Vadym Kazulkin 🇺🇦’s Post

New blog post alert 🚨 "AWS Lambda Managed Instances with Java 25 and AWS SAM – Part 6 Lambda function performance improvement approaches". In this article, we’ll optimize our Lambda function to improve the cold start time significantly. Amazon Web Services (AWS) Oracle #Java #Serverless #AWSLambda #performance https://lnkd.in/ehASnK6m

New blog post alert 🚨 "Serverless applications on AWS with Lambda using Java 25, API Gateway and DynamoDB – Part 1 Sample application". In this article series, we’ll explain how to implement a serverless application on AWS using Lambda with the support of the released Java 25 version. If you like my content, please follow me on GitHub (github.com/Vadym79) and give my repositories like this https://lnkd.in/epud2eRf a star! Maximilian Schellhorn Andrei Shakirin Lefteris Karageorgiou Matt Meckes Yuriy Bezsonov Raluca Constantin Philipp Page James Ward Amazon Web Services (AWS) Oracle #Java #Serverless https://lnkd.in/eAwyKCQq

New blog post alert 🚨 "Serverless applications on AWS with Lambda using Java 25, API Gateway and Aurora DSQL – Part 1 Sample applications". In this article series, we’ll explain how to implement a serverless application on AWS using Lambda with the support of the released Java 25 version. One application uses JDBC and another Hibernate, both with Aurora DSQL. If you like my content, please follow me on GitHub (github.com/Vadym79) and give my repositories like this https://lnkd.in/epud2eRf a star! Maximilian Schellhorn Andrei Shakirin Lefteris Karageorgiou Matt Meckes Yuriy Bezsonov Raluca Constantin Philipp Page James Ward Amazon Web Services (AWS) Oracle #Java #Serverless #AuroraDSQL #PostgreSQL https://lnkd.in/eMSzrJGy

New blog post alert 🚨 "Serverless applications on AWS using Lambda with Java 25, API Gateway and Aurora DSQL – Part 2 Initial performance measurements". In this article, we’ll perform Lambda function initial performance measurements of the sample application using Lambda with Java 25 API Gateway and Aurora DSQL without any optimizations. If you like my content, please follow me on GitHub (github.com/Vadym79) and give my repositories like this https://lnkd.in/epud2eRf a star! Amazon Web Services (AWS) Oracle #Java #Serverless #PostgreSQL https://lnkd.in/grbkixBZ

Spring Framework Learning Roadmap I’ve been strengthening my knowledge in the Spring Ecosystem, which is one of the most powerful frameworks for building Java-based enterprise applications. 📌 Key areas I am focusing on: 🔹 Spring Core & Spring MVC – Dependency Injection, Annotations, Scheduling 🔹 Spring Boot – Auto-configuration & production-ready applications 🔹 Spring Data & Hibernate – JPA, JDBC, Transactions, Entity Lifecycle 🔹 Databases – MySQL, PostgreSQL, MongoDB (Queries, Joins, Indexing, Locking) 🔹 Spring Security – OAuth2, Form Authentication, Basic Auth, JWT 🔹 Testing – JPA Test, MockMVC, Service Testing 🔹 Microservices Architecture – Docker, Spring Cloud, API Gateway, Eureka 🔹 Message Queues – Kafka, RabbitMQ, AWS SQS This roadmap helps in understanding how different components of the Spring ecosystem work together to build scalable, secure, and production-ready applications. Always learning and improving! 💻 #Java #SpringBoot #SpringFramework #Microservices #BackendDevelopment #Hibernate #SpringSecurity #Kafka #SoftwareDevelopment

🚀 Day 23 – Java Backend Journey | Caching getUserById API Today I practiced applying Redis caching to a real API, specifically the getUserById endpoint, to improve performance and reduce database load. 🔹 What I practiced today I implemented caching for the GET API: GET /users/{id} This API fetches a user by ID from the database. 🔹 Caching Implementation Used Spring Boot caching with @Cacheable: 🔹 How it works • First request → Cache miss → Data fetched from DB → Stored in Redis • Next requests → Cache hit → Data returned from cache (no DB call) 🔹 What I observed ✔ Reduced database queries ✔ Faster response time ✔ Improved performance for repeated requests 🔹 Why this is important Caching frequently accessed APIs like getUserById is a common optimization technique used in real-world backend systems. It helps in: • Scaling applications • Handling high traffic • Improving user experience 🔹 Key takeaway Adding caching to APIs is a simple but powerful way to make backend systems efficient and high-performing, especially for read-heavy operations. 📌 Next step: Implement cache eviction using @CacheEvict to handle data updates. #Java #SpringBoot #Redis #Caching #BackendDevelopment #SoftwareEngineering #LearningInPublic #JavaDeveloper #100DaysOfCode

New blog post alert 🚨 "Serverless applications on AWS using Lambda with Java 25, API Gateway and DynamoDB – Part 2 Initial performance measurements". In this article, we’ll perform Lambda function initial performance measurements of the sample application using Lambda with Java 25 API Gateway and DynamoDB without any optimizations. If you like my content, please follow me on GitHub (github.com/Vadym79) and give my repositories like this https://lnkd.in/epud2eRf a star! Amazon Web Services (AWS) Oracle #Java #Serverless https://lnkd.in/ejJYXn_4

Added more functional examples to my aws-lambda-java-25-spring-boot-4 GitHub repo 👉 https://lnkd.in/eKafFg7p where I explore how to run Spring Boot 4 applications with #Java 25 on #AWS Lambda. 1) AWS Lambda with Java 25, Spring Boot 4, AWS Serverless Java Container, Spring JDBC (instead of Hibernate), Hikari and Aurora DSQL 👉 https://lnkd.in/e7T8S5tG 2) AWS Lambda as a Custom Runtime containing GraalVM Native Image built with 25.0.2 version, AWS #Serverless Java Container, Spring JDBC (instead of Hibernate), Hikari and Aurora DSQL 👉 https://lnkd.in/ePQRJaxu If you like my content, please follow me on GitHub (github.com/Vadym79) and give my repositories a star! Please also check out my website vkazulkin.com for more technical content and upcoming public speaking activities. Maximilian Schellhorn Andrei Shakirin Lefteris Karageorgiou Matt Meckes Yuriy Bezsonov Raluca Constantin Philipp Page James Ward Amazon Web Services (AWS) Oracle #Hibernate #PostgreSQL #AuroraDSQL #Spring #SpringBoot

Check out the example of AWS Lambda with Custom Runtime based on GraalVM Native Image (version 25) using Spring Boot 4 with AWS Serverless Java Container and Amazon DynamoDB database 👉 https://lnkd.in/ex5wJJMQ If you like my content, please follow me on GitHub (github.com/Vadym79) and give my repositories a star! Please also check out my website 👉 vkazulkin.com for more technical content and upcoming public speaking activities. Amazon Web Services (AWS) GraalVM #Java #Serverless #Spring #SpringBoot

🚀 Day 21 – Java Backend Journey | Redis Basics Today I focused on learning the basics of Redis, a powerful in-memory data store widely used for caching and high-performance applications. 🔹 What I practiced today ✔ Understanding Redis Redis is an in-memory key-value database that stores data in RAM, making it extremely fast compared to traditional databases. ✔ Basic Redis Commands I explored some commonly used Redis commands: Set value SET user:1 "Akash" Get value GET user:1 Delete key DEL user:1 Check all keys KEYS * ✔ Working with Data Structures Redis supports multiple data types: • Strings → Simple key-value pairs • Lists → Ordered collection • Sets → Unique values • Hashes → Key-value pairs inside a key Example: HSET user:1 name "Akash" email "akash@gmail.com" HGETALL user:1 🔹 What I learned • Redis works as a NoSQL database • Data is stored in memory for ultra-fast access • Supports multiple data structures • Used in caching, session management, and real-time systems 🔹 Why Redis is important ✔ High performance (low latency) ✔ Reduces database load ✔ Supports scalable backend systems ✔ Widely used in real-world applications 🔹 Key takeaway Redis is not just a cache—it’s a powerful in-memory data store that plays a critical role in building fast and scalable backend systems. 📌 Next step: Implement Redis caching in real APIs and explore advanced features. #Java #SpringBoot #Redis #BackendDevelopment #Caching #SoftwareEngineering #LearningInPublic #JavaDeveloper #100DaysOfCode