain — new developers had to trace Java code just to understand the database logic So we made a small shift. We built a lightweight utility to externalize MongoDB aggregation pipelines into versioned JSON files (one per module), with support for typed runtime parameters using a simple {{placeholder}} syntax. Here’s what improved: ✅ Pipelines became data, not code — stored as JSON, easy to read and reason about ✅ Type-safe parameter injection — integers stay integers, lists stay lists (no manual escaping) ✅ Auto-discovery at startup — drop a new JSON file in the right place and it’s picked up automatically ✅ Cleaner DAO layer — just call getPipeline("query_key", params) and execute ✅ Better code reviews — query changes show up as clean JSON diffs, not escaped Java strings The biggest win? The people who understand the business logic can now review and reason about queries directly — without digging through Java code. Sometimes small architectural changes remove a surprising amount of friction. This one took a few hours to build and is already paying off in maintainability and developer productivity. Curious — how are you managing complex database queries in your services? #Java #SpringBoot #MongoDB #SoftwareEngineering #Microservices #BackendArchitecture #CleanCode #TechDebt #DeveloperProductivity

🚀 Day 23 – Logging Best Practices in Java: Build Systems You Can Debug at 3 AM Logging is your system’s black box recorder. Done well → it accelerates debugging, improves reliability, and strengthens observability. Done poorly → it becomes noise, slows performance, and hides root causes. Here are the best practices every architect should enforce: 🔹 1. Use the Right Log Levels ERROR → Something failed, needs attention WARN → Suspicious but system continues INFO → High-level business events DEBUG → Internal flow details TRACE → Too detailed, use sparingly ➡ Proper level usage prevents both noisy logs and missing insights. 🔹 2. Never Log Sensitive Data Avoid logging: ❌ Passwords ❌ Tokens ❌ Personal data ❌ Payment details ➡ Reduces compliance risk (PCI-DSS, GDPR) and enhances security. 🔹 3. Use Structured Logging Prefer JSON logs: {"event":"orderCreated","orderId":123,"amount":4500} ➡ Machine-readable logs enhance monitoring, searchability, and analytics. 🔹 4. Always Log With Context Include identifiers: ✔ userId ✔ orderId ✔ correlationId ✔ requestId ➡ Makes debugging distributed systems dramatically easier. 🔹 5. Avoid Log Spam No unnecessary logs like: ❌ "Entering method…" ❌ "Processing…" ❌ Repetitive debug statements ➡ Less noise → faster troubleshooting. 🔹 6. Use Parameterized Logging log.info("Order created: {}", orderId); ➡ Avoids string concatenation overhead. 🔹 7. Log Exceptions Properly Never do this: ❌ log.error(e.getMessage()); Always do this: ✔ log.error("Payment failed", e); ➡ Ensures stack traces are preserved. 🔹 8. Centralize Logs for Observability Use: ✔ ELK/EFK ✔ Splunk ✔ Datadog ✔ CloudWatch / Azure Log Analytics ➡ Unified logs = faster RCA & better system insights. 🔹 9. Correlate Logs Across Microservices Every request should carry a correlation ID all the way through. ➡ Helps visualize entire request flow. 🔥 Architect’s Takeaway Good logging is not optional — it’s a core part of system design. A well-logged system is: ✔ Easier to debug ✔ Safer ✔ More reliable ✔ More observable ✔ More performant #100DaysOfJavaArchitecture #Logging #Java #Microservices #SystemDesign #Observability #TechLeadership

These fields showcase the flexibility of MongoDB’s schema design, allowing us to group related information and co-locate it efficiently. Read more 👉 https://lttr.ai/ApxSZ #Java #MongoDB

I worked on improving the performance of one of our Java microservices, and I’m excited to share a quick win We were seeing API response times averaging around 800ms, which wasn’t ideal for user experience or system efficiency. So I took a step back and focused on a few key areas: Optimized database queries by improving indexing and reducing unnecessary calls Introduced a more effective caching strategy to avoid repeated data fetches Implemented asynchronous processing to better utilize system resources The result ? We brought response time down to 600ms — a 25% improvement. This experience reinforced something simple but powerful: small, targeted optimizations can make a big impact when you approach problems systematically. Always learning, always iterating #Java #Microservices #BackendDevelopment #SoftwareEngineering #PerformanceOptimization #SystemDesign #Scalability #APIs #TechCareer #BuildInPublic